WiSER introduces a transmitter-conditioned sparse 3D scene encoder queried by a ray-corridor decoder for radiomaps and a DETR-style set decoder for variable-cardinality CIR taps, trained on co-registered ScanNet++ and Sionna data.
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Sionna: An open-source library for next-generation physical layer research
29 Pith papers cite this work. Polarity classification is still indexing.
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A framework combining VQ-VAE, task-relevance scoring, DRL subset selection, and a learned semantic-aware constellation claims near-100% semantic protection probability and proves standard Gray-coded constellations are suboptimal under non-uniform importance.
BiSplat-WRF applies 2D planar Gaussians rendered on angular domains plus a bilinear spatial transformer to capture electromagnetic interactions, outperforming prior NeRF and GS methods on SSIM for wireless radiance field reconstruction.
An LLM-powered agentic framework autonomously designs competitive and sometimes superior explainable algorithms for wireless PHY and MAC layer tasks.
A Conditional Diffusion Transformer recovers full MIMO-OFDM channels from sparse noisy pilots, delivering over 5 dB gain versus baselines even at 1/32 pilot density and completing inference in 10 steps.
A self-supervised multimodal alignment step plus equivariant GNN-based MARL yields over twofold sensing accuracy and 50% performance gains in decentralized V2I rate maximization.
Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
RxGS delivers a single unified 3DGS model for RF synthesis that generalizes across seen and unseen receivers by freezing shared geometry and conditioning directional radiance on receiver position.
Proposes Topological Resilience Index (TRI) via persistent homology to quantify resilience of deep learning OFDM receivers to channel shifts, claiming superior warning lead and BER reduction in simulations across ITU-R transitions.
Far-field power measurements enable inference of near-field user locations via location-dependent leakage, with a Bayesian CRLB and two estimators evaluated under LoS and multipath conditions.
Plan2Cleanse frames RL backdoor detection as a Monte Carlo planning problem to achieve over 61 percentage point gains in trigger detection and improved win rates in competitive environments.
ILCP transfers 128-byte compressed latent context over the Xn interface to eliminate post-handover cold start in GNN-based handover decisions, reporting 0.0% ping-pong rate and +5.1 pp accuracy gain on Vienna drive-test data.
A submodular optimization algorithm called IA-SPA with realistic ray-tracing on urban 3D maps achieves approximately 2x mean data rate and 2-8x edge rate gains over existing base station placements.
Diffusion-OAMP combines a pre-trained diffusion model with the OAMP algorithm under an SNR-matching rule to enable training-free reconstruction of compressed images transmitted over noisy wireless channels.
A joint clustering and prediction method for QoS distributions in vehicular cellular networks reduces mean absolute error by 9-27% compared to local or global baselines by adapting clusters to network changes.
SP-CCI augments conformal calibration sets with synthetic counterfactual labels and uses RCPS with PPI debiasing to achieve tighter prediction intervals while preserving marginal coverage guarantees.
A CNN modulator jointly trained with a neural receiver spreads information across local time-frequency neighborhoods in OFDM, breaking QAM rotational symmetry to support sparse or zero pilots under high Doppler.
A low-overhead DDST framework with mix transmission and ViT-based receiver shows performance gains in low-to-medium SNR under time-varying channels.
The paper presents GENESIS, an agentic AI framework for autonomous 6G RAN synthesis, research, and testing that converts intents into over-the-air validated solutions via composable primitives and a knowledge layer.
A meta-reinforcement learning agent achieves 80.1% success in localizing RF emitters by sequentially sensing the environment with a 2x2 patch antenna in Sionna ray-tracing simulations.
M-CVST aligns video context with MIMO subcarriers via a correlation map and applies recursive time-correlated sampling to boost semantic video performance over multi-path channels.
A differentially private equilibrium-seeking algorithm for OTA MIMO-based energy sharing protects prosumers' private data while converging to near-optimal solutions with quantified accuracy loss.
Proposes a dual task-oriented and generative semantic comms framework using scene graphs, ST-GNNs, and diffusion decoders over 3GPP vehicular channels, claiming 99.1% compression and improved FID scores.
ARIADNE uses online RL with SIONNA for MCS selection in digital twins, claiming up to 20% spectral efficiency gains over SOTA and divergence from OLLA behavior.
citing papers explorer
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WiSER: A Wireless Scene Encoder for Geometry-Grounded Multi-View Wireless Prediction
WiSER introduces a transmitter-conditioned sparse 3D scene encoder queried by a ray-corridor decoder for radiomaps and a DETR-style set decoder for variable-cardinality CIR taps, trained on co-registered ScanNet++ and Sionna data.
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Not All Symbols Are Equal: Importance-Aware Constellation Design for Semantic Communication
A framework combining VQ-VAE, task-relevance scoring, DRL subset selection, and a learned semantic-aware constellation claims near-100% semantic protection probability and proves standard Gray-coded constellations are suboptimal under non-uniform importance.
-
Planar Gaussian Splatting with Bilinear Spatial Transformer for Wireless Radiance Field Reconstruction
BiSplat-WRF applies 2D planar Gaussians rendered on angular domains plus a bilinear spatial transformer to capture electromagnetic interactions, outperforming prior NeRF and GS methods on SSIM for wireless radiance field reconstruction.
-
The AI Telco Engineer: Toward Autonomous Discovery of Wireless Communications Algorithms
An LLM-powered agentic framework autonomously designs competitive and sometimes superior explainable algorithms for wireless PHY and MAC layer tasks.
-
Diffusion Inpainting MIMO-OFDM Channels with Limited Noisy Observations
A Conditional Diffusion Transformer recovers full MIMO-OFDM channels from sparse noisy pilots, delivering over 5 dB gain versus baselines even at 1/32 pilot density and completing inference in 10 steps.
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Equivariant Multi-agent Reinforcement Learning for Multimodal Vehicle-to-Infrastructure Systems
A self-supervised multimodal alignment step plus equivariant GNN-based MARL yields over twofold sensing accuracy and 50% performance gains in decentralized V2I rate maximization.
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Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
-
RxGS: Receiver-Generalizable 3D Gaussian Splatting for Radio-Frequency Data Synthesis
RxGS delivers a single unified 3DGS model for RF synthesis that generalizes across seen and unseen receivers by freezing shared geometry and conditioning directional radiance on receiver position.
-
Resilience Characterization of AI-Native Wireless Receivers via Persistent Homology
Proposes Topological Resilience Index (TRI) via persistent homology to quantify resilience of deep learning OFDM receivers to channel shifts, claiming superior warning lead and BER reduction in simulations across ITU-R transitions.
-
Near-Field User Location Inference From Far-Field Power Measurements
Far-field power measurements enable inference of near-field user locations via location-dependent leakage, with a Bayesian CRLB and two estimators evaluated under LoS and multipath conditions.
-
Plan2Cleanse: Test-Time Backdoor Defense via Monte-Carlo Planning in Deep Reinforcement Learning
Plan2Cleanse frames RL backdoor detection as a Monte Carlo planning problem to achieve over 61 percentage point gains in trigger detection and improved win rates in competitive environments.
-
Inductive Latent Context Persistence: Closing the Post-Handover Cold Start in 6G Radio Access Networks
ILCP transfers 128-byte compressed latent context over the Xn interface to eliminate post-handover cold start in GNN-based handover decisions, reporting 0.0% ping-pong rate and +5.1 pp accuracy gain on Vienna drive-test data.
-
Optimal Transmitter Placement in Realistic Urban Environments
A submodular optimization algorithm called IA-SPA with realistic ray-tracing on urban 3D maps achieves approximately 2x mean data rate and 2-8x edge rate gains over existing base station placements.
-
Diffusion-OAMP for Joint Image Compression and Wireless Transmission
Diffusion-OAMP combines a pre-trained diffusion model with the OAMP algorithm under an SNR-matching rule to enable training-free reconstruction of compressed images transmitted over noisy wireless channels.
-
Joint Clustering and Prediction of the Quality of Service in Vehicular Cellular Networks
A joint clustering and prediction method for QoS distributions in vehicular cellular networks reduces mean absolute error by 9-27% compared to local or global baselines by adapting clusters to network changes.
-
Synthetic Counterfactual Labels for Efficient Conformal Counterfactual Inference
SP-CCI augments conformal calibration sets with synthetic counterfactual labels and uses RCPS with PPI debiasing to achieve tighter prediction intervals while preserving marginal coverage guarantees.
-
Deep-OFDM: Neural Modulation for High Mobility
A CNN modulator jointly trained with a neural receiver spreads information across local time-frequency neighborhoods in OFDM, breaking QAM rotational symmetry to support sparse or zero pilots under high Doppler.
-
Low-Overhead Receiver Design for Data-Dependent Superimposed Training via Deep Learning
A low-overhead DDST framework with mix transmission and ViT-based receiver shows performance gains in low-to-medium SNR under time-varying channels.
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GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing
The paper presents GENESIS, an agentic AI framework for autonomous 6G RAN synthesis, research, and testing that converts intents into over-the-air validated solutions via composable primitives and a knowledge layer.
-
Active Sensing with Meta-Reinforcement Learning for Emitter Localization from RF Observations
A meta-reinforcement learning agent achieves 80.1% success in localizing RF emitters by sequentially sensing the environment with a 2x2 patch antenna in Sionna ray-tracing simulations.
-
Contextual Wireless Video Semantic Communication in MIMO-OFDM Systems
M-CVST aligns video context with MIMO subcarriers via a correlation map and applies recursive time-correlated sampling to boost semantic video performance over multi-path channels.
-
Mechanism and Communication Co-Design for Differentially Private Energy Sharing
A differentially private equilibrium-seeking algorithm for OTA MIMO-based energy sharing protects prosumers' private data while converging to near-optimal solutions with quantified accuracy loss.
-
Towards a Joint Task-Oriented and Generative Semantic Communication Framework for 6G Networks
Proposes a dual task-oriented and generative semantic comms framework using scene graphs, ST-GNNs, and diffusion decoders over 3GPP vehicular channels, claiming 99.1% compression and improved FID scores.
-
ARIADNE: AI-RAN Informed Link Adaptation in Digital Twin Network Environments
ARIADNE uses online RL with SIONNA for MCS selection in digital twins, claiming up to 20% spectral efficiency gains over SOTA and divergence from OLLA behavior.
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Automated Heuristic Design for Network Operations
Automated heuristic design is applied to network operations with an implementation for 5G decoding that produces LDPC heuristics on par with state-of-the-art production solutions.
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NF-TrackLLM: Joint Prediction of UAV Trajectory and Near-Field Beam for LAE XL-MIMO Systems
NF-TrackLLM is a multi-modal GPT-2-based framework that first predicts UAV trajectories then uses them as priors for near-field beam prediction in XL-MIMO systems.
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Against the Monolithic Wireless World Model: Why NextG Needs Composable and Agentic Intelligence
Argues that wireless data's configuration dependence and lack of self-containment make monolithic foundation models unsuitable for AI-native 6G, favoring instead composable agentic architectures.
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When Does a Neural Receiver Help? Calibration-Drift Benchmarking and Detect-and-Rollback for 5G/6G NR
Neural receivers outperform MMSE in-distribution but their robustness to calibration drift is poorly characterized; the work provides benchmarking and a detect-and-rollback approach for 5G/6G NR.
- Advancing Network Digital Twin Framework for Generating Realistic Datasets