eess.SP — Pith

0

eess.SP 2026-05-13 Recognition

Commercial 5G dataset aids AI handover and beam management

Enabling AI-Native Mobility in 6G: A Real-World Dataset for Handover, Beam Management, and Timing Advance

It records timing advance at signaling events across pedestrian to train speeds to train models that cut interruption times.

abstract click to expand

To address the issues of high interruption time and measurement report overhead under user equipment (UE) mobility especially in high speed 5G use cases the use of AI/ML techniques (AI/ML beam management and mobility procedures) have been proposed. These techniques rely heavily on data that are most often simulated for various scenarios and do not accurately reflect real deployment behavior or user traffic patterns. Therefore, there is an utmost need for realistic datasets under various conditions. This work presents a dataset collected from a commercially deployed network across various modes of mobility (pedestrian, bike, car, bus, and train) and at multiple speeds to depict real time UE mobility. When collecting the dataset, we focused primarily on handover (HO) scenarios, with the aim of reducing the HO interruption time and maintaining continuous throughput during and immediately after HO execution. To support this research, the dataset includes timing advance (TA) measurements at various signaling events such as RACH trigger, MAC CE, and PDCCH grant which are typically missing in existing works. We cover a detailed description of the creation of the dataset; experimental setup, data acquisition, and extraction. We also cover an exploratory analysis of the data, with a primary focus on mobility, beam management, and TA. We discuss multiple use cases in which the proposed dataset can facilitate understanding of the inference of the AI/ML model. One such use case is to train and evaluate various AI/ML models for TA prediction.

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eess.SP 2026-05-13 Recognition

Spiking nets cut massive MIMO CSI feedback energy by 93%

Massive MIMO CSI Feedback with Spiking Neural Networks

Bio-inspired spiking networks match transformer accuracy on channel reconstruction while using far less power

abstract click to expand

Deep learning-based channel state information (CSI) feedback has achieved empirical success in massive multiple-input multiple-output (MIMO) systems. However, existing approaches largely rely on dense artificial neural networks (ANNs), whose computational overhead limits their practical applications. In this article, we exploit bio-inspired spiking neural networks (SNNs) for massive MIMO CSI feedback, referred to as SpikingCSINet, where both the feedback and the main network computations are implemented through spikes. To overcome the information bottleneck of binary spikes in high-dimensional reconstruction, we develop a progressive residual (PR) architecture that exploits the natural temporal dimension of SNNs, encoding successive residuals across time steps to enhance information compactness. Experiments on the COST 2100 benchmark show that SpikingCSINet attains a more favorable performance-efficiency tradeoff than lightweight convolutional baselines. Moreover, it achieves performance competitive with Transformer-based feedback while reducing energy consumption by over $93\%$.

0

eess.SP 2026-05-13 2 theorems

Artifact rejection boosts low-SNR MI-BCI accuracy most

From EEG Cleaning to Decoding: The Role of Artifact Rejection in MI-based BCIs

FAAR reduces user-to-user performance gaps on 13 datasets by rejecting bad epochs adaptively with no manual tuning or heavy data loss.

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Motor imagery (MI) BCIs are sensitive to EEG artifacts, yet the practical impact of automated artifact rejection on downstream MI decoding performance remains unclear. While most work focuses on decoder design, the contribution of data curation, particularly automated rejection policies, has received comparatively less attention, despite its importance for robust ML pipelines. Here, we propose Fast Automatic Artifact Rejection (FAAR), a lightweight method that computes a compact set of artifact-sensitive features, derives an epoch-level Signal Quality Index, adaptively selects rejection thresholds, and automatically rejects contaminated epochs without requiring prior knowledge of artifact types or manual threshold tuning. We evaluate FAAR on 13 publicly available MI datasets and compare it to a no-rejection baseline, AutoReject, and Isolation Forest. We show rejection effects are strongly subject- and regime-dependent, with the largest gains in low-baseline/low-SNR conditions, so it should be used adaptively. FAAR reduces inter-subject performance variability, an important property for MI-BCI reliability and BCI-illiteracy, without aggressive data removal. Finally, FAAR's lightweight and fully automated thresholding yields consistent rejection behavior across offline curation, training, and online filtering, and supports real-time BCI constraints.

0

eess.SP 2026-05-13 Recognition

State space models beat transformers for ECG foundation models

Pretraining Strategies and Scaling for ECG Foundation Models: A Systematic Study

Systematic comparison up to 11M samples shows inductive biases, not scale, drive better transferable representations.

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Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the size of the pretraining dataset are rarely assessed systematically and in a like-for-like manner. This work focuses on foundation models for electrocardiography (ECG) data, one of the most widely captured physiological time series world-wide. We present a comprehensive assessment of pretraining methodologies, covering five different contrastive and non-contrastive self-supervised learning objectives for ECG foundation models, and investigate their scaling behavior with pretraining dataset sizes up to 11M input samples, exclusively from publicly available sources. Pretraining strategy has a meaningful and consistent impact on downstream performance, with contrastive predictive coding (slightly ahead of JEPA) yielding the most transferable representations across diverse clinical tasks. Scaling pretraining data continues to yield meaningful improvements up to 11M samples for most objectives. We also compare model architectures across all pretraining methodologies and find evidence for a clear superiority of structured state space models compared to transformers and CNN models. We hypothesize that the strong inductive biases of structured state space models, rather than pretraining scale alone, are the primary driver of effective ECG representation learning, with important implications for future foundation model development in this and potentially other physiological signal domains.

0

eess.SP 2026-05-13 Recognition

Block filtering speeds long-context prefilling

BFLA: Block-Filtered Long-Context Attention Mechanism

BFLA skips unimportant KV blocks with coarse masks and tile rescues while keeping accuracy close to full attention

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This paper proposes Block-Filtered Long-Context Attention (BFLA), a training-free sparse prefill attention mechanism for long-context inference. BFLA adopts a two-stage design. In Stage 1, query and key sequences are compressed into coarse blocks, and lightweight block-level softmax mass estimation is performed to construct an input-dependent block importance mask. In Stage 2, the coarse mask is expanded to the Triton attention-tile grid. Several tile-level rescue strategies are applied to reduce information loss, where a fused sparse prefill kernel skips unimportant KV tiles while preserving exact token-level attention inside every retained tile. BFLA requires no retraining, calibration, preprocessing, or model modification and can be plugged into existing vLLM-style paged-attention workloads. Experiments on Gemma 4, Llama 3.1, Qwen 3.5, and Qwen 3.6 series models show that BFLA substantially accelerates long-context prefilling with minimal accuracy degradation compared to dense Triton FlashAttention. Project website: https://github.com/Alicewithrabbit/BFLA.

0

eess.SP 2026-05-13 2 theorems

Cell statistics alone set movable-antenna positions

Slow Movable Antenna System Design Based on Cell-Specific Long-Term Angular Power Spectrum

CEBAP uses only long-term APS to cut overhead and raise long-term rates and SINR without short-term CSI.

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Movable antenna (MA) has recently emerged as a promising paradigm for enhancing wireless communication performance by exploiting spatial degrees of freedom through flexible antenna repositioning. However, most existing designs rely on short-term user-specific instantaneous/statistical channel state information (CSI), which incurs excessive channel estimation overhead and complexity due to frequent antenna movement. To address this issue, this paper proposes a new design framework for antenna position optimization over a much longer timescale based on the cell-level statistical channel information acquired at the base station (BS). To this end, a cell-specific statistical channel model is developed for MA-aided multiuser communication systems, based on which the antenna position optimization framework for maximizing the ergodic system utility is formulated. Then, the covariance-eigenvalues-balancing antenna positions (CEBAP) design is derived to asymptotically approximate optimal solutions by statistically reducing users' channel correlation. Notably, the CEBAP solution solely depends on the BS-side angular power spectrum (APS) of wireless channels for mobile users across the cell, which significantly alleviates the overhead of channel acquisition and antenna movement, and yet remains effective for improving various system utilities over long timescales, such as weighted sum rate and minimum signal-to-interference-plus-noise ratio. Moreover, a low-complexity log-barrier penalized optimization (LOBPO) method is proposed to numerically solve the CEBAP. Simulation results based on realistic urban layouts and ray-tracing channels demonstrate consistent performance gains of the proposed CEBAP over fixed-position antenna systems across different utility functions, which closely approaches the upper bound achieved by instantaneous CSI-based MA optimization for moderately large antenna regions.

0

eess.SP 2026-05-13 2 theorems

Beamspace sorting yields SNR from one mmWave sample

Low-Complexity Blind SNR Estimator for mmWave Multi-Antenna Communications

A sorting procedure isolates noise components via order statistics so that noise power, signal power, and SNR follow from arithmetic alone.

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In this paper, we propose a low-complexity blind estimator for the average noise power, average signal power, and signal-to-noise ratio (SNR) in millimeter-wave (mmWave) massive multi-antenna uplink systems. In particular, the proposed method is designed to operate using only a single received signal sample, without relying on pilot signals, iterative optimization, or multiple observations, and without requiring prior knowledge of the transmitted signal. By exploiting the inherent sparsity of mmWave channels in the beamspace domain, the estimator identifies noise-dominant components through a sorting-based procedure combined with a finite-difference criterion. This separation is further supported by the order statistics of noise power under Gaussian assumptions, enabling statistically grounded discrimination between signal and noise elements. The average noise power is estimated from the identified noise-only components, and the signal power and SNR are subsequently obtained through simple arithmetic operations. The proposed algorithm achieves low computational complexity and is well-suited for real-time implementation. To demonstrate its practical feasibility, a hardware-efficient very large-scale integration (VLSI) architecture is developed and implemented on a AMD-Xilinx Kintex UltraScale+ KCU116 Evaluation Kit, with corresponding field-programmable gate array (FPGA) results provided. The implementation exhibits low latency and sublinear scaling of hardware resource utilization with respect to the number of antennas, and enables parameter estimation within a duration shorter than a single symbol of conventional wireless systems. Simulation results verify that the proposed estimator achieves high estimation accuracy compared to existing single-sample-based methods.

0

eess.SP 2026-05-13 2 theorems

Adaptive power control keeps RSMA feasible in MIMO under imperfect CSI

Adaptive RSMA-OMA for Resilient MIMO Networks Under Imperfect CSI and SIC

By reallocating power and switching to OMA when needed, the method reduces outages and improves efficiency despite channel errors and cancel

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This paper addresses the challenge of power control in Rate-Splitting Multiple Access (RSMA) systems for downlink Multi-Input Multi-Output (MIMO) networks under practical impairments such as spatial correlation, imperfect Channel State Information (CSI), and residual Successive Interference Cancellation (SIC) errors. We propose a novel degeneracyaware framework that adaptively adjusts the power allocation between the common and private streams, ensuring optimal performance despite CSI uncertainty and imperfect SIC. Our approach incorporates a dynamic switching mechanism between RSMA and Orthogonal Multiple Access (OMA) to maintain system feasibility and resilience in the face of these impairments. Extensive analytical and simulation results demonstrate that the proposed framework significantly enhances power efficiency, mitigates outage probability, and improves overall system robustness, making RSMA a viable and efficient solution for modern wireless networks with realistic CSI and SIC conditions.

0

eess.SP 2026-05-13 2 theorems

Riemannian covariance matching beats Euclidean methods

Spatial Power Estimation via Riemannian Covariance Matching

The Jensen-Bregman LogDet divergence on positive definite matrices gives more accurate DOA and power estimates especially with weak signals,

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We propose a new method for spatial power spectrum estimation in array processing that leverages the Riemannian geometry of Hermitian positive definite (HPD) matrices. We show that conventional approaches minimize variants of the Euclidean distance between the sample covariance matrix and a model covariance matrix, without considering the fact that covariance matrices lie on the Riemannian manifold of HPD matrices. By exploiting this manifold, we present a Riemannian-aware covariance matching algorithm, termed SERCOM, using the Jensen-Bregman LogDet (JBLD) divergence, which, unlike other Riemannian distances, can be evaluated efficiently without eigen-decomposition. We theoretically compare the JBLD divergence to other Euclidean- and Riemannian-based distances, demonstrating robustness to spectral distortions. Experimental results demonstrate that SERCOM consistently outperforms existing methods in direction-of-arrival (DOA) and power estimation, particularly in challenging scenarios with low SNR, limited number of snapshots, and correlated sources.

0

eess.SP 2026-05-13 2 theorems

Energy split between sensing and uploads improves federated models

ISAC for AI: A Trade-off Framework Across Data Acquisition and Transfer in Federated Learning

A closed-form bound on the learning gap lets each device choose sensing snapshots and transmit powers under its own energy budget to shrink

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In this paper, we propose a resource allocation framework for federated learning (FL) in integrated sensing and communication (ISAC) systems, where we consider not only the reliability of model transfer through communication, but also the quality of data acquisition through sensing in the first place. Unlike existing works that assume training data is pre-collected or only impose a fixed sensing signal-to-noise ratio (SNR) threshold to reflect data quality, we explicitly characterize the relationship between sensing data quality (measured by sensing SNR), dataset size, and the upload reliability in FL training, and exploit this relationship to allocate resources between sensing and communication under a shared energy budget. This is non-trivial due to the intricate coupling among sensing data quality, transmission reliability, and communication resource allocation; nevertheless, it enables a principled joint optimization framework that directly enhances learning performance. Specifically, we derive a closed-form convergence upper bound that quantifies the joint impact of these factors on the FL optimality gap. Utilizing this upper bound, the original intractable optimization problem can be reformulated into a tractable resource allocation problem that jointly optimizes the sensing transmit power, number of sensing snapshots, and communication transmit power at each device subject to individual energy budget constraints. To solve the reformulated problem, we propose a two-layer optimization algorithm with linear complexity, where the outer layer employs golden section search and the inner layer solves per-device subproblems with closed-form solutions.

0

eess.SP 2026-05-13

Signal segment consistency trains modulation classifiers with fewer labels

Modulation Consistency-based Contrastive Learning for Self-Supervised Automatic Modulation Classification

Mod-CL pulls together different parts of one transmission so the model focuses on what stays constant across noise and channel changes.

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Deep learning-based AMC methods have achieved remarkable performance, but their practical deployment remains constrained by the high cost of labeled data. Although self-supervised learning (SSL) reduces the reliance on labels, existing SSL-based AMC methods often rely on task-agnostic pretext objectives misaligned with modulation classification, leading to representations entangled with nuisance factors such as symbol, channel, and noise. In this paper, we identify intra-instance modulation consistency as a task-aware structural prior, whereby different temporal segments of the same signal may differ in waveform while preserving the same modulation type, thus providing a principled cue for task-aligned self-supervision. Based on this prior, we propose Mod-CL, a Modulation consistency-based Contrastive Learning framework that constructs positive pairs from different temporal segments of the same signal instance, to encourage the model to learn shared modulation information while suppressing nuisance variations. We further develop a contrastive objective tailored to Mod-CL, which jointly exploits temporal segmentation and data augmentation to pull together views sharing the same modulation semantics while avoiding supervisory conflicts within each signal instance. Extensive experiments on RadioML datasets show that Mod-CL consistently outperforms strong baselines, especially in low-label regimes, achieving substantial improvements in linear probing accuracy.

0

eess.SP 2026-05-13 2 theorems

Neural model reconstructs radio paths from point clouds

PointNeRT: A Physics Aware Neural Ray Tracing Surrogate for Propagation Channel Modeling

Hop-by-hop prediction with physical constraints replaces mesh building and material rules for channel modeling.

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Ray tracing (RT) has emerged as a key tool for propagation channel modeling and network planning. Conventional RT is based on electromagnetic (EM) wave theory and its application relies on detailed mesh-based environment representations and material properties. In realistic environments, limited environmental geometry and material uncertainties hinder its scalability to complex scenarios. In this paper, we propose a novel physics aware neural RT surrogate named PointNeRT to address these limitations. The proposed model directly takes point clouds as environmental input, and efficiently reconstruct multipath without explicitly constructing mesh models or manually defining EM interaction rules. PointNeRT adopts a hop-by-hop modeling strategy guided by physical interaction constraints. It supports sequential prediction of multipath propagation and power attenuation. Numerical results and experiments demonstrate that the proposed method implicitly captures surface normal characteristics and EM material effects. It further achieves robust generalization in mobility scenarios and provides a physics-guided neural modeling of multipath propagation.

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eess.SP 2026-05-13 Recognition

Backscatter cuts IoT radio power by up to 1000x

Long-Range Backscatter: A Bottom-Up Approach

Bottom-up review of topologies, CSS modulation, and MAC shows how to match low-power tags to harvested energy for perpetual operation.

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Continued progress towards energy-neutral Internet of Things (IoT) nodes expose the wireless communication link as the dominant energy bottleneck. While low-power wide-area network (LPWAN) technologies achieve long-range communication with multiple years of battery life, their active radios hinder reaching full energy neutrality. Long-range backscatter communication emerged as a key enabler, reaching one to three order of magnitude lower power consumption. New advancements leverage concepts from active radio systems such as chirp spread spectrum (CSS) modulation and integrate them on a low-power backscatter tag. This paper presents a comprehensive survey of long-range backscatter communication, using a bottom-up analysis spanning system topologies, hardware architecture, modulation techniques and medium access. Backscatter communication requires different topologies compared to active radios to reach longer communication distances. Different hardware architectures support backscattering a modulated signal with differing complexity, power consumption and spectral efficiency. At the physical layer binary switch-based modulation are well known and provide an easy form of modulation while chirp spread spectrum (CSS)-based modulation gain traction due to their robustness. Medium Access Control (MAC) techniques are examined with a focus on synchronization, concurrency and lightweight feedback mechanisms requiring low-power, low-complexity hardware. Building on these established solutions the paper evaluates the feasibility of long-range backscatter communication in different energy-neutral Internet of Things (IoT) applications. Starting from the available energy budget, harvested through solar, radio frequency (RF) or capacitive harvesting, feasible hardware, modulation and Medium Access Control (MAC) solutions are explored.

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eess.SP 2026-05-13 2 theorems

TOA methods detect both Lamb modes from plate impacts despite noise

Assessment of Time-of-Arrival Estimation Methods for Impact Detection in Isotropic Plates using Piezoceramic Sensors

Assessment on calibrated simulations shows noise mainly affects symmetric arrivals while anti-symmetric estimates stay usable with simple

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This work describes and assesses different methods for estimating the time-of-arrival (TOA) of impact-induced waves in isotropic plate-like structures. The methods considered include threshold crossing (TC), continuous wavelet transform (CWT), short/long term average (SLA), modified energy ratio (MER), and the Akaike information criterion (AIC). Their advantages, limitations, and sensitivities to method-specific parameters are systematically investigated. The assessment is based on synthetic data from transient finite element simulations that are experimentally calibrated with respect to excitation and dispersion characteristics. Wave propagation is monitored using piezoceramic patch sensors bonded to the plate surface, and robustness is evaluated for impacts of varying positions and force profiles, including noise-contaminated sensor signals in order to account for practically relevant measurement conditions. The results show that the methods are capable of detecting the fundamental Lamb wave modes, with nearly all capturing both the symmetric and anti-symmetric mode arrivals under noise-free conditions. In particular, noise primarily impairs the detection of the earliest symmetric-mode arrivals, while meaningful anti-symmetric-mode TOA-estimates can still be obtained by suitable preprocessing or time-frequency analysis. Besides, new contributions to the assessed TOA-estimation methods include a frequency-domain threshold crossing within the CWT framework that improves both robustness and accuracy of TOA-estimation, and the consideration of local minima in the AIC that proves effective for detecting the TOA of the fundamental symmetric mode. Beyond these findings, the research provides practical guidelines and insights into the specific characteristics of each assessed method, supporting accurate and reliable TOA-estimation for applications such as impact localization.

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eess.SP 2026-05-13 2 theorems

Midpoint chirp choice makes AFDM waveforms continuous and lower in OOBE

Stepped Frequency Division Multiplexing: A Jump-Free Continuous-Time AFDM Waveform

Stepped frequency division multiplexing holds frequency at the wrapped chirp midpoint and accumulates phase continuously to eliminate jumps.

abstract click to expand

Affine frequency division multiplexing (AFDM) has emerged as a promising modulation scheme for doubly selective channels, but its canonical continuous-time realization, referred to herein as piecewise continuous AFDM (PC-AFDM), has been observed to exhibit high out-of-band emission (OOBE) whose mechanism has not been analytically characterized. This paper shows that the underlying cause is frequency wrapping, which introduces internal envelope jumps between AFDM sampling instants and generates a high-frequency spectral tail distinct from ordinary block truncation. To eliminate these discontinuities without altering the inverse discrete affine Fourier transform (IDAFT) output sequence, we propose stepped frequency division multiplexing (SFDM). In SFDM, the instantaneous frequency is kept constant at the midpoint of the wrapped chirp within each sampling interval, while the phase is continuously accumulated across interval boundaries. We prove that, under continuous phase accumulation and without additional phase correction, the midpoint choice is the unique sample-preserving choice for arbitrary chirp-rate parameter. The resulting waveform is continuous within each AFDM block, reduces OOBE, and preserves the standard AFDM modulation matrix, guard-interval structure, and receiver processing. Moreover, under fractional-delay propagation, SFDM mitigates the receiver sensitivity that arises when delayed sampling points fall near wrapping-induced discontinuities in PC-AFDM. Numerical results verify the theoretical tail coefficients, demonstrate OOBE reduction, and show improved receiver robustness in the high-percentile and worst-case regimes. These findings establish SFDM as a spectrally cleaner and more reliable physical layer for AFDM systems.

0

eess.SP 2026-05-13 1 theorem

Group symmetries unify all standard signal transforms

Unification of Signal Transform Theory

Each transform is the eigenbasis for covariances invariant under a specific group, allowing automatic discovery from observed data

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We unify the discrete Fourier transform (DFT), discrete cosine transform (DCT), Walsh-Hadamard, Haar wavelet, Karhunen-Lo\`eve transform, and several others along with their continuous counterparts (Fourier transform, Fourier series, spherical harmonics, fractional Fourier transform) under one representation-theoretic principle: each is the eigenbasis of every covariance invariant under a specific finite or compact group, with columns constructed from the irreducible matrix elements of the group via the Peter-Weyl theorem. The unification rests on the Algebraic Diversity (AD) framework, which identifies the matched group of a covariance as the foundational object of second-order signal processing. The data-dependent KLT emerges as the trivial-matched-group limit; classical transforms emerge as the cyclic, dihedral, elementary abelian, iterated wreath, and hybrid wreath cases. Composition rules cover direct, wreath, and semidirect products. The Reed-Muller and arithmetic transforms appear as related change-of-basis transforms on the matched group of Walsh-Hadamard. A polynomial-time algorithm for matched-group discovery, the DAD-CAD relaxation cast as a generalized eigenvalue problem in double-commutator form, closes the operational loop: the matched group of any empirical covariance is discovered without expert judgment, with noise-aware variants via the commutativity residual $\delta$ and algebraic coloring index $\alpha$ for finite-SNR settings. The fractional Fourier transform is treated as the metaplectic $SO(2)$ case with Hermite-Gauss matched basis, and a structural principle relates matched group size inversely to transform resolution. Modern applications (massive-MIMO, graph neural networks, transformer attention, point cloud and 3D vision, brain connectivity, single-cell genomics, quantum informatics) are sketched with their matched groups.

0

eess.SP 2026-05-13 2 theorems

3D Gaussians reconstruct wireless fields across frequencies

XFreq-GS: Cross-Frequency Wireless Radiation Field Reconstruction with 3D Gaussian Splatting

Shared geometry plus frequency-adaptive RF attributes yields accurate PAS maps without retraining per band.

abstract click to expand

Channel modeling is fundamental to the analysis, design, and optimization of wireless communication systems, which, however, accurate wireless channel modeling remains challenging, especially given the increasingly complex wireless environments. As an emerging paradigm, 3D Gaussian Splatting (3DGS)-based channel modeling methods achieve accurate wireless radiation field (WRF) reconstruction and high-fidelity spatial spectrum synthesis. However, existing works only consider a single carrier frequency and fail to adapt to wide-range cross-frequency channels. To address this challenge, we propose XFreq-GS, a cross-frequency Gaussian splatting framework for WRF reconstruction. It employs 3D Gaussian primitives with shared geometry and frequency-adaptive radio frequency (RF) attributes to reconstruct cross-frequency WRF, and synthesizes power angular spectrum (PAS) maps for wireless channel modeling. Experiments show that XFreq-GS outperforms state-of-the-art 3DGS-based methods in PAS synthesis and achieves superior cross-frequency generalization. Code is available at https://github.com/KINGAZ1019/XFreq-GS.

0

eess.SP 2026-05-12 Recognition

Lanczos Krylov method matches exact EVD for adaptive diagonal loading

Adaptive Diagonal Loading using Krylov Subspaces for Robust Beamforming

Ritz values from small subspaces guarantee white noise gain bounds for large microphone arrays without O(M^3) cost.

abstract click to expand

Reliable adaptive beamforming is critical for large microphone arrays operating in highly dynamic acoustic environments. In scenarios characterized by fast-moving talkers and interferers, the available sample support for estimating the spatial correlation matrix is often snapshot-deficient. This deficiency degrades the White Noise Gain (WNG), leading to severe target signal cancellation. To ensure stable and robust beamforming, we previously proposed an adaptive diagonal loading method that leverages the Kantorovich inequality to guarantee the WNG remains strictly within specified bounds. However, accurately determining the smallest necessary loading level requires calculating the extreme eigenvalues of the spatial correlation matrix, a computationally expensive $\mathcal{O}(M^3)$ operation for large arrays. In this paper, we introduce a highly efficient $\mathcal{O}(kM^2)$ estimation technique using Lanczos iterations to build a small Krylov subspace. By projecting the correlation matrix onto a tridiagonal matrix of dimension $k \ll M$, we extract Ritz values that rapidly converge to the exact extreme eigenvalues. Our evaluations demonstrate that this Lanczos-accelerated approach achieves performance identical to exact Eigenvalue Decomposition (EVD), ensuring optimal interference suppression and strict WNG adherence at a fraction of the computational cost.

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eess.SP 2026-05-12 Recognition

Fluid antennas raise hybrid NOMA-AirFL rates under imperfect CSI

Fluid Antenna-Enabled Hybrid NOMA and AirFL Networks Under Imperfect CSI and SIC

Position adaptation reduces aggregation errors and residual interference when channel estimates and cancellation are imperfect.

abstract click to expand

The integration of communication and computation is essential for next-generation wireless systems, especially in scenarios demanding massive connectivity and ultra-low latency. Over-the-air federated learning (AirFL), leveraging the superposition nature of wireless channels, enables fast data aggregation, while non-orthogonal multiple access (NOMA) offers spectrum-efficient connectivity. This paper investigates a fluid antenna (FA)-aided hybrid network, supporting hybrid users comprising both AirFL and NOMA participants. The dynamic reconfigurability of FAs offers significant potential for mitigating interference and enhancing network performance by adapting antenna positions in response to changing channel conditions. We consider practical challenges arising from imperfect channel state information (CSI) and residual interference due to imperfect successive interference cancellation (SIC). To jointly evaluate the learning and communication performance, a hybrid rate metric is introduced. Subsequently, we formulate a robust optimization problem that jointly minimizes the aggregation error while ensuring reliable user communication under CSI and SIC uncertainties. This joint optimization is formulated as a non-convex problem, complicated by the intricate interactions between NOMA and AirFL users and the impact of imperfect CSI and SIC. To solve this problem effectively, we reformulate the optimization as a Markov decision process and solve it using a long short-term memory deep deterministic policy gradient (LSTM-DDPG) algorithm, a memory-based approach within the realm of deep reinforcement learning. Simulation results demonstrate the superiority of the proposed FA-assisted approach over fixed-antenna baselines, particularly under imperfect CSI and SIC conditions, in terms of hybrid rate performance.

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eess.SP 2026-05-12 Recognition

Low-cost sEMG wristband passes safety and signal tests

Design of a validation methodology for a prototype wristband for capturing muscle signals and upper limb movement

Protocol shows leakage currents under limits and correlations above 0.85 with commercial reference, opening access for research labs.

abstract click to expand

Surface electromyography (sEMG) is a noninvasive technique widely used to control myoelectric prostheses and other human-machine interfaces. However, the high cost of commercial systems limits accessibility in academic and research environments, especially in developing countries. This study presents a validation protocol for a low-cost eight-electrode sEMG wristband prototype based on IEC 60601 and ANSI/AAMI EC13 standards. The protocol includes electrical safety tests, such as leakage current measurement, insulation evaluation, and continuity verification between electrodes and circuits. Functional performance was evaluated by comparing signals acquired with the prototype against those obtained from a commercial reference device (PortiLab2) using Pearson correlation, Bland-Altman analysis, and mean squared error. Additional tests included signal stability during rest and contraction, UART and Bluetooth communication, frequency response, mechanical characterization of the casing, and user comfort assessment. Results showed leakage currents between 11.4 uA and 13.5 uA, adequate insulation, stable signal acquisition, and high correlation with the reference system (r > 0.85). Reliable wireless transmission without packet loss was also observed. Limitations included power supply constraints during wireless testing and discrepancies in the frequency response at high-gain stages compared with simulations. Mechanical tests showed elastic behavior of the casing under loads up to 98 N. The proposed protocol provides a practical and reproducible framework for the technical and functional validation of low-cost sEMG systems for research and educational applications.

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eess.SP 2026-05-12 Recognition

Reweighting improves D-optimal bearing sensor placement

Improving D-Optimal Sensor Placement for Bearing-Only Localization via Maximum-Entropy Reweighting

Two-layer method reduces average localization error, with larger gains at higher sensor-to-source ratios and lower noise.

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In this paper, we present a two-layer architecture for bearing-only sensor placement that improves upon classical D-optimal design. The first layer reweights particles by minimizing Kullback-Leibler divergence from the current distribution subject to a distributional accuracy bound, concentrating mass on regions where the posterior is likely to settle, without reference to the sensor model. The second layer performs D-optimal sensor placement with respect to the reweighted Fisher information matrix, steering sensors toward geometrically informative configurations. Because the two layers are structurally decoupled, the reweighting generalizes across sensing modalities while the placement remains specific to bearing geometry. Systematic experiments on multi-source localization at two noise levels show that this reweighting reduces localization error on average, with the benefit growing as the sensor-to-source ratio increases and as measurements become more informative. The improvement is established in the first few iterations of the sequential procedure and persists as the posterior concentrates.

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eess.SP 2026-05-12 2 theorems

Nanosensor networks refresh in-body data every tens of seconds

How Time-Sensitive are IoBNT Networks? An Age of Information Perspective for In-Body Monitoring

The modeled delay suits tracking tissue-level changes such as infections but not faster cellular processes.

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This thesis develops a theoretical framework to evaluate the monitoring capability of IoBNT networks. We consider a scenario in which nanosensors passively flow in the bloodstream and detect biomarkers associated with potential diseases, reporting their detections to external gateways on the skin that host a monitoring device. The nanosensors thus realize an artificial point-to-point communication channel between the disease region and the monitor: some packets reach the destination directly, while others are lost through vessel paths that bypass the gateway. We evaluate the network's monitoring capability over this artificial channel using the \ac{AoI} concept, which jointly integrates sample generation (at the disease region), carrying (nanosensor travel through vessels), and delivery (nanosensor-to-gateway) as random events. These are modeled through (i) a Markov model that follows cardiovascular physiology and (ii) channel models of reported nanocommunication technologies. We compute the Markov transition probabilities using a cardiovascular simulator built as a low-complexity electric circuit model of the human vessels. For the nanosensor-to-gateway link, we model two well-known schemes: ultrasonic and terahertz channels. Integrating these components within the \ac{AoI} framework, we report information freshness via the average \ac{PAoI} metric. Under realistic physiological and communication assumptions, fresh information appears on the monitor within tens of seconds. The network is therefore suitable for monitoring tissue-level processes such as bacterial infections, while more adequate architectures are needed to monitor cellular-scale processes, which occur on timescales below tens of seconds.

0

eess.SP 2026-05-12 2 theorems

RL xApp cuts UAV handovers by 54% in O-RAN

xApp Empowered Resource Management for Non-Terrestrial Users in 5G O-RAN Networks

Proactive DDQN controller with transfer learning anticipates conditions to balance reliability and switching frequency for aerial users.

abstract click to expand

This paper introduces a proactive Unmanned Aerial Vehicle (UAV) mobility management xApp for Open Radio Access Network (O-RAN) Near Real-Time Radio Intelligent Controller (Near-RT RIC) environments, employing Double Deep Q-Network (DDQN) reinforcement learning (RL) enhanced with transfer learning to optimise handover decisions for UAVs operating along predetermined flight trajectories. Unlike reactive approaches that respond to signal degradation, the proposed framework anticipates network conditions and minimises both outage probability and handover frequency through predictive optimisation. The system leverages centralised weight averaging to consolidate knowledge from multiple flight scenarios into a global model capable of generalising to previously unseen operational environments without extensive retraining. A comprehensive evaluation demonstrates that the proposed framework achieves a favourable trade-off between handover frequency and connectivity reliability, reducing handover events by up to 54.6% compared to greedy approaches while maintaining outage probability at practically negligible levels. The results validate the effectiveness of intelligent learning-based approaches for UAV mobility management in next-generation O-RAN architectures, thereby contributing to seamless integration of aerial user equipment into cellular networks.

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eess.SP 2026-05-12 Recognition

RIS and ZF precoding fix rank deficiency in mmWave multiuser links

RIS-assisted Multiuser MISO Transmission and the Impact of Imperfect Channel Estimation

Joint design with equalized pilots keeps interference low despite channel estimation errors.

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This paper proposes the joint design of reconfigurable intelligent surfaces (RIS) and zero-forcing (ZF) precoding for the downlink (DL) multiuser multiple-input single-output (MU-MISO) setup in millimeter-wave (mmWave) bands, where ZF is particularly attractive due to its ability to suppress inter-user interference by exploiting the large antenna arrays and sparse directional channels characteristic of mmWave systems. This ensures efficient spatial multiplexing with manageable complexity, making ZF a practical and in modern 5G/6G deployments. However, a careful design is necessary to overcome potential rank deficiency in the channel matrix. For the MU-MISO case, rank deficiency may arise if users exhibit significantly different channel gains or if, being in far-field, they are aligned with the position of the transmitter. On the other hand, the deployment of a RIS introduces artificial scattering which can shape the radio environment to address those situations. We explore the joint design under perfect channel knowledge, assess the impact of imperfect channel estimation on the bit error rate (BER) and propose a robust design of pilot transmissions that equalizes multiuser interference across users in the presence of channel errors in the precoder design. This evaluation shows the advantages of optimized RIS-aided ZF MU-MISO communication for the DL of wireless systems.

0

eess.SP 2026-05-12 2 theorems

RIS restores full rank for ZF in multiuser MISO downlink

RIS-assisted Multiuser MISO Transmission and the Impact of Imperfect Channel Estimation

Joint phase-shift and precoder design counters alignment-induced rank loss and keeps BER low under imperfect CSI.

abstract click to expand

This paper proposes the joint design of reconfigurable intelligent surfaces (RIS) and zero-forcing (ZF) precoding for the downlink (DL) multiuser multiple-input single-output (MU-MISO) setup in millimeter-wave (mmWave) bands, where ZF is particularly attractive due to its ability to suppress inter-user interference by exploiting the large antenna arrays and sparse directional channels characteristic of mmWave systems. This ensures efficient spatial multiplexing with manageable complexity, making ZF a practical and in modern 5G/6G deployments. However, a careful design is necessary to overcome potential rank deficiency in the channel matrix. For the MU-MISO case, rank deficiency may arise if users exhibit significantly different channel gains or if, being in far-field, they are aligned with the position of the transmitter. On the other hand, the deployment of a RIS introduces artificial scattering which can shape the radio environment to address those situations. We explore the joint design under perfect channel knowledge, assess the impact of imperfect channel estimation on the bit error rate (BER) and propose a robust design of pilot transmissions that equalizes multiuser interference across users in the presence of channel errors in the precoder design. This evaluation shows the advantages of optimized RIS-aided ZF MU-MISO communication for the DL of wireless systems.

0

eess.SP 2026-05-12 2 theorems

TBMA turns AirComp noise into exponential error decay

Exponential Noise Robustness of Type-Based Multiple Access for Over-the-Air Computation

Lattice projection on device-count points makes MSE fall exponentially with energy-to-noise ratio, unlike inverse scaling in standard Air

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This paper studies the robustness of type-based multiple access (TBMA) in over-the-air computation (AirComp) under nonparametric estimation, where no prior knowledge of the data distribution is available. While conventional AirComp approaches rely on amplitude modulations and suffer from noise sensitivity, TBMA enables the use of more structured modulation formats that can be exploited for improved performance. We show that the superposition of transmitted signals in TBMA induces a discrete lattice structure in the received signal space, where each lattice point corresponds to the number of devices accessing a given channel resource. By exploiting this structure through nearest-lattice-point projection, noise effects can be substantially suppressed. The proposed technique achieves an exponential decay of the mean squared error (MSE) with respect to the energy-to-noise spectral density ratio, whereas in conventional techniques the MSE only scales inversely with this ratio. Simulation results validate the theoretical findings and demonstrate that TBMA provides a fundamental robustness advantage over traditional AirComp.

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eess.SP 2026-05-12 Recognition

Near-field effects drive ray-tracing errors in 5G KPIs

Quantifying System Level KPI Deviations of Sionna RT: Material and Near-Field Error Analysis Using a 5G OAI Testbed

Indoor OAI testbed comparison with Sionna RT quantifies material and position-dependent deviations for digital twins

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Ray tracing (RT) has recently gained renewed interest in wireless communications, driven by its integration into digital twin (DT) frameworks for site specific channel modeling. Several previous studies have validated RT at the channel level, yet how these errors propagate into real 5G system level key performance indicators (KPIs) on actual hardware remains unquantified. This paper addresses this gap by comparing Sionna RT simulated channels against vector network analyzer (VNA) measured channels using an OpenAirInterface (OAI) 5G NR testbed. Channel measurements are conducted at 20 receiver positions in an indoor laboratory, with both channel types injected into a hardware in the loop channel emulator interfacing an OAIBOX MAX base station and a Quectel UE. RSRP, PUCCH SNR, and SINR are evaluated under both conditions. The results identify antenna near-field transition effects as a critical position-dependent error source, alongside material property mismatch, providing a quantitative benchmark for digital twin-based 5G and beyond network planning.

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eess.SP 2026-05-12 2 theorems

RAQ-MIMO beats RF-MIMO only when its noise floor is lower

Signal-Dependent Shot Noise Modeling of Rydberg Atomic Quantum Receivers: A Design Perspective

New model shows Rydberg receivers reach non-zero asymptotic rates superior to RF-MIMO when normalized noise is reduced.

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In this paper, we develop a communication-oriented complex baseband equivalent model for superheterodyne Rydberg atomic quantum receivers (RAQRs). The model explicitly captures photodetection-induced signal-dependent shot noise and its coupling with the optical operating point. By leveraging an atomic superheterodyne architecture and a strong local oscillator, we construct a complex baseband representation for both the received signal and the signal-dependent shot noise under both direct incoherent optical detection and balanced coherent optical detection. The derived model reveals that the optical operating point jointly determines the normalized effective receive gain and the equivalent noise background, thereby establishing a traceable gain-noise tradeoff governed by system design. More importantly, the proposed model shows that neglecting signal-dependent shot noise may lead to inaccurate operating-point design. Finally, by extending to the multiple-input-multiple-output (MIMO) case, we derive a lower bound on the achievable rate while considering the signal-dependent shot noise. Our analysis \textcolor{black}{reveals} that the non-zero asymptotic rate of RAQ-MIMO and its superiority over conventional RF-MIMO hinge on the normalized noise floor of the RAQ receive chain falling below that of RF MIMO. Simulation results validate our analysis and yield practical, closed-form design guidelines for RAQR front ends, revealing parameter regimes in which RAQ-MIMO outperforms conventional MIMO systems.

0

eess.SP 2026-05-12 2 theorems

Neural model estimates high-mobility channels without training data

Unsupervised Online Channel Estimation for High-Mobility OFDM via Implicit Neural Representation

Online fitting of a continuous time-frequency function yields near-optimal reliability in V2X simulations and resists environmental shifts.

abstract click to expand

Accurate channel estimation remains challenging in high-mobility wireless systems because Doppler shifts induce severe inter-carrier interference (ICI) in Orthogonal Frequency Division Multiplexing (OFDM). We propose an unsupervised online channel estimation framework based on Implicit Neural Representation (INR). Unlike discrete-grid estimators, the proposed method decouples channel representation from the OFDM sampling resolution by modeling the time-varying frequency-selective channel as a continuous function of time-frequency coordinates. A Sinusoidal Representation Network (SIREN) with Gaussian Fourier feature mapping captures fine-grained channel variations and high-frequency details without offline pre-training or labeled data. For each received slot, the network parameters are updated by per-slot online fitting that minimizes a physics-aware ICI loss, while a confidence-aware decision-directed loop balances reliable pilots and dynamically harvested pseudo-pilots. Simulations in realistic Vehicle-to-Everything (V2X) environments show that the proposed method achieves near-optimal link-level reliability, significantly outperforming Least Squares (LS) and robust Linear Minimum Mean Square Error (LMMSE) estimators. Compared with supervised deep learning baselines, it also exhibits strong out-of-distribution (OOD) robustness under environmental distribution shifts, establishing an adaptable data-efficient physical-layer paradigm.

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eess.SP 2026-05-12 2 theorems

Phase differences carry both data and sensing at LO-free receivers

LO-Free Receiver: Next-Gen Low-Power Joint Communication and Sensing

SPMC recasts JCAS as inference over the unit-circle manifold using only antenna correlations, removing LO and estimation overhead.

abstract click to expand

This paper introduces and analyzes Spatial Phase Manifold Communications (SPMC), a paradigm that facilitates joint communication and sensing (JCAS) over Local Oscillator (LO) free receiver. Information is embedded in, and recovered from, the relative spatial phase between antennas. In contrast to conventional coherent receivers that rely on LOs and on channel estimation/equalization, SPMC exploits antenna-domain correlation to form a baseband observable that is a function of inter-antenna phase differences. Since these phase differences are fundamentally tied to Direction-of-Arrival (DoA) and vice-versa, the formulation recasts communication and sensing as inference over the unit-circle manifold and thus naturally supports JCAS decomposition, i.e., data and spatial sensing are encoded and recovered through DoA signatures. We develop a comprehensive framework comprising: (i) a manifold-domain signal model and corresponding phase-alphabet design; (ii) an LO-free quadrature spatial-correlator receiver architecture that resolves the phase-sign ambiguity without requiring an LO; and (iii) an analysis of error probability and sensing precision, including robustness to phase noise. The proposed paradigm is particularly suited to massive Internet-of-Things (IoT) deployments, for which hardware simplicity, LO distribution cost, power consumption, and seamless sensing integration are critical, especially at millimeter-wave and higher carrier frequencies.

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eess.SP 2026-05-12 2 theorems

Digital twin with AI anticipates blockages to cut wireless interference

A Generative AI-Enhanced Digital Twin Framework for Proactive Interference Management in Hybrid Near/Far-Field Wireless Systems

Indoor simulations of hybrid near/far-field systems show higher SINR and fewer outages than reactive or deterministic methods.

abstract click to expand

The applications of Digital Twins (DT) and Generative AI (GenAI) have demonstrated their capabilities in modeling and learning-based wireless communications. However, their joint potential for proactive wireless system design remains largely underexplored, particularly in extremely large-scale multiple-input multiple-output (XL-MIMO) networks, characterized by hybrid near-field (NF) and far-field (FF) propagation regimes. In this work, we propose an integrated GenAI-enhanced DT framework for proactive interference management in dynamic indoor scenarios. The DT constructs a high-resolution, site-specific virtual replica of the deployment environment, understanding where and why blockage occurs within a realistic 3D representation of the indoor space. Integration of the GenAI module further assists the framework in anticipating and proactively suppressing blockage, rather than reacting after the disruption occurs. Extensive simulation results based on Sionna ray-tracing datasets demonstrate that the proposed framework achieves significant improvements in interference suppression, signal-to-interference-plus-noise ratio (SINR), and outage probability compared to conventional reactive schemes and purely deterministic DT-based approaches.

0

eess.SP 2026-05-12 2 theorems

3D geometry conditions diffusion to cut WiFi localization error 20%

Environment-Conditioned Diffusion Meta-Learning for Data-Efficient WiFi Localization

EnvCoLoc supplies better model starting points for new spaces, reaching usable accuracy with only ten calibration samples in NLOS conditions

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Fingerprinting-based localization often suffers from poor cross-environment generalization, especially when only a few labeled samples are available in the target environment. Existing methods mitigate distribution shifts through domain adaptation or improved signal representations, but they usually ignore environmental geometry or use it in a deterministic manner, limiting their ability to capture diverse multipath variations in complex propagation conditions. To address this issue, we propose EnvCoLoc, an environment-conditioned diffusion meta-learning framework for few-shot fingerprinting localization. EnvCoLoc extracts structured descriptors from 3D point clouds and uses them to condition a latent diffusion generator, which produces environment-specific parameter offsets to modulate a shared meta-learned initialization. This design injects geometry-aware priors into the adaptation process and provides more informative initializations for new environments. To learn the stochastic mapping from coarse environmental descriptors to high-dimensional parameter corrections under limited data, the diffusion generator and localization network are jointly optimized within a two-loop meta-learning framework. The generated offsets capture systematic environment-dependent variations, while gradient-based inner-loop adaptation further refines the model to reduce residual task-specific mismatch. We also provide an excess-loss analysis for finite-step adaptation, theoretically supporting the benefit of geometry-aware initialization. Real-world experiments show that EnvCoLoc consistently improves localization accuracy over baseline methods, achieving up to a 20.0% reduction in mean localization error in NLOS scenarios with only 10 support samples.

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eess.SP 2026-05-12 1 theorem

Utility estimates let UDP packets stop early for hazard detection

Utility-Aware Progressive Inference over UDP Packet Blocks for Emergency Communications

Receiver accumulates decision value from partial blocks to cut packet budget 34% and delay over 1s at 91.5% accuracy.

abstract click to expand

Emergency communications increasingly rely on remote visual inference for timely hazard detection under stringent bandwidth and latency constraints. However, conventional UDP-based visual delivery typically performs inference only after the full payload has been received, even though partially received packet blocks may already contain sufficient task-relevant evidence for reliable decision making. This paper proposes a utility-aware progressive inference framework for emergency communications, which operates directly on UDP packet blocks and determines when sufficient task value has been accumulated for early hazard recognition. Specifically, the sender estimates packet-level decision utility as lightweight control metadata, while the receiver progressively updates partial observations, accumulates the utility of received packets, and triggers an early stop once the normalized utility exceeds a prescribed threshold. Experiments on a fire-scene detection dataset show that, at the main operating point, the proposed method reduces the average packet budget by 34.2% and the decision delay by 1209.17 ms while retaining 91.5% of the full-reception match rate. The method also maintains its advantage over the stability-based baseline under moderate packet loss and different packet-arrival orders. These results demonstrate that packet-level utility provides an effective basis for communication-efficient and delay-aware hazard recognition over UDP-based emergency links.

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eess.SP 2026-05-12 2 theorems

Polar codebook halves Airy beam training overhead in THz

Efficient Airy Beam Training for Quasi-LoS Terahertz Near-Field Communications

It raises spectral efficiency by 13 bit/s/Hz versus prior methods in near-field obstructed links.

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With the enlargement of antenna apertures in 6G Terahertz (THz) communications, the Rayleigh distance expands significantly, rendering near-field propagation a dominant scenario in THz links. Beyond conventional Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) conditions, quasi-LoS scenarios with partial obstructions have emerged as a critical challenge. Airy beams offer a promising solution to circumvent obstacles due to their unique curving trajectory. However, existing Airy beam training methods typically rely on parameter-based sampling or exhaustive search, leading to significant pilot overhead and low training efficiency. In this paper, an efficient Airy beam training framework is proposed to address this research gap. First, the theoretical bounds of Airy beam generation under finite apertures to prune physically invalid codewords are derived. Based on this, a two-stage Non-Uniform Polar Codebook (NUPC) design is presented, utilizing a probing mechanism to resolve the bending direction and a polar-domain spatial sampling strategy to generate Airy beams. To address ultra-low latency requirements, a Fast-Scanning 1D Codebook (FS1C) is further developed that sweeps the entire LoS region with minimal codewords. Simulation results demonstrate that NUPC achieves a higher average spectral efficiency (SE) by 13.4 bit/s/Hz while reducing training overhead by 54.2% compared to the state-of-the-art hierarchical focusing-Airy codebook (HFAC). Furthermore, FS1C reduces overhead by 92.9% with only a marginal 0.3 bit/s/Hz reduction compared with HFAC.

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eess.SP 2026-05-12 Recognition

State-coupled model fixes outage bias in LEO optical link predictions

Revisiting the Independence Assumption in LEO Satellite-to-Ground Optical Links: A State-Coupled Joint Fading Model

Replacing unconditional independence with state-conditioned independence reveals elevation-dependent errors in standard models.

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Performance analysis of low Earth orbit (LEO) satellite-to-ground optical links relies on composite fading models that typically evaluate scintillation and angular loss under the assumption of statistical independence. While ensuring analytical tractability, this assumption decouples fading mechanisms driven by the same atmospheric turbulence and fails to capture the distinct effects of free atmosphere (FA) and boundary layer (BL) perturbations. To model this coupling while preserving tractability, this paper develops a state-coupled joint fading model. In the proposed framework, aperture-averaged scintillation and effective angular loss are jointly characterized by a discrete slow atmospheric state, parameterized by separate FA and BL scaling factors. By replacing unconditional independence with state-conditioned independence, the model enables a closed-form derivation of the outage probability, preserving the computational simplicity of the independent baseline. Numerical results show that the independent baseline can misestimate outage under non-nominal layered turbulence states. This outage prediction bias varies with elevation because the relative roles of scintillation and angular loss change with the link geometry, resulting in different residual angular correction requirements for a given outage target.

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eess.SP 2026-05-11 2 theorems

PICO estimates MRI noise maps seven times faster than replica methods

Fast Voxelwise SNR Estimation for Iterative MRI Reconstructions

Random-phase probes on the image covariance operator deliver accurate voxelwise SNR and g-factor without long computations or closed-form

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Purpose: To develop a fast, general-purpose framework for voxelwise noise characterization in linear and nonlinear iterative MRI reconstructions, recovering the image-domain noise variance from which SNR, $g$-factor, and related image-quality metrics are derived. The framework addresses both the intractability of closed-form formulas beyond Cartesian sampling and the long runtime of Pseudo Multiple Replica (PMR) methods. Methods: We propose PICO (Probing Image-space COvariance), an estimator that operates in the image domain by probing the image-domain noise covariance operator -- or, for nonlinear compressed-sensing reconstructions, the Jacobian of the converged solution -- with random probe images. Complex random-phase probes are shown theoretically and empirically to minimize estimator variance compared with Gaussian or real-valued alternatives. PICO was validated against analytical benchmarks and high-replica PMR references using retrospective Cartesian knee data ($R=2$), prospective non-Cartesian spiral brain phantom data ($R=2,3,4$), and compressed-sensing knee reconstructions ($R=2$). Results: In Cartesian experiments, PICO accurately reproduced analytical SENSE $g$-factor maps. In non-Cartesian spiral imaging ($R=2$), it achieved 1% estimation error in 64 s compared with 462 s for PMR (approximately 7.2x speedup), with the efficiency advantage persisting at higher acceleration. For nonlinear compressed sensing, the Jacobian-based estimator produced noise maps consistent with PMR while converging faster (52 s vs. 95 s; approximately 1.8x speedup). Conclusion: PICO provides a computationally efficient alternative to PMR for voxelwise noise and $g$-factor estimation across generalized iterative MRI reconstructions. By reusing existing reconstruction primitives, it enables voxelwise noise maps to be produced as a routine by-product of the reconstruction pipeline.

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eess.SP 2026-05-11 Recognition

Review unifies exact and approximate results for Gaussian quadratic forms

Quadratic Forms in Gaussian Random Variables Theoretical Results and Applications

Covers real and complex cases, multiforms, ratios, numerical methods, applications, and remaining open questions.

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This manuscript reviews theoretical results and applications related to quadratic forms in Gaussian random variables. It summarizes definitions, canonical representations, exact and approximate distributional results, numerical inversion methods, applications, and selected open problems for real and complex quadratic forms, multiforms, and ratios of quadratic forms.

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eess.SP 2026-05-11 2 theorems

Medium waves show vertical fields deep underground

Subsurface Propagation Characteristics of Medium-Wave Electromagnetic Fields Revealed by Measurements in the Nanatsuo-guchi Quarry: Conceptual Framework of the Subground Wave and the Rainfall Model

Quarry measurements find an Hz component that grows with depth and a sensitivity minimum at 40-50 degrees, unlike surface-wave predictions.

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This paper presents field observations of medium-wave (MW; 300 kHz-3 MHz) radio signals propagating in the subsurface rock environment of the Nanatsuo-guchi quarry, an underground Shakudani Ishi excavation site on Mt. Asuwayama in Fukui City, Japan. MW broadcast signals from a nearby local station (JOFG, 927 kHz, 5 kW), received mainly as a surface wave, and from a distant station (JOAB, 693 kHz, 500 kW), received via ionospheric reflection, were successfully received deep inside the quarry, whereas very-high-frequency frequency-modulated (FM) broadcast signals attenuated rapidly and became undetectable near the entrance. This contrasting behavior highlights the strong wavelength dependence of electromagnetic-wave propagation in subsurface environments. Two-axis rotation measurements were performed using loop antennas to analyze the arrival direction and angular dependence of the received signals. In addition to the horizontal magnetic field component (Hx), dominant near the ground surface, a vertical magnetic field component (Hz) was consistently observed inside the quarry. The relative contribution of Hz increased with depth and was accompanied by systematic variations in the apparent arrival direction. Inclination measurements further revealed a characteristic minimum in reception sensitivity near 40-50 degrees, suggesting a composite magnetic field structure involving both Hx and Hz. These observations cannot be fully explained by conventional surface-wave propagation models based on the Zenneck-Sommerfeld formulation, and instead suggest the formation of a characteristic electromagnetic field structure under subsurface boundary conditions. This study provides experimental evidence for previously unreported MW field behavior in underground spaces and offers new perspectives for subsurface communication and disaster-resilient information systems.

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eess.SP 2026-05-11 Recognition

B2X bootstrap enables sync with ATSC 3.0 in mixed signals

Bootstrap-Based Receiver Synchronization and System Discovery in B2X: An Extension of ATSC 3.0

Design maintains low cross-correlation and works across stationary to high-speed channels for broadcast-mobile interworking.

abstract click to expand

Addressing the increasing and diversified demands of multicast and broadcast services require highly efficient multicast and broadcast technologies. Broadcast networks, such as Advanced Television Systems Committee 3.0 (ATSC 3.0), are inherently designed to support these services and continue to evolve to meet growing performance and scalability requirements. At the same time, smartphones are increasingly used for video streaming and other high-volume services, placing growing pressure on mobile network capacity. Interworking between broadcast and mobile networks is therefore an important enabler for efficient and seamless service delivery. In this context, Broadcast-to-Everything (B2X) extends ATSC 3.0 to support enhanced interoperability with Third Generation Partnership Project (3GPP) mobile systems while maintaining low cross-correlation with ATSC 3.0 bootstrap signals, supporting reliable system identification in scenarios where multiple waveforms may be present. Bootstrap signaling, which enables initial signal detection and synchronization, is a key feature of ATSC-based waveform discovery and synchronization, and B2X further extends this capability through a scalable bootstrap framework supporting a range of bandwidth configurations. This paper investigates system discovery through bootstrap signal detection at the B2X receiver and presents key design-related findings, including parameter selection and cross-testing with ATSC 3.0. We present extensive simulations of the receiver performance under diverse propagation and mobility conditions, ranging from stationary to high-speed scenarios. The results demonstrate the robustness of the B2X bootstrap signaling design across a broad range of channel conditions relevant to multicast and broadcast operation.

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eess.SP 2026-05-11 Recognition

Open-source framework couples vehicle and bridge models in OpenSees

An Open-Source Framework for Coupled Vehicle-Bridge Interaction Analysis Using OpenSees

Iterative subsystem exchange matches benchmarks with R2 above 0.998 and shows when a decoupled approximation is adequate

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Vehicle-bridge interaction (VBI) is important for simulating bridge response under moving vehicular loads and supports applications such as dynamic amplification studies, weigh-in-motion, and indirect bridge monitoring. Although VBI theory is well established, many existing implementations use custom finite element code or research-specific solvers, which limits their reuse. This paper presents an open-source Python framework for VBI analysis built on OpenSees. The bridge and vehicle are modeled as separate OpenSees subsystems and connected through an iterative scheme that exchanges displacement and force values at each time step until convergence. Five vehicle model types are supported, from a single axle spring-mass system to two-axle composite half-cars with body pitch and separate tyre and suspension elements. A decoupled mode is also provided: the vehicle static weight is applied as a moving load on the bridge, and the resulting bridge motion is then used as base excitation for the vehicle. Validation against three published benchmarks (quarter-car, half-car with pitch, and full composite models) shows close agreement, with R2 above 0.998 in all cases. A parametric study reports the accuracy of the decoupled mode as a function of vehicle-to-bridge mass ratio, span length, speed, road roughness class, and background traffic density, and indicates when the decoupled mode is adequate and when full coupling is needed. The complete framework and benchmark configurations are released as open-source software to support reproducible research in vehicle-bridge dynamics.

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eess.SP 2026-05-11 2 theorems

Dynamic mode switching boosts semantic performance in satellite MIMO

Semantic Communication for Multi-Satellite Massive MIMO Transmission: A Mixture of Cooperative Modes Framework

A mixture framework lets multi-satellite systems adapt between coherent and non-coherent transmission using channel statistics to improve 4G

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This paper investigates semantic communications (SemComs) for multi-satellite cooperative massive multiple-input multiple-output (MIMO) transmission, where multiple massive-MIMO satellites jointly serve a common set of multi-antenna user terminals. For the first time, SemComs with image transmission task are integrated into satellite massive MIMO and multi-satellite cooperative transmission. For the two representative cooperative modes, namely coherent transmission (CT) and non-coherent transmission (NCT), we develop multi-satellite CT (MSCT) and multi-satellite NCT (MSNCT) SemCom frameworks, respectively. MSCT adopts a symmetric architecture, whereas MSNCT introduces transmitter-side stream allocation and a two-stage receiver design that combines per-stream semantic extraction with cross-stream semantic-interference exploitation. To instantiate MSCT, we further design a symmetric encoder and decoder network based on hybrid Swin-Transformer and lightweight bottleneck convolutional neural network (CNN) blocks, termed HSTC, where Swin Transformer provides scalable computation and the CNN branch improves performance and convergence. For MSNCT, a Transformer-based backbone is employed to support cross-stream interference exploitation through global attention. Building on these two frameworks, we propose a mixture of cooperative modes (MoCM) framework, in which a permutation-invariant network dynamically switches between MSCT and MSNCT using multi-satellite statistical channel state information, thereby balancing semantic performance and complexity. Simulation results under practical configurations demonstrate the performance gains of the proposed frameworks.

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eess.SP 2026-05-11 2 theorems

The paper introduces SEM-RAG

SEM-RAG: Structure-Preserving Multimodal Graph Compilation and Entropy-Guided Retrieval for Telecommunication Standards

SEM-RAG compiles telecommunication standards into structure-preserving graphs and uses entropy-guided retrieval to reach 94.1% accuracy on…

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Telecommunication standards pose a unique challenge for retrieval systems, where accuracy depends on semantic relevance as well as on preserving the structural logic embedded in the documents, including structured relationships embedded in tables, conditions, and formulas. When these elements are flattened into text, critical dependencies are lost, leading to unreliable retrieval. In this paper, we present SEM-RAG, an end-to-end retrieval framework built around two design choices. First, a layout-aware compiler converts text, tables, and formulas into typed graph primitives. Each table cell is linked to its row headers, column headers, predicates, and source coordinates, while each formula is converted into an operator graph tied to nearby symbol definitions. Second, the compiled graph is compressed with Structural Entropy Minimization (SEM), which avoids LLM-based bottom-up clustering during indexing. A Jensen-Shannon alignment layer and a lightweight query controller serve as supporting retrieval components that map user queries to the right subgraphs, while keeping online cost stable. Experiments on TeleQnA, TSpec-LLM, SPEC5G, and ORAN-Bench-13K show that SEM-RAG improves performance on table-heavy and formula-heavy questions, reaches 94.1\% accuracy on TeleQnA and 93.8\% on ORAN-Bench-13K, and cuts indexing-time token usage by a wide margin relative to standard GraphRAG. These results indicate that structure-preserving compilation is a practical requirement for retrieval over telecom specifications, not merely an optional preprocessing step.

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eess.SP 2026-05-11 2 theorems

Review links new hardware to machine learning allocation for 6G

Comprehensive Review of Advances and Challenges in Next Generation Wireless Networks: From Novel Hardware Technologies to Learning Based Resource Allocation in 6G

Covers reconfigurable surfaces, sensing integration, and learning methods while flagging open challenges for handling massive connectivity.

abstract click to expand

In modern wireless communication systems, there is a rapidly increasing demand for connectivity to wireless networks. Devices such as internet of things (IoT) devices, connected vehicles, smartphones, surveillance systems, and various other applications contribute significantly to this demand. Consequently, next-generation wireless systems must be capable of handling this enormous volume of devices and traffic. In recent years, several technologies have been introduced to address these challenges, including reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), advanced antenna and intelligent surface technologies, and novel multiple access (MA) techniques. Furthermore, due to the limited resources available in communication systems, efficient resource allocation strategies are essential to support complex and high-dimensional optimization problems. In addition, modern communication systems are required to optimize resources within strict time constraints. Therefore, resource allocation solutions must be intelligent and computationally efficient. Conventional optimization techniques, such as convex optimization, are often inadequate for addressing these requirements. To overcome these limitations, novel resource allocation algorithms based on learning methods have been developed. In this paper, we comprehensively investigate advanced communication technologies alongside modern resource allocation optimization methods and algorithms based on machine learning techniques. Subsequently, current challenges of wireless networks are analyzed. Finally, open research challenges are identified.

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eess.SP 2026-05-11 2 theorems

Autoencoder cuts PAPR in OTFS for satellite links

OTFS-IM-Assisted Non-Terrestrial Networks Relying on Autoencoder-Aided Soft-Decision Detection

MB-DFT-S-OTFS-IM with index modulation raises throughput and Doppler tolerance via deep-learning detection in NTN.

abstract click to expand

Orthogonal Time Frequency Space ({OTFS}) modulation offers significant advantages over Orthogonal Frequency Division Multiplexing ({OFDM}), particularly in high speed environments. Hence, we consider {OTFS} transmission over high-Doppler Non-Terrestrial Networks ({NTN}). However, OTFS-based systems inherit some deficiencies from {OFDM}, such as its high peak to average power ratio, the bandwidth efficiency loss due to the cyclic prefix, and the sensitivity to the carrier frequency offset. Against this background, we harness both Multi-Band Discrete Fourier Transform-based Spreading (MB-DFT-S) and Index Modulation ({IM}) in our {OTFS} system, termed as MB-DFT-S-OTFS-IM. More explicitly, 1) DFT-S has been shown to reduce the {PAPR}; 2) {IM} is capable of improving the throughput by harnessing it in the Delay and Doppler ({DD}) domain; and 3) MB-DFT-S-OTFS-IM provides frequency diversity gain, which benefits the tolerance to carrier frequency offset. Furthermore, we propose a {PAPR} reduction method based on a Deep Learning ({DL}) Autoencoder ({AE}) architecture for both hard- and soft-decision detection, where the encoder is specifically trained for minimizing {PAPR} and the decoder is conceived for accurately reconstructing the transmitted signal. Finally, we extend the proposed {AE}-aided {OTFS-IM} scheme constructed for a practical {NTN} channel model, representing a variety of satellite-to-ground schemes.

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eess.SP 2026-05-11 Recognition

Low-complexity denoiser matches heavy mmWave MIMO methods

Low-Complexity Beamspace Channel Denoiser for mmWave Massive MIMO with Low-Resolution ADCs

Beamspace thresholding with composite noise model cuts computation to near-linear while delivering comparable accuracy on FPGA hardware.

abstract click to expand

In this paper, we propose a low-complexity beamspace channel denoising algorithm for millimeter-wave (mmWave) massive multi-input multi-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). The proposed method exploits the inherent sparsity of mmWave channels in the beamspace domain and formulates the denoising problem as a Bayesian binary hypothesis testing under a Bernoulli-complex Gaussian prior. To capture the distortion induced by low-resolution ADCs in a complexity-efficient manner, thermal noise and quantization noise are jointly modeled as a composite noise. Based on this modeling, a closed-form threshold value and a hard-thresholding-based denoising rule are derived to distinguish signal-dominant and noise-dominant components. The resulting algorithm avoids computationally intensive operations such as matrix inversion, iterative optimization, and parameter searching, and achieves near-linear computational complexity with respect to the number of antennas. Furthermore, a hardware-efficient very large-scale integration (VLSI) architecture is developed to enable practical deployment of the proposed algorithm, and is implemented on an AMD-Xilinx Kintex UltraScale+ KCU116 FPGA platform. The design incorporates hardware-aware simplifications and an efficient processing structure, leading to significantly lower latency and reduced hardware resource utilization compared to existing hardware implementations, along with sublinear scaling as the number of antennas increases. Extensive simulation results demonstrate that the proposed method achieves performance comparable to computationally intensive existing approaches while significantly reducing computational complexity.

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0

eess.SP 2026-05-11 Recognition

Holographic surfaces cut costs for 6G joint sensing and data

Holographic Surface Enabled Integrated Sensing and Communications

Reconfigurable leaky-wave antennas replace phased arrays to support simultaneous transmission and environmental sensing with lower power.

abstract click to expand

Integrated sensing and communications (ISAC) is an essential 6G capability for joint data transmission and environmental sensing. To support 6G scenarios with stringent ISAC performance requirements, existing massive-MIMO-based systems are expected to scale toward ultra-massive MIMO. However, this scaling incurs prohibitive cost and power consumption when realized using widely adopted phased arrays with complex phase shifters and feeding networks. Recently, holographic integrated sensing and communications (HISAC) has emerged as a promising paradigm to address this issue. It employs reconfigurable holographic surfaces (RHSs), a type of leaky-wave antenna, as a cost- and energy-efficient implementation of ultra-massive MIMO-based ISAC, and offers enhanced flexibility for ISAC beam synthesis through holographic beamforming. In this paper, we provide a comprehensive tutorial on HISAC, focusing on how RHS-enabled holographic beamforming can be exploited to jointly support communication and sensing under practical hardware constraints. We first introduce the fundamentals of RHSs and discuss the unique leakage power constraint of holographic beamforming. We then present a general optimization framework for HISAC and show how HISAC enhances joint communication and sensing, sensing-assisted communication, and communication-assisted sensing. We further present HISAC system implementations and experimental results. Finally, we outline promising research directions for HISAC, highlighting the potential of HISAC in advancing efficient, flexible, and high-performance ISAC networks.

0

eess.SP 2026-05-11 2 theorems

Few buildings hold the key to accurate wireless digital twins

Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins

An algorithm identifies which structures to detail using a rough model, cutting costs while improving signal predictions and beam control.

abstract click to expand

Wireless digital twins (WDTs) enable site-specific learning, management, and evaluation in wireless networks. However, constructing and maintaining a high-fidelity WDT over large-scale complex environments can be prohibitively expensive, especially in terms of data acquisition, geometric reconstruction, storage, and ray tracing. To address this issue, a task-oriented nonuniform refinement framework for WDTs is proposed, where limited resources are selectively allocated to the WDT components that matter most to wireless fidelity. Specifically, a unified refinement framework is first developed, which maximizes task-level fidelity under resource constraints through fine-grained component-wise fidelity allocation. This framework is then instantiated for building-level geometry refinement in urban WDTs. It is found that different buildings exhibit highly heterogeneous impacts on wireless fidelity. Motivated by this observation, an ellipsoid-guided selective refinement algorithm (EGSR) is proposed. By jointly considering the relevance of each building to both line-of-sight (LoS) and non-line-of-sight (NLoS) propagation paths, its refinement priority can be estimated using only a low-fidelity WDT. Simulations across multiple urban scenarios show that EGSR can substantially improve radio-map fidelity and preserve beamforming effectiveness by refining only a small subset of buildings. These results demonstrate the potential of task-oriented fidelity allocation as a scalable principle for constructing efficient and performance-aware WDTs, thereby facilitating reliable site-specific learning and optimization.

0

eess.SP 2026-05-11 2 theorems

Radio maps cut MIMO channel estimation to 1D search per path

Path-Level Radio Map-Aided Fast and Robust Channel Estimation for Pilot-Starved MIMO-OFDM Systems

Precomputed angular-delay priors match 3D OMP accuracy at 35x lower cost and resist mismatch better.

abstract click to expand

Accurate channel estimation in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is challenging when the number of pilot symbols is much smaller than the number of transmit antennas. Conventional compressed sensing methods perform a three-dimensional search over the angle-of-arrival, angle-of-departure, and delay domains, which incurs high computational cost. In this paper, we propose CHARM (channel estimation with angular-delay radio map), a framework that extracts an angular-delay power spectrum (ADPS) prior from path-level radio maps. The ADPS identifies the joint angle-of-arrival and delay support of the dominant multipath components offline, reducing the online estimation to a one-dimensional angle-of-departure search per path. A trust-region constraint is further introduced to prevent sub-grid refinement from diverging under dictionary mismatch. Simulation results show that CHARM achieves accuracy comparable to three-dimensional joint orthogonal matching pursuit (OMP) with $34.8\times$ speedup at pilot length $T \leq 4$, and that the trust-region variant degrades by only 3.7~dB under severe dictionary mismatch of 0.2~rad standard deviation, compared with 8.2~dB without the constraint.

0

eess.SP 2026-05-11 Recognition

Online policy cuts sensor transmissions by 70 percent

Fair and Efficient Scheduling for Sensor Networks via Online Whittle Index Policy

Whittle-index scheduling on age of incorrect information keeps monitor error acceptable with far fewer polls than round-robin in wake-up rad

abstract click to expand

Wake-Up Radio (WUR) enables resource-constrained, battery-powered sensor nodes to remain in a low-power deep sleep state while continuously listening for a Wake-Up Signal (WUS). Sensor nodes only wake and transmit data after receiving the WUS, significantly reducing energy consumption. However, polling nodes whose transmitted data provides little or no meaningful update to the remote monitor can still result in unnecessary energy usage and increased storage overhead. To address this issue, this paper uses the Age of Incorrect Information (AoII) metric to prioritise the polling of nodes that provide informative updates to the remote monitor. Determining the optimal set of nodes to poll based on AoII can be formulated as a Restless Multi-Armed Bandit (RMAB) problem, which traditionally requires prior knowledge of the monitored process transition dynamics. Since such dynamics are often unknown in practical deployments, we propose an online learning framework based on state estimation to derive Whittle Index AoII (WAoII) and Fair Whittle Index AoII (FWAoII) policies without assuming known transition probabilities. The proposed policies efficiently schedule node polling while adapting to unknown process behaviour. Experimental evaluation using both real-world and synthetic datasets demonstrates that the proposed online WAoII policy can reduce packet transmissions by up to 70\% compared to the widely used Round Robin (RR) polling strategy, while maintaining Root Mean Squared Error (RMSE) values within acceptable application error tolerances. These results demonstrate the effectiveness of WAoII and FWAoII as energy-efficient polling techniques for low-power WUR sensor networks.

0

eess.SP 2026-05-11 2 theorems

Text and image data alignment improves few-shot power load forecasts

PrismNet: Viewing Time Series Through a Multi-Modal Prism for Interpretable Power Load Forecasting

PrismNet augments time series with text and images then aligns them via guided contrastive learning to raise accuracy when historical datais

abstract click to expand

Load forecasting plays a pivotal role in the safe and stable operation of power systems. Conventional deep learning methods often struggle to adapt to few-shot scenarios frequently encountered in industrial applications. Existing multi-modal approaches typically overlook domain-specific cross-modal semantic alignment and lack sufficient mechanism interpretability. To address these challenges, this study proposes PrismNet, an interpretable multi-modal framework for power load forecasting. First, a multi-modal augment module integrates text and image modalities to strengthen load time series representations, empowering the model with few-shot learning capabilities. Subsequently, we design a Partial Information Decomposition (PID) guided multi-modal contrastive learning (CL) mechanism to achieve domain-specific cross-modal semantic alignment. This process elucidates the intrinsic interactions among modalities and offers a new lens for interpretability. Extensive experiments on real-world public datasets demonstrate that PrismNet outperforms strong deep learning and multi-modal baselines, particularly in few-shot settings, while providing a trustworthy and interpretable solution for safety-critical electric load scenarios. Our code is available at https://anonymous.4open.science/r/PrismNet-9DFC.

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eess.SP 2026-05-11 2 theorems

Beam squint stays significant in U6G XL-MIMO

Wideband Precoding for U6G XL-MIMO Systems: Beam Squint Boundaries and Channel Slicing

Near-field effects hit first with larger arrays, yet channel slicing raises spectral efficiency by handling paths separately.

abstract click to expand

The unconventionally large aperture of extremely large-scale multiple-input multiple-output (XL-MIMO) arrays, in conjunction with the wider bandwidths in the upper-6 GHz (U6G) frequency bands, will very likely lead to non-negligible beam squint effects. In the context of a limited number of radio frequency (RF) chains, and by adopting hybrid precoding, the beams at different subcarriers may point to different positions and compromise the spectral efficiency (SE). Moreover, the existence of \textit{multiple paths} in U6G XL-MIMO channels also entails practical challenges for wideband precoding. It is therefore essential to ascertain whether the beam squint effect is pronounced for U6G XL-MIMO systems and design efficient wideband precoding schemes. To address these challenges, precise antenna-domain and frequency-domain wideband boundaries are derived from the near-field and far-field perspectives, respectively. These boundaries can inform the design of wideband precoding in future system settings. Subsequently, a channel slicing scheme is proposed for wideband precoding. The process involves the segmentation of U6G XL-MIMO channels into multiple blocks, with the objective of mitigating the beam squint effect for each path. The antenna-domain and frequency-domain slicing methods are developed for multipath and multiuser scenarios, respectively. The simulation results prove that the beam squint effect remains a significant issue for U6G XL-MIMO systems, while the near-field effect invariably precedes the beam squint effect as the array size and the bandwidth increase. In addition, the proposed scheme can greatly improve the SE.

0

eess.SP 2026-05-11 2 theorems

XL-MIMO requires aperture and spacing rules for clean Airy beams

Analytical Framework of Airy Beams in Near-Field XL-MIMO: From Ideal Optics to Wireless Reality

Framework shows how to keep non-diffracting beams on track and when they improve efficiency over Gaussian options.

abstract click to expand

The synthesis of Airy-profiled wavefronts has emerged as a pivotal paradigm for advanced electromagnetic engineering, attributed to their intrinsic non-diffractive propagation, transverse self-acceleration, and structural self-healing properties. While the advent of extremely large-scale multiple-input multiple-output (XL-MIMO) and the elevation in frequency bands for sixth generation wireless systems provide the physical foundation for generating such structured beams, their wireless realization is fundamentally governed by hybrid precoding architectures, finite array apertures, and discrete antenna topologies. These constraints induce significant deviations from ideal optical Airy beam models, necessitating a rigorous re-characterization of Airy beams in practical wireless contexts. Consequently, this paper establishes an analytical theoretical framework to explicitly characterize Airy beam propagation in near-field XL-MIMO and derives the constraints on array aperture and antenna spacing to sustain distortion-free main lobe trajectories. Furthermore, quantitative metrics are developed to rigorously evaluate the performance trade-offs between Airy beams and Gaussian focusing beams, thereby providing systematic guidelines for their deployment in scenario-dependent wireless applications. Numerical results corroborate the proposed analytical theoretical framework of Airy beams in near-field XL-MIMO, and demonstrate the potential to achieve robust communication and spectral efficiency (SE) improvement in certain scenarios.

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eess.SP 2026-05-11 2 theorems

Flow models raise MU-MIMO sum rates by keeping channel geometry

Channel Geometry Preserving Generative Models for CSI Feedback in MU-MIMO

Generative decoders at the base station preserve user separations that MMSE averages away, especially when interference is strong.

abstract click to expand

Under limited feedback, channel state information (CSI) reconstruction for multiuser multiple-input multiple-output (MU-MIMO) precoding is challenging, since the precoder should provide not only beamforming gain, but also robust suppression of inter-user interference. This paper revisits this classic problem by developing powerful decompression techniques at the base station (BS) that harness modern deep generative models. We propose two novel BS-side flow-matching generative CSI decoders that progressively transform either a simple prior or an initial CSI estimate into a reconstruction consistent with the feedback-conditioned channel distribution. We further show theoretically that conventional minimum mean-squared-error (MMSE)-based reconstructions of CSI often result in centroid-like compromises that fail to preserve the posterior geometry needed for inter-user interference suppression. In other words, MU-MIMO precoding based on MSE-oriented CSI reconstructions can be suboptimal, since such reconstructions frequently fail to maintain user orthogonality. Numerical results in FR3 spectrum show that the proposed flow-based methods consistently outperform MSE-based baselines in downlink sum-rate, with the advantage especially pronounced in interference-limited and spatially dense regimes. These results suggest that posterior-guided flow reconstruction is better aligned with MU-MIMO precoding than traditional MMSE-oriented CSI feedback, since it better preserves the channel geometry needed for user separation.

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eess.SP 2026-05-11 Recognition

LLMs collaborate across devices and cloud to meet resource limits

Large Language Models over Networks: Collaborative Intelligence under Resource Constraints

Message-based task sharing between independent models handles latency, connectivity, and residency demands no single endpoint can satisfy.

abstract click to expand

Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a growing class of applications, including those operating under intermittent connectivity, sub-second latency budgets, data-residency constraints, or sustained high-volume inference. On-device deployment is in turn constrained by limited computation and memory. No single endpoint can deliver high-quality service across this spectrum. This article focuses on collaborative intelligence, a paradigm in which multiple independent LLMs distributed across device and cloud endpoints collaborate at the task level through natural language or structured messages. Such collaboration strives for superior response quality under heterogeneous resource constraints spanning computation, memory, communication, and cost across network tiers. We present collaborative inference along two complementary and composable dimensions: vertical device-cloud collaboration and horizontal multi-agent collaboration, which can be combined into hybrid topologies in practice. We then examine learning to collaborate, addressing the training of routing policies and the development of cooperative capabilities among LLMs. Finally, we identify open research challenges including scaling under resource heterogeneity and trustworthy collaborative intelligence.

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eess.SP 2026-05-11 Recognition

Graph model tracks skew buildup in cascaded channels

Intra-Pair Skew Propagation Graph (ISPG): An Analytical Model for Cascaded Channels

ISPG integrates skew into S-parameter calculations to predict cumulative effects, matching simulations and 2m cable tests.

abstract click to expand

As data rates scale, intra-pair skew has become a critical bottleneck for high-speed differential signaling. Current analytical models are often limited, while 3D electromagnetic simulations are computationally intensive. This paper presents a comprehensive analytical framework for intra-pair skew in generic asymmetric coupled transmission lines, explicitly integrating skew into S-parameter formulations. We introduce the Intra-pair Skew Propagation Graph (ISPG), a novel graph-based methodology for calculating cumulative skew in complex, cascaded channels. The proposed framework is validated against both S-parameter simulations and empirical measurements of a 2m twinax cable assembly, demonstrating excellent accuracy and robustness for high-speed interconnect design.

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eess.SP 2026-05-11 Recognition

RL method lets UAV meet QoS for city mobile users in real time

Online UAV Trajectory Planning Under QoS Constraints to Mobile Users in Urban Environments

Online learning optimizes path and resource allocation under rate, power and fronthaul constraints while balancing throughput and fairness.

abstract click to expand

This paper studies real-time trajectory planning and radio resource allocation for a single uncrewed aerial vehicle (UAV) serving multiple mobile ground users in an urban environment. The downlink system considers heterogeneous user mobility, where independent users and group users coexist and interact. To ensure reliable communication, quality-of-service (QoS) constraints are imposed by requiring the instantaneous data rate of each user to satisfy a minimum threshold whenever feasible. A capacity limited high-altitude platform (HAP)-assisted wireless fronthaul is further considered to capture practical network-side transmission limitations. Under these constraints, the UAV updates its position at each time slot, while QoS-aware bandwidth and power are jointly allocated under total bandwidth and transmit power constraints to maximize system throughput. Due to user mobility and urban blockages, the resulting problem is highly nonconvex and time-varying. An online reinforcement learning (RL) based approach is adopted for real-time UAV trajectory optimization. Simulation results show that the proposed method satisfies the QoS, fronthaul, and radio resource constraints and achieves a balanced trade-off between throughput and user fairness.

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eess.SP 2026-05-11 2 theorems

Deep RL tunes pinching antennas for moving users

Joint Beamforming and Antenna Placement Optimization in Pinching Antenna Systems with User Mobility: A Deep Reinforcement Learning Approach

DDPG jointly optimizes beamforming and waveguide pinch points to raise average sum rate under mobility and blockage.

abstract click to expand

Recently, the pinching antenna systems (PASS) have attracted significant attention due to their ability to exploit dynamically reconfigurable pinching points along waveguides for flexible signal transmission. However, existing work largely overlooks user mobility although the optimal pinching configuration is highly dependent on the user's location and must be continuously adjusted. In this work, we investigate a PASS-enabled system model in which a base station (BS) serves a mobile user. We formulate an optimization problem that aims to maximize the user's average sum rate over a predefined time horizon while satisfying quality-of-service (QoS) constraint. This objective is achieved by jointly optimizing the beamforming vector at the BS and the pinching locations along the waveguides. Nevertheless, the resulting problem is highly non-convex and challenging to solve using conventional optimization techniques due to the intricate coupling among variables. The difficulty is further exacerbated by environmental randomness arising from user mobility and a probabilistic blockage model. This reveals a key engineering challenge: the performance gains of PASS critically rely on the ability to track or predict user trajectories in real time. To address these challenges, we adopt a deep deterministic policy gradient (DDPG) approach within a reinforcement learning framework, which is well-suited for continuous state and action spaces. Finally, extensive simulations are conducted to validate the proposed approach and demonstrate the importance of real-time configurability.

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eess.SP 2026-05-11 2 theorems

Sparse RF readings alone rebuild site-specific fields without maps

PropSplat: Map-Free RF Field Reconstruction via 3D Gaussian Propagation Splatting

3D anisotropic Gaussians adjust a baseline path-loss model using only direct measurements along observed paths, matching or beating map-reli

abstract click to expand

Building a site-specific propagation model typically requires either ray-tracing over detailed 3D maps or dense measurement campaigns. Both approaches are expensive and often infeasible for rapid deployments where geographic data is unavailable or outdated. We present PropSplat, a map-free propagation modeling method that reconstructs radio frequency (RF) fields using 3D anisotropic Gaussian primitives. Each Gaussian encodes a scalar path loss offset relative to an explicit baseline path loss model with a learnable path loss exponent. Gaussians are initialized along observed transmitter--receiver paths and optimized end-to-end to learn the propagation environment without external information like floor plans, terrain databases, or clutter data. We evaluate PropSplat against wireless radiance field methods NeRF$^2$, GSRF, and WRF-GS+ on two real-world datasets. On large-scale outdoor drive-tests spanning multiple topographical regions at six sub-6 GHz frequencies, PropSplat achieves 5.38 dB RMSE when training measurements are spaced 300m apart and outperforms WRF-GS+ (5.87 dB), GSRF (7.46 dB), and NeRF$^2$ (14.76 dB). On indoor Bluetooth Low Energy measurements, PropSplat achieves 0.19m mean localization error, an order of magnitude better than NeRF$^2$ (1.84m), while achieving near-identical received signal strength prediction accuracy. These results show that accurate site-specific propagation reconstruction is achievable from sparse RF-native measurements. The need for geographic data as a prerequisite for scalable RF environment modeling is reduced.

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eess.SP 2026-05-11 Recognition

Nine-transistor cell stores current spikes at nanowatt power

A Fully Tunable Ultra-Low Power Current-Mode Memory Cell in Standard CMOS Technology

Independent tuning of threshold, hysteresis and gain enables asynchronous logic and recurrent units in standard CMOS.

abstract click to expand

This work introduces a fully tunable, ultra-low power unipolar memory cell inspired by the Schmitt-trigger comparator and designed in CMOS using only nine transistors. The proposed circuit operates entirely in the current domain and exploits a novel feedback configuration between two interdependent Heaviside-like thresholding elements to produce tunable bistable switching behavior. Its three key parameters-threshold current, hysteresis width, and output gain-are independently tunable via programmable bias currents, enabling flexibility across diverse analog computing applications. Unlike prior Schmitt-trigger designs, it simultaneously achieves current-mode operation, nanowatt-range power consumption, temperature stability, and full tunability, solely using standard MOSFET elements. Schematic-level simulations in a 180 nm CMOS process confirm robust hysteresis and resilience to device mismatch. Building on this circuit, we develop a complete family of spike-based logic gates using three-level current encoding, where the bistable memory retains the polarity of the last spike on each input indefinitely, enabling asynchronous logic operations without temporal windowing or refresh mechanisms. The same circuit also serves as the primitive for Bistable Memory Recurrent Units in analog neural networks, where the quantized hidden states provide inherent noise immunity. Together, these capabilities position the design as a versatile building block for next-generation neuromorphic processors integrating memory, logic, and recurrent computation.

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eess.SP 2026-05-11 2 theorems

FPGA offload hits 900 Mbps PHY speed in smartphone test

RFNoC-Based FPGA Offloading for Fully Programmable PHY Acceleration

RFNoC framework accelerates LDPC, rate matching and scrambling while driving the radio front-end inside OpenAirInterface.

abstract click to expand

Hardware acceleration has emerged as a key research topic for supporting computationally intensive signal processing and artificial intelligence applications in 6G research and development studies. This paper presents an RF Network on Chip (RFNoC) based hardware acceleration framework that offloads key physical layer procedures to a field programmable gate array (FPGA). The proposed design accelerates procedures, including low density parity check codes (LDPC) encoding and decoding, rate matching and unmatching, interleaving and deinterleaving, scrambling and descrambling, and log likelihood ratio estimation. The accelerator is integrated directly into the OpenAirInterface radio access network software, enabling simultaneous use of the FPGA as driver of the radio front end and a high throughput accelerator. The proposed system is validated through real time experiments with a commercial smartphone successfully connecting to the network. The implementation results demonstrate that a throughput of about 900 Mbps is achiievable using a moderate FPGA resource utlization.

0

eess.SP 2026-05-11 2 theorems

Continuous EM manifold model raises near-field accuracy for any array

A Novel Framework for the Characterization of Continuous Electromagnetic Manifolds

A surface feeding function and 2D patch quadrature remove point-source errors and hardware limits in MIMO characterization.

abstract click to expand

A unified framework for the characterization of continuous electromagnetic (EM) manifolds for arbitrary multipleinput multiple-output (MIMO) system geometries is presented. The EM manifold refers to the set of all physically realizable radiated field vectors, parameterized by the array excitation, that encodes the full spatial structure of the antenna system including near-field phase variations, polarization, and mutual coupling. Building upon the discrete moment-matrix formulation, the proposed framework addresses three fundamental limitations simultaneously: (i) point-source near-field modeling errors in the radiation operator; (ii) confinement of the beamforming space to the $N$-dimensional subspace dictated by hardware port count; and (iii) restriction to linear (1D) array geometries. Each mesh element is modeled as a two-dimensional (2D) planar patch, whose spatially averaged Green's function is evaluated via Gauss-Legendre (GL) quadrature, yielding superior nearfield accuracy at negligible additional cost. A continuous feeding function $w(\mathbf{p})\in L^2(\mathcal{S}_\mathrm{T})$ is introduced as the infinite-dimensional limit of the $N$-port network, enabling optimization over a higher dimensional current subspace, decoupled from hardware constraints. Full-wave MATLAB Antenna Toolbox validation confirms near-field accuracy improvements over the state-of-the-art (SotA) baseline for both linear and planar array geometries, while maintaining reasonable computational complexity.

0

eess.SP 2026-05-11

AI on fixed wireless CSI detects drones at 0.63% miss rate

AI-Empowered Low-Altitude Economy: Cooperative Sensing With Fixed Wireless Access

Cooperative analysis from multiple base station-CPE links meets 3GPP standards with 6.5 m positioning accuracy.

abstract click to expand

The rapid growth of the low-altitude economy has intensified safety concerns arising from unauthorized unmanned aerial vehicles (UAVs), positioning UAV supervision as a key use case in 3GPP. To precisely sense such UAVs with wide coverage and low cost, we leverage fixed wireless access (FWA) customer premises equipment (CPEs), static, densely deployed devices that serve as wireless cameras for the radio environment. We develop an artificial intelligence-empowered two-stage cooperative sensing pipeline that exploits uplink channel state information (CSI) from multiple base station-CPE pairs for UAV detection and localization. In cooperative detection, lightweight CSI features are first individually extracted by neural network, and then adaptively integrated through an attention-based scheme to declare UAV presence. The learned attention scores effectively identify the critical pairs during detection, while facilitating UAV-affected pair selection for subsequent localization. For cooperative localization, neural network initially generates individual estimates and extract CSI features from selected pairs. These estimates, together with features and pair indexes, are fused using a Transformer to produce a precise cooperative estimate. Simulations show that cooperative schemes significantly reduce the missed detection probability to 0.63% and realize a 95%-confidence positioning error of 6.50 m, satisfying 3GPP requirements and showing the potential of FWA-assisted cooperative sensing. Dataset and codes are available on GitHub.

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eess.SP 2026-05-11 Recognition

Coarse maps boost CSI accuracy with sparse pilots

Geometry-Aided Channel Deduction: A Robust Channel Acquisition Framework Utilizing Coarse Scenario Prompt

Ray-traced pseudo-channels from environmental geometry are fused with limited measurements in a neural network to recover full channel state

abstract click to expand

Channel state information (CSI) is critical for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Pilot-based channel estimation methods suffer from high pilot overhead and low channel acquisition quality, while pilot-free approaches typically impose impractical demands on positional or environmental information precision. This paper proposes geometry-aided channel deduction (GCD), which leverages readily available geometric information to assist channel acquisition. The environmental map and base station position together constitute the scenario geometry, which can provide geometric channel features through ray tracing. To obtain the complete channel, the user first retrieves approximate geometric features by performing neighborhood searching within a pre-extracted geometric feature set, and then converts them into pseudo channels through a priori designed feature alignment. These pseudo channels serve as contextual prompt, providing supplementary channel features beyond those derived from pilot-based estimate. Finally, a neural network fuses these pseudo channels with partial estimate to generate the complete channel. Comprehensive experiments validate the superiority of our method, which achieves the leading accuracy in channel acquisition under sparse pilot conditions, demonstrates strong generalization capabilities in new scenarios and dynamic environments, and exhibits robust resilience against user position errors and non-ideal environmental information.

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eess.SP 2026-05-11 Recognition

Pilot projection cuts leakage in full-duplex cell-free MIMO

Over-the-Air Beamforming Design for Full-Duplex Cell-Free Massive MIMO Systems

Iterative signaling with best-response updates at users yields faster convergence and higher sum rates than separate uplink-downlink designs

abstract click to expand

We study a full-duplex (FD) cell-free massive MIMO system where distributed access points (APs) operate in FD mode while user equipments (UEs) remain half-duplex. Although simultaneous uplink (UL) and downlink (DL) transmissions improve spectral efficiency, they introduce residual self-interference, AP-to-AP coupling, and UE-to-UE cross-link interference. Building on prior over-the-air distributed beamforming frameworks, we develop a fully distributed beamforming design based on iterative UL and DL pilot signaling under a joint UL and DL sum mean-square error criterion that explicitly accounts for these interference components. In FD operation, simultaneous UL and DL pilot transmissions cause UE-to-UE pilot leakage, which contaminates the reconstruction of the cross terms required for AP-specific beamforming design. To mitigate this effect, we introduce a pilot-domain projection of the received signals at the UEs, which suppresses the interference component and enables accurate cross-term reconstruction at the APs. In addition, best-response updates at the UEs are employed within the alternating optimization framework to improve convergence under strong UE-to-UE interference. Numerical results demonstrate faster convergence and higher effective sum rate, with particularly significant gains for strongly interfering UEs, compared with both separate UL and DL distributed OTA beamforming training schemes and designs based solely on local channel state information.

0

eess.SP 2026-05-11 1 theorem

Motion tokens cut video transmission to 1% for action understanding

Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference

Edge sends 9-bit pose indices per frame; cloud VLM matches full-video accuracy at one-fifth the latency.

abstract click to expand

The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditional approaches require massive video data to be transmitted from resource-constrained edge devices to powerful cloud servers, incurring prohibitive uplink bandwidth consumption and unacceptable latency while raising privacy concerns. To overcome these bottlenecks, we propose a task-oriented communication framework for human action understanding (TOAU) through edge-cloud collaboration. Our framework utilizes a monocular pose estimator to extract continuous joint coordinates from raw videos, followed by a vector quantized variational autoencoder (VQ-VAE) to convert these coordinates into discrete motion tokens. Consequently, only a compact sequence of codebook indices is transmitted over the network, consuming as few as 9 bits per frame and avoiding privacy leakages. At the cloud server, a lightweight projector aligns these motion tokens with the embedding space of a large vision-language model (VLM) to facilitate complex action understanding, which is trained with an efficient instruction tuning paradigm. Comprehensive evaluations on three benchmarks demonstrate that our TOAU system reduces the transmission payload to approximately 1\% and the system latency to around 20\% compared to video codec-based solutions, while delivering comparable action understanding accuracy.

0

eess.SP 2026-05-11 Recognition

VO2-switched RIS achieves 10-20 dB gain for 60-degree mm-wave steering

A Microfabricated PCM-Switched Reconfigurable Intelligent Surface for Wideband Millimeter-Wave Beam Steering

10x20 array on alumina steers beams across 18 percent bandwidth at 33 GHz via column-wise serial biasing

abstract click to expand

This paper presents the design, fabrication, and experimental validation of a reconfigurable intelligent surface (RIS) employing electrically actuated vanadium dioxide (VO2) switches for millimeter wave beam steering. The proposed RIS is realized using a multilayer microfabrication process on an alumina substrate, enabling monolithic integration of hundreds of sub-4 micrometer VO2 switching elements within deeply subwavelength unit cells, approximately one-fifth of the wavelength. The switching-induced modulation of surface impedance is analyzed through full-wave simulations, and the resulting phase and amplitude responses are experimentally characterized using a custom WR-28 waveguide measurement setup. Based on the validated unit-cell design, a 10 x 20 RIS array integrating 200 VO2 switches is fabricated. The switches within each column are serially biased using integrated routing lines, allowing programmable control of the spatial phase distribution across the surface. Synthesized phase profiles enable dynamic beam steering, resulting in measured far-field gain enhancement of 10-20 dB over an 18 percent fractional bandwidth centered at 33 GHz, with steering angles up to 60 degrees. The measured radiation patterns are in good agreement with semi-numerical channel modeling predictions. By combining thin-film PCM switching with an integration-aware unit-cell design, this work demonstrates a scalable RIS architecture that mitigates packaging parasitics and footprint limitations inherent to conventional semiconductor-based implementations, providing a practical pathway toward higher-frequency reconfigurable surfaces.

0

eess.SP 2026-05-11 2 theorems

Energy difference on two resources replaces CSI for wireless federated learning

Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning

Positive and negative update parts sent on orthogonal elements let the server recover signed sums from received energies alone, preserving 1

abstract click to expand

Over-the-air federated learning (OTA-FL) reduces uplink latency by exploiting waveform superposition, but conventional analog aggregation schemes typically require instantaneous channel state information (CSI), channel inversion, and coherent phase alignment, which can be difficult to maintain in practical wireless systems. This paper proposes resource-element energy difference (REED), a noncoherent aggregation primitive for continuous signed updates that avoids instantaneous CSI. REED maps the positive and negative parts of each real-valued update to transmit energies on two orthogonal resource elements with independent phase dithers, and the server estimates the signed aggregate from their energy difference. With only slow-timescale calibration of average channel powers, REED is unbiased for the desired signed sum and admits an exact closed-form variance under Rayleigh fading. We incorporate REED into full-participation FedAvg and prove a smooth nonconvex stationarity bound. Under an average per-client energy budget, the aggregation gain can be scheduled so that the REED-induced perturbation scales quadratically with the local stepsize, yielding the canonical (1/sqrt(T)) stationarity rate. Experiments on MNIST and Fashion-MNIST demonstrate that REED closely matches clean FedAvg and coherent CSIT aggregation in IID settings, while maintaining stable convergence with a moderate performance degradation under strong data heterogeneity.

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eess.SP 2026-05-11 Recognition

Frequency translation enables omnidirectional SAR calibration

Omnidirectional Transponder for Narrow-band Radar Calibration

Compact single-antenna transponder provides full viewing angles and precise phase tracking for circular and bistatic radar.

abstract click to expand

Conventional reference targets for Synthetic Aperture Radar (SAR) calibration, such as corner reflectors and standard transponders, are often inherently large and suffer from limited viewing angles. This paper presents a novel frequency-translating transponder architecture that circumvents these limitations, enabling a truly compact, single-antenna design capable of omnidirectional operation. While the operational bandwidth is consequently narrowed, restricting its use primarily to azimuth-direction calibrations, the design excels at providing highly accurate pulse-to-pulse phase measurements across the synthetic aperture. The transponder was prototyped and experimentally validated with a drone-mounted SAR. The results demonstrate the transponder's significant potential for applications requiring omnidirectional reference targets, such as Circular SAR (CSAR) and bistatic SAR.

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eess.SP 2026-05-11 2 theorems

Neural channel maps raise multi-UAV throughput

Towards Intelligent Low-Altitude Wireless Network Deployment: Differentiable Channel Knowledge Map Construction and Trajectory Design

Differentiable construction from continuous locations and environmental features enables joint power-bandwidth-trajectory optimization.

abstract click to expand

Channel knowledge map (CKM) has emerged as a promising technique to leverage prior propagation knowledge in low-altitude wireless networks (LAWNs), yet state-of-the-art grid-based CKM construction methods struggle to support efficient LAWN deployment due to their lack of differentiability with respect to continuous locations of unmanned aerial vehicles (UAVs). To overcome this limitation, we propose a differentiable CKM-triggered trajectory optimization framework for LAWNs. Firstly, we propose a location-oriented CKM construction method that directly maps continuous spatial coordinates to channel gain. In particular, a shared convolutional neural network (CNN) is employed to encode high-level environmental features from conditional inputs. These features are then sampled based on location information to form a fused regressor-conditional multilayer perceptron (c-MLP) or conditional Kolmogorov-Arnold network (cKAN)-for channel gain prediction. We further propose a joint power, bandwidth, and trajectory optimization (JPBTO) method for multi-UAV systems, with the constructed differentiable CKM employed to evaluate the communication performance. The formulated non-convex problem is solved via alternating optimization and successive convex approximation. Numerical results show that the proposed framework enables location-aware differentiability of the CKM, while achieving higher accuracy than the methods without environmental features. Furthermore, the proposed CKM-JPBTO achieves a significantly higher minimum throughput than the conventional statistical channel model-based JPBTO.

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eess.SP 2026-05-08 Recognition

DGD tracks streaming minimizers at O(1/t) under uniform weights

Decentralized Time-Varying Optimization for Streaming Data via Temporal Weighting

Uniform weighting lets tracking error vanish linearly in time; discounting and agent heterogeneity each leave a persistent floor.

abstract click to expand

Classical optimization theory largely focuses on fixed objective functions, whereas many modern learning systems operate in dynamic environments where data arrive sequentially and decisions must be updated continuously. In this work, we study optimization with streaming data over a distributed network of agents. We adopt a structured, weight-based formulation that explicitly captures the streaming-data origin of the time-varying objective: at each time step, every agent receives a new sample, and the network seeks to track the minimizer of a temporally weighted objective formed from all samples observed across the network so far. We focus on decentralized gradient descent (DGD) with a limited communication/computation budget, where at each time step, only a limited number of DGD iterations can be performed before the objective changes again. For strongly convex and smooth losses, we analyze the tracking error with respect to the time-varying minimizer through a fixed-point theory lens. Our analysis reveals that the tracking error decomposes into a fixed-point tracking term and a bias term induced by data heterogeneity across agents. We specialize the analysis to two natural weighting strategies: uniform weights, which treat all samples equally, and exponentially discounted weights, which geometrically decay the influence of older data. Under uniform weighting, DGD tracks the fixed-point at a rate $\mathcal{O}(1/t)$, whereas discounted weighting yields a non-vanishing fixed-point tracking floor controlled by the discount factor. In both cases, decentralization induces an additional non-zero bias floor under a constant step size. We validate our theoretical findings through numerical simulations.

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eess.SP 2026-05-08 2 theorems

TTD homomorphism yields low-memory split-beam dictionary

Homomorphic Directional Beamforming with Analog True Time Delay Arrays

The structure approximates frequency-dependent patterns for multiple users with far smaller tables and uniform gains than optimization orLUT

abstract click to expand

Recently, true-time-delay (TTD) arrays, also referred to as joint phase-time arrays (JPTA), have been investigated for low-cost frequency-dependent beamforming capabilities to enable various applications, including beam-squint correction, fast beam training, and serving multiple user equipment (UE)s by frequency band to direction mapping, termed as split beampatterns. Several heuristics and optimization-based solutions have been proposed to determine TTD array parameters settings. However, they have practical limitations due to either computationally demanding optimization procedures, requirements for extremely large memory look-up tables, or degradations due to the beam-squint effect. In this article, we propose a novel split-beampattern generation algorithm based on the observed homomorphism between TTD array configuration matrices and corresponding beampatterns. First, we rigorously analyze the beampattern synthesis process and demonstrate the observed homomorphism and mathematical structure. Then, we propose the Homomorphic Directional Beamforming (HDB) algorithm to approximate the desired split beampatterns by utilizing a generator beampattern dictionary that requires a dictionary size orders of magnitude lower than existing approaches without ignoring the beam squint. With extensive simulations, we show that the proposed algorithm can provide a practical implementation with low memory and low computational cost requirements. In addition, HDB design provides close to uniform beamforming gains among UEs in different subbands, enabling fairness in power allocation.

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eess.SP 2026-05-08 2 theorems

Fluid antennas cut aggregation errors in wireless federated learning

Performance Analysis of Fluid Antenna-Assisted Over-the-Air Federated Learning Under Spatially Correlated Fading

Dynamic port selection yields closed-form improvements in outage probability and active-user count over fixed antennas under correlated fade

abstract click to expand

Fluid antenna (FA) technology has recently emerged as an effective means of exploiting spatial diversity through position-domain reconfigurability. This paper investigates the integration of FA into over-the-air federated learning (OTA-FL) systems with the aim of improving aggregation reliability and user participation under realistic channel conditions. By dynamically selecting antenna positions, FA-equipped users can exploit additional spatial degrees of freedom to realize more favorable channel conditions, thereby increasing the probability of successful contribution to the OTA aggregation process in each communication round. We consider an uplink OTA-FL framework consisting of a single fixed-antenna access point and multiple FA-enabled users operating over spatially correlated fading channels. Unlike existing studies that primarily rely on optimization-based designs or numerical evaluations, we develop a tractable analytical framework that enables a rigorous performance characterization of FA-assisted OTA-FL. In particular, closed-form expressions are derived for the aggregation error outage probability and the expected number of participating users per round. Spatial channel correlation across FA ports is modeled using a copula-based approach, where the Clayton copula is adopted to capture lower-tail dependence relevant to worst-case fading conditions. Numerical results validate the analytical findings and demonstrate that FA-assisted OTA-FL significantly outperforms conventional fixed-antenna schemes in terms of aggregation reliability and participation efficiency, while providing insights under practical system considerations.

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eess.SP 2026-05-08

MIMO CSI predictor uses 26% fewer parameters at -13.84 dB accuracy

Resource-Efficient CSI Prediction: A Gated Fusion and Factorized Projection Approach

Gated fusion and factorized projection enable 2.3 times faster inference for short-horizon predictions on standard simulated channels.

abstract click to expand

Accurate Channel State Information (CSI) prediction is essential for dynamic multiple-input multiple-output (MIMO) systems but remains computationally demanding. This letter proposes a resource-efficient predictor that combines a gated recurrent unit (GRU) encoder with Luong attention, a bottleneck gated fusion module, and a Dimension-wise Separable Linear Head (DSLH). The gated fusion module integrates local recurrent features with global attention context, while the DSLH reduces the cost of the output mapping. Evaluated on 3GPP TR 38.901-compliant channels, the proposed model achieves an average NMSE of -13.84 dB with 26% fewer parameters and approximately 2.3x higher inference throughput than a dimension-matched LinFormer baseline. The proposed model is best suited to LOS and mixed-condition scenarios, offering a practical accuracy-efficiency trade-off for short-horizon CSI prediction at moderate sequence lengths.

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eess.SP 2026-05-08

Hybrid antenna cuts outage by combining pinching and fluid movement

Hybrid Pinching-Fluid Antenna Assisted Wireless Communications: Modeling and Performance Analysis

Transmitter adjusts radiation point for LoS while receiver varies position for diversity, beating either approach alone

abstract click to expand

Reconfigurable-antenna systems have received increasing attention for their ability to adapt wireless channels. However, existing architectures exhibit scenario-dependent limitations: fluid antennas provide strong diversity gains in rich-scattering environments but offer limited benefits under line-of-sight (LoS)-dominant conditions, while pinching antennas can effectively reduce path loss by adjusting the radiation point along a waveguide, yet perform poorly in severe non-LoS (NLoS) scenarios. This letter proposes a hybrid pinching-fluid antenna system (HPFAS), where pinching antenna (PA) is employed at the transmitter and a fluid antenna (FA) is used at the receiver to jointly exploit LoS enhancement and spatial diversity. A tractable channel model is developed, and outage probability expressions are derived for both single-user and multi-user scenarios. Simulation results validate the analysis and show that the proposed HPFAS consistently outperforms systems using only pinching antennas or only fluid antennas under various propagation conditions.

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eess.SP 2026-05-08 Recognition

Hybrid pinching-fluid antennas cut wireless outages across conditions

Hybrid Pinching-Fluid Antenna Assisted Wireless Communications: Modeling and Performance Analysis

Pairing a transmitter pinching element with a receiver fluid element exploits both path-loss reduction and spatial diversity for lower error

abstract click to expand

Reconfigurable-antenna systems have received increasing attention for their ability to adapt wireless channels. However, existing architectures exhibit scenario-dependent limitations: fluid antennas provide strong diversity gains in rich-scattering environments but offer limited benefits under line-of-sight (LoS)-dominant conditions, while pinching antennas can effectively reduce path loss by adjusting the radiation point along a waveguide, yet perform poorly in severe non-LoS (NLoS) scenarios. This letter proposes a hybrid pinching-fluid antenna system (HPFAS), where pinching antenna (PA) is employed at the transmitter and a fluid antenna (FA) is used at the receiver to jointly exploit LoS enhancement and spatial diversity. A tractable channel model is developed, and outage probability expressions are derived for both single-user and multi-user scenarios. Simulation results validate the analysis and show that the proposed HPFAS consistently outperforms systems using only pinching antennas or only fluid antennas under various propagation conditions.

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eess.SP 2026-05-08

Frame-level splits inflate intrinsic decomposition scores by 1.6-2 dB

The frame-level leakage trap: rethinking evaluation protocols for intrinsic image decomposition, with source-separable uncertainty as a case study

Scene-level splits eliminate leakage between similar frames and yield more reliable benchmarks on MPI Sintel.

abstract click to expand

Evaluation protocols for learned intrinsic image decomposition on MPI Sintel have been inconsistent. Several prior works split the dataset by frames, which allows spatially similar frames of the same scene to appear in both train and test partitions. We quantify this leakage effect for the first time, across three architectures: a frame-level split inflates test R_PSNR by 1.6 to 2.0 dB (p less than 0.01 for all three, paired t-test across 3 seeds) relative to a scene-level split, confirming an architecture-independent protocol effect. A three-point gradient (random/temporal/scene) shows the gap is continuous, and under extended training the frame-level inflation exceeds 10 dB. We advocate scene-level splits as the community standard and provide reference numbers for six representative models under this protocol. As a case study within the corrected protocol, we present a physics-informed decomposition I = R composed with S + N with a source-separable three-way heteroscedastic uncertainty head. We empirically verify channel specialization: the non-Lambertian uncertainty channel shows r = 0.67 cross-correlation with non-Lambertian residual error, more than 4 times the texture channel's correlation. We further demonstrate downstream utility: filtering out the 75% highest-uncertainty pixels reduces reconstruction MSE by 77% on retained pixels, whereas random filtering produces no improvement. The specialization also holds on out-of-distribution real photographs. We report negative results for a more elaborate variant combining frequency decomposition, cross-task supervision, evidential learning, contrastive loss, and test-time adaptation. Our method reaches 15.98 plus or minus 0.41 dB R_PSNR, within 0.8 dB of a 5-member Deep Ensemble at one-fifth the cost, with the unique capability of source-separated uncertainty.

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eess.SP 2026-05-08

ISAC localization hits 45 cm accuracy in six iterations for cluttered IIoT

Cooperative Multi-Static Target Localization for ISAC in Cluttered Industrial IoT Networks

Clutter learning and adaptive sensor selection fuse measurements to beat benchmarks with limited resources.

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In this paper, we propose a novel integrated sensing and communications (ISAC) framework for collaborative multi-static target localization in dense Industrial Internet-of-Things (IIoT) environments in the presence of environmental clutter. We first develop a lightweight temporal clutter-suppression learning method to mitigate persistent reflections. Building on this, we propose an iterative localization algorithm that integrates two key components introduced in this work: a sampling-based field-of-view-aware initialization (SFI) scheme and an empirical position error bound (PEB) scheme, which together adaptively identify the most informative subset of sensing nodes. A reliability-aware weighted least-squares estimator is then employed to fuse range and angle-of-arrival measurements from the selected sensing receivers for target localization. Numerical results demonstrate rapid convergence of the proposed method, reducing the localization RMSE by nearly two orders of magnitude within six sensing iterations to about 45 cm, while significantly outperforming all considered benchmarks under the same sensing-resource budget.

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eess.SP 2026-05-08

Hybrid method jointly estimates target count and DoAs

Orthogonal Least Squares with Integrated Information Theoretic Criteria for Joint Number of Targets and DoA Estimation

Folding ITC penalties into greedy OLS steps improves accuracy over variants and baselines in simulations

abstract click to expand

We address the joint estimation of the number of targets and their direction-of-arrivals (DoAs) using antenna arrays. Target-number estimation can be formulated as a model-order selection problem and solved with the information theoretic criteria (ITC). The ITC minimize an objective function that balances a likelihood term and a complexity penalty. However, direct application of the ITC requires maximum-likelihood DoA estimates for each candidate model order, which is computationally prohibitive because it entails a multidimensional search over all angle combinations. To reduce complexity, many radar processing exploit greedy methods such as orthogonal least squares (OLS). In this paper, we explore three distinct methods to integrate the ITC model-order selection into the OLS estimation procedure for joint target-number and DoA estimation. Specifically, we propose the disjoint rank-based, the joint selection-based, and the hybrid rank-and-selection-based ITC-OLS algorithms. Each algorithm is derived under both the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) frameworks. Numerical simulations show that the proposed hybrid ITC-OLS algorithm consistently outperforms both the other proposed variants and a baseline method from the literature.

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eess.SP 2026-05-08

Hybrid BD-RIS matches SNR with fewer amplifiers

A Family of Hybrid Beyond-Diagonal RIS Architectures: Design and Performance Analysis

Partitioning the surface into active and passive group-connected sections delivers equal or better signal strength while reducing the number

abstract click to expand

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) extend conventional diagonal RISs by allowing inter-element coupling, thereby enlarging the set of attainable scattering matrices and improving the achievable signal-to-noise ratio (SNR). On the other hand, hybrid active/passive RISs use reflect-type power amplifiers in a fraction of the elements to alleviate the multiplicative path loss. In this paper, we bring these two ideas together and introduce a \emph{family of hybrid BD-RIS architectures}, in which the surface is partitioned into two reflecting subsurfaces (RSs), each adopting either a passive or an active group-connected BD-RIS design. We derive a closed-form SNR-maximizing solution that combines, for every BD-RIS group, Takagi's factorization of a certain complex symmetric matrix with an optimal per-group amplification factor that satisfies the reflect-power budget. Three architectures within the proposed family (active/passive, fully-connected-active/sub-connected-active, and sub-connected-active/sub-connected-active hybrid BD-RIS) are studied. Numerical results in a single-input single-output (SISO) link with blocked direct path show that the proposed hybrid BD-RIS architectures attain the same or higher receive SNR than their diagonal counterparts while using significantly fewer reflect-type amplifiers.

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eess.SP 2026-05-08

End-to-end scoring of GNSS covariance yields credible urban fixes

CredibleDFGO: Differentiable Factor Graph Optimization with Credibility Supervision

A weighting network and differentiable solver let proper scoring rules supervise posterior uncertainty directly, cutting both error and mis-

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Global navigation satellite system (GNSS) positioning is widely used for urban navigation, but the covariance reported by the GNSS solver is often unreliable in urban canyons. Existing differentiable factor graph optimization (DFGO) methods already learn measurement weighting through the solver, but they still use position-only objectives. As a result, the mean estimate may improve while the reported covariance remains too small, too large, or wrong in shape. In this work, we propose CredibleDFGO (CDFGO), a differentiable GNSS factor graph framework that makes covariance credibility an explicit training target. The Weighting Generation Network (WGN) predicts per-satellite reliability weights. The differentiable Gauss--Newton solver maps these weights to a position estimate and posterior covariance, and proper scoring rules supervise the East--North predictive distribution end-to-end. We study negative log-likelihood (NLL), Energy Score (ES), and their combination. Results on three UrbanNav test scenes show consistent gains in uncertainty credibility. Positioning accuracy also improves on the medium-urban and harsh-urban scenes, and the mean horizontal error and 95th-percentile error improve on the deep-urban scene. On the harsh-urban Mong Kok (MK) scene, CDFGO-Combined reduces the mean horizontal error from 13.77\,m to 11.68\,m, reduces NLL from 40.63 to 6.59, and reduces ES from 12.31 to 9.05. The case studies link the MK improvement to better axis-wise consistency, more credible local covariance ellipses, and satellite-level reweighting.

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