AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges

Guangjin Pan; Shugong Xu; Wenjun Yu; Xiaoyu Yang; Xinyu Guo; Yilin Gao; Yuan Gao; Zhiyong Zhong

REVIEW 3 major objections 5 minor 1 cited by

AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This paper claims that AI-driven cellular positioning is now a coherent field best understood through the 3GPP taxonomy of AI/ML-assisted versus direct AI/ML positioning, and that a CNN baseline beats Transformer, LSTM, and FCNN on both…

desk verdict Useful 3GPP-centered survey with a credible taxonomy; its own benchmark is undermined by spatial leakage in the data split. read the letter →

arxiv 2501.14970 v2 pith:AS5EDH6V submitted 2025-01-24 eess.SP cs.AIcs.LG

Guangjin Pan , Yuan Gao , Yilin Gao , Wenjun Yu , Zhiyong Zhong , Xiaoyu Yang , Xinyu Guo , Shugong Xu This is my paper · ORCID

classification eess.SPcs.AIcs.LG

keywords wirelesspositioningAI-drivencellular3GPPAI/MLstandardsAI/ML-assisteddirectchannelchartingfingerprint-basedlocalizationdatasets

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Wireless positioning is shifting from geometric models to machine learning, and this paper tries to give researchers a reliable map of that shift: the fundamentals of radio positioning, the AI models applied to it, the 3GPP standards that now govern AI/ML positioning, the state of the art in both assisted and direct AI approaches, and the datasets available for training and testing. The organizing claim is that 3GPP's distinction between AI/ML-assisted positioning (AI refines measurements such as LOS/NLOS, TOA/TDOA, and angles, which feed a conventional solver) and direct AI/ML positioning (AI maps signals straight to coordinates via fingerprinting, knowledge-assisted learning, or channel charting) covers the field. The paper's own experiment compares FCNN, CNN, LSTM, and Transformer on the MaMIMO and DeepMIMO datasets and reports that CNN ranks first on both, with median errors of 33.1 mm on MaMIMO ULA-LOS and 1.0 m on DeepMIMO O1. If the survey is right, it gives practitioners a common vocabulary and a baseline comparison to build on; what the field still lacks, the paper argues, are datasets with mobility, Doppler, and multi-modal data, and a unified evaluation benchmark.

What carries the argument

The central organizing object is the 3GPP AI/ML positioning taxonomy from the Release 18 study: AI/ML-assisted positioning (models output refined measurements or probabilities such as LOS/NLOS indicators, TOA/TDOA, or AOA/AOD that feed a conventional positioning solver) versus direct AI/ML positioning (models consume raw channel observations and output the UE location directly). Around this, the paper builds its review around the lifecycle management (LCM) framework—data collection, model training, management, inference, and storage—and the five deployment cases (model on UE, gNB, or LMF) that determine communication overhead and scalability. The other load-bearing mechanism is the case-study comparison of FCNN, ResNet-32 CNN, LSTM, and Transformer on MaMIMO and DeepMIMO, which is what grounds the survey's claims about model selection. The channel model formula $H_s = \sum_{l=1}^L \alpha_l \mathbf{a}(\theta_l,\varphi_l)e^{-j2\pi f_s \tau_l}$ defines the CSI object that nearly every surveyed method uses as input.

What would settle it

Running the released code with the stated data split and hyperparameters and checking whether the median errors reproduce 141.2 mm, 33.1 mm, 51.8 mm, and 40.9 mm on MaMIMO and 13.1 m, 1.0 m, 1.7 m (not mm), and 1.2 m on DeepMIMO; a mismatch in the LSTM DeepMIMO value, or in the CNN/Transformer ordering under multiple random seeds, would falsify the paper's ranking. Separately, recovering the Google Scholar search behind Fig. 1 would determine whether the 'more than half of positioning papers use AI by 2023' statistic is reproducible.

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Extended reading notes

Core claim

The central claim is that AI-driven cellular positioning has matured into an identifiable technical area with a 3GPP-defined structure, and that current research divides into two classes: AI/ML-assisted positioning, where models improve LOS/NLOS detection, TOA/TDOA estimation, or angle prediction and hand those refined measurements to a conventional geometry-based location estimator, and direct AI/ML positioning, where models learn the signal-to-position mapping end to end through fingerprinting, knowledge-assisted learning, or channel charting. The paper argues that model-based positioning struggles under multipath, NLOS, hardware impairment, and database aging, while AI models learn those patterns from data, and that 3GPP Release 18 has begun standardizing this through a lifecycle-management framework and five deployment cases spanning UE, gNB, and LMF. Its original quantitative contribution is a comparison of four backbones on one measured and one simulated dataset (MaMIMO ULA-LOS and DeepMIMO O1 at 20 MHz, 100 subcarriers, 64-element ULA): median errors on MaMIMO of 141.2 mm (FCNN), 33.1 mm (CNN), 51.8 mm (LSTM), and 40.9 mm (Transformer), and on DeepMIMO of 13.1 m, 1.0 m, 1.7 mm (written in the text, though the figure indicates 1.7 m), and 1.2 m, giving the ordering CNN, Transformer, LSTM, FCNN. The survey concludes that datasets are the bottleneck: none of the reviewed public datasets combines mobility, Doppler, multi-band, and multi-modal sensing, so it calls for standardized benchmarks and extensible simulators.

Load-bearing premise

The load-bearing premise is that the Sec VII.B case study—four architectures, one measured and one simulated dataset, a single random 7000/1000/2000 split, and fixed bandwidth and array settings—is a fair and representative basis for the paper's model ranking, and that the reported median errors are accurate, an assumption the DeepMIMO LSTM '1.7 mm' value already violates inside the paper.

Editorial extensions

If this is right

A common vocabulary: the AI/ML-assisted versus direct AI/ML split, tied to 3GPP deployment cases, gives researchers and operators a standard way to classify positioning algorithms across papers and releases.
On the two tested datasets, CNN (ResNet-32) is the best default backbone for small-data, single-BS, static-channel positioning; Transformer needs more data or pretraining to catch up.
Simulated ray-tracing data (DeepMIMO) preserves the relative ranking of models found on real measurements (MaMIMO), but absolute errors differ by orders of magnitude, so synthetic benchmark numbers should not be treated as achievable field accuracy.
The biggest bottleneck identified is data: until datasets include user mobility, Doppler, multi-band signals, and vision/IMU modalities, AI positioning models cannot be fairly evaluated for dynamic 6G scenarios.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

I would not bet the CNN-over-Transformer ranking on a single split: the paper does not report seed variance or hyperparameter tuning, so the ordering may shift under repeated runs; that is my inference, not the paper's claim.
The '1.7 mm' LSTM value deserves a policy response: the field needs a convention that error units are stated explicitly and that tables and CDF figures are cross-checked, because unit typos of this kind make model comparisons meaningless.
A natural next experiment the paper does not run is to pretrain a Transformer on unlabeled CSI from the same scenarios (the LWLM-style self-supervised approach the paper reviews) and then compare against ResNet CNN at the same label budget; given the paper's 'Transformers need more data' explanation, this would directly test whether the ranking persists.
If the 3GPP taxonomy is as complete as the survey claims, future standards work should define evaluation metrics that penalize communication overhead and on-device compute, not just accuracy, because Cases 2b and 3b trade overhead for accuracy.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a solid, standard survey of AI-driven cellular positioning, organized around the 3GPP R18 taxonomy. Its real value is as a reference map. The standards recap (deployment cases, KPIs, lifecycle management) is accurate and useful, and the SOTA tables give a newcomer a fast route into the literature. The dataset comparison table is also handy, and the authors actually shipped code for the small benchmark, which is more than most surveys do.

The genuinely new artifact is the Sec. VII.B case study comparing FCNN, CNN, LSTM, and Transformer on MaMIMO and DeepMIMO. That is where the soft spots are. The random 7000/1000/2000 split on dense, regularly spaced grids is a real problem. MaMIMO positions are CNC-controlled with sub-millimeter precision over a 1.25 m square; DeepMIMO O1 is a dense street-level grid. Randomly assigning samples from the same or neighboring grid positions to train and test means the test set measures interpolation, not generalization. The reported ranking (CNN > Transformer > LSTM > FCNN) is likely optimistic and could change under a spatially disjoint split. There are no error bars, no multiple seeds, and no hyperparameter detail. The LSTM median on DeepMIMO is reported as '1.7 mm' against meters for everything else, which has to be a units typo. These are fixable, but they matter because the paper uses this benchmark to draw a general lesson about model choice.

Two smaller inconsistencies: Table XV labels xG-Loc 'No' for open source while the text calls it an open dataset, and Fig. 1's Google Scholar methodology is not described, so the 'more than half of papers use AI by 2023' claim is hard to verify.

None of this destroys the survey's descriptive value. The classification into AI/ML-assisted vs. direct AI/ML positioning, the LOS/NLOS, TOA/TDOA, angle, fingerprinting, and channel charting summaries, and the 3GPP discussion are competent and mostly accurate. The citation coverage looks broad and appropriate, with no sign of promotional self-citation. It is not a groundbreaking paper, but it does not need to be.

Who benefits: graduate students and engineers entering the field, and anyone needing a compact R18-oriented map. It deserves a serious referee, but a referee should insist on fixing the split protocol, adding seeds and error bars, correcting the typo, and reconciling the xG-Loc entry.

Referee Report

3 major / 5 minor

Summary. This manuscript is a survey of AI-driven cellular positioning. It reviews the fundamentals of wireless positioning and of mainstream AI models, traces the evolution of 3GPP positioning standards with emphasis on Release 18 AI/ML positioning and the five deployment cases, and organizes the state of the art into AI/ML-assisted positioning (LOS/NLOS detection, TOA/TDOA estimation, angle estimation) and direct AI/ML positioning (fingerprinting, knowledge-assisted learning, channel charting). It also compares public datasets and presents a case study on MaMIMO and DeepMIMO that benchmarks FCNN, CNN, LSTM, and Transformer models, with the stated finding that CNNs perform most consistently while Transformers require more data. The paper's central claim is that it provides a comprehensive, up-to-date, 3GPP-structured reference map of the field.

Significance. If the survey's descriptive content is accurate, it fills a useful niche: it is one of the few surveys that couples the 3GPP AI/ML positioning taxonomy with a systematic review of both assisted and direct AI/ML methods, and it releases open-source evaluation code for its case study. The dataset comparison table and the summary of 3GPP deployment cases (Case 1--3b) are potentially valuable reference material for researchers entering the area. However, the paper's only original quantitative contribution, the case study in Sec. VII.B, has methodological flaws that directly affect the model-ranking claims drawn in Secs. VII.B and VII.D. The descriptive survey parts are broadly sound, but the empirical section needs substantial revision before the manuscript can be accepted.

major comments (3)

[Sec. VII.B] The case study uses a single random 7000/1000/2000 split on spatially dense measurement grids: MaMIMO positions lie on a 1.25 m x 1.25 m grid with sub-millimeter CNC-controlled accuracy, and DeepMIMO O1 UEs are densely spaced along street-level grid points. A random split assigns samples from identical or adjacent grid positions to both training and test sets, so the test CSI can be nearly identical to a training CSI. This spatial leakage makes the reported median errors (33.1 mm on MaMIMO, 1.0 m on DeepMIMO for CNN) optimistic and potentially changes the ranking under a spatially disjoint split. Please report results with a spatially disjoint split (e.g., train and test on separated grid regions or with a minimum position separation), multiple seeds, and confidence intervals or per-seed spreads.
[Sec. VII.B, Fig. 11] The LSTM median error on DeepMIMO is reported as 1.7 mm, which is physically implausible for the configuration used (20 MHz bandwidth, 100 subcarriers, 64-element ULA in an urban street scenario) and contradicts both the CDF in Fig. 11 and the other models' errors in the same setting. This is almost certainly a units typo (1.7 m), but the presence of such an error in the paper's only quantitative table undermines confidence in the remaining reported numbers. Please correct the value and audit the other entries for consistency with the figures.
[Secs. VII.B and VII.D] The general conclusions that 'CNNs perform consistently well' and 'Transformers require more data' are supported only by a single configuration (fixed 20 MHz, 100 subcarriers, 64-element ULA, one data split, one scenario per dataset) with no hyperparameter details, no model-size or training-budget matching, and no statistical significance testing. These claims are therefore not established by the presented evidence. Please either soften the conclusions to be explicitly configuration-specific or add the missing experimental controls and uncertainty quantification.

minor comments (5)

[Sec. VII.A, Table XV] The text states that xG-Loc is 'an open dataset' and 'open source', but Table XV lists xG-Loc's 'Open Source Type' as 'No'. Please reconcile this inconsistency.
[Sec. I, Fig. 1] The figure and the associated claim that more than half of wireless positioning papers use AI by 2023 are based on Google Scholar data, but no search strings, inclusion criteria, or validation methodology are described. Adding a brief methodology note would improve reproducibility.
[Sec. III.A.4] The Transformer complexity expression uses the notation d_LSTM for the feature dimension; this should be d_Trans or another symbol to avoid confusion with LSTM dimensions.
[Sec. V.B, Table VIII] In the text and table, 'V AE' appears with an extra space; also 'angle-delay cannel power matrix' should be 'channel power matrix' (ADCPM). Please fix these typos.
[Throughout] There are several spacing and formatting issues (e.g., 'UA Vs', 'ttoa' in the TOA equation, and 'V oronoi' in Sec. VI.A.2) that should be corrected in a final pass. The reference list also contains some garbled author names (e.g., 'V . D' in [95]) that need cleanup.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey taxonomy is externally attributed to 3GPP, and the case study is an empirical evaluation on public datasets with released code.

full rationale

This is a survey paper whose organizational skeleton is explicitly attributed to an external standard body: the two-class taxonomy (AI/ML-assisted versus direct AI/ML positioning) is said to be 'Guided by the 3GPP-defined taxonomy' and the deployment cases (Cases 1-3b) are presented as 3GPP Release 18 definitions, so the paper's categorization is not defined in terms of its own conclusions. The only original quantitative contribution is the Sec. VII.B case study, which trains four standard backbones (FCNN, CNN/ResNet-32, LSTM, Transformer) on the public MaMIMO and DeepMIMO datasets under a fixed 7000/1000/2000 random split with identical channel parameters, and reports held-out test median errors. Nothing is fitted to the reported numbers and then re-derived: the resulting ranking (CNN > Transformer > LSTM > FCNN) is an empirical outcome of that held-out evaluation rather than a 'prediction' forced by construction, and the code is released for reproducibility. The in-text survey summaries and SOTA tables cite external literature; at most one supporting citation (the multi-frequency CSI compression work of co-author X. Yang, refs. [29]/[163]) is a descriptive, non-load-bearing reference to externally published and falsifiable work, not a load-bearing self-citation chain, and no 'uniqueness theorem' or ansatz is imported from the authors' prior work. The noted weaknesses are methodological rather than circular: the single random split over dense spatial grids may leak spatial information (a generalization-validity concern), the DeepMIMO LSTM '1.7 mm' result is internally inconsistent with the figure and the other metrics (a units/consistency typo), and the Fig. 1 Google Scholar trend lacks a described counting methodology (a verifiability gap). None of these involve an equation or defined quantity reducing to its own input, so the paper incurs no circularity under the stated criteria.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper is a survey, so it introduces no free parameters and no invented entities. It relies on standard channel and AI assumptions from the literature, and on one paper-specific benchmark design, which are listed below.

assumptions (5)

standard math Cramer-Rao lower bounds for TOA and angle estimation (Sec II.B.1, Eq 10) are valid for the stated signal model.
Used to justify accuracy limits of model-based estimators; quoted from prior literature without derivation.
domain assumption The channel frequency response model Hs = sum_l alpha_l a(theta_l, phi_l) exp(-j2 pi fs tau_l) (Eq 2) represents the MIMO-OFDM positioning signal.
The entire problem formulation (Eq 6-7) and the discussion of CSI-based AI positioning rests on this sparse multipath model from prior work.
domain assumption 3GPP CDL channel model (TR 38.901) is the appropriate benchmark standard, so datasets conforming to it (e.g., xG-Loc) are treated as more suitable.
Sec VII.A.1 argues CDL-based datasets are more suitable for link- and system-level simulation, which is a normative choice for the field.
domain assumption Neural networks can learn the mapping from high-dimensional CSI to position (Eq 6-7).
The motivation for all AI-based positioning methods reviewed in the paper relies on this learnability assumption.
ad hoc to paper The benchmark configuration in Sec VII.B (ULA-LOS MaMIMO subset, DeepMIMO O1, fixed 20 MHz, 100 subcarriers, 64-element ULA, single random split) is a fair and representative comparison.
Chosen for convenience and not justified beyond stating that both datasets are fixed to the same parameters; it is the paper's own design.

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Cite this review

Pith. "Pith review of AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges." pith.science (2026). https://pith.science/paper/AS5EDH6V

@misc{pith2026250114970,
  author       = {Pith},
  title        = {Pith review of: AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AS5EDH6V}},
  note         = {Machine review of arXiv:2501.14970}
}

read the original abstract

Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), unmanned aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. Driven by the requirements and functionalities defined in the 3rd Generation Partnership Project (3GPP) standards, AI/machine learning (ML)-based cellular positioning is becoming a key technology to overcome the limitations of traditional methods. This paper presents a comprehensive survey of AI-driven cellular positioning. We begin by reviewing the fundamentals of wireless positioning and AI models, analyzing their respective challenges and synergies. We provide a comprehensive review of the evolution of 3GPP positioning standards, with a focus on the integration of AI/ML in current and upcoming standard releases. Guided by the 3GPP-defined taxonomy, we categorize and summarize state-of-the-art (SOTA) research into two major classes: AI/ML-assisted positioning and direct AI/ML-based positioning. The former includes line-of-sight (LOS)/non-line-of-sight (NLOS) detection, time of arrival (TOA)/time difference of arrival (TDOA) estimation, and angle prediction; the latter encompasses fingerprinting, knowledge-assisted learning, and channel charting. Furthermore, we review representative public datasets and conduct performance evaluations of AI-based positioning algorithms using these datasets. Finally, we conclude by summarizing the challenges and opportunities of AI-driven wireless positioning.

Figures

Figures reproduced from arXiv: 2501.14970 by the authors.

**Figure 2.** The overall structure of this paper. in positioning tasks. In contrast, our work focuses specifically on different types of positioning algorithms (such as AI/MLassisted positioning and direct AI/ML positioning methods), and discusses how AI can enhance each of these categories. For wireless positioning standards, numerous studies have reviewed the progress of 3GPP standards [1], [16], [20], [21]. Nonetheless, the … view at source ↗

**Figure 3.** MIMO-OFDM-based wireless positioning systems. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: Schematic diagram of positioning technology based on TOA, TDOA, angle and fingerprint. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]

**Figure 5.** Figure 5: Schematic diagram of channel charting. The relative proximity in the [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: Schematic diagram of AI/ML model LCM. the computational complexity in tera operations per second (TOPS), floating-point operations per second (FLOPS), and multiplication-accumulation operations (MACs), and the potential difference between actual complexity and evaluat…

**Figure 7.** Figure 7: Schematic diagram of AI/ML positioning cases and categories. [PITH_FULL_IMAGE:figures/full_fig_p015_7.png]

**Figure 8.** Figure 8: Dataset generation process of DeepMIMO. It enables a wide range of tasks, including distance estimation, angle estimation, position estimation, and wireless channel quality evaluation. 2) MaMIMO: MaMIMO [349] is an open-access indoor CSI dataset, providing 252,004 high…

**Figure 9.** Figure 9: Dataset generation process of the ViWi dataset. [PITH_FULL_IMAGE:figures/full_fig_p026_9.png]

**Figure 11.** Figure 11: Localization performance comparison using the DeepMIMO ‘O1’ [PITH_FULL_IMAGE:figures/full_fig_p026_11.png]

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Forward citations

Cited by 1 Pith paper

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Works this paper leans on

300 extracted references · 78 canonical work pages · cited by 1 Pith paper

[1]

Survey of cellular mobile radio localization methods: From 1g to 5G,

J. A. del Peral-Rosado, R. Raulefs et al. , “Survey of cellular mobile radio localization methods: From 1g to 5G,” IEEE Commun. Surv. Tutorials, vol. 20, no. 2, pp. 1124–1148, 2018

2018
[2]

A survey of indoor localization systems and technologies,

R. C. Shit, S. Sharma et al., “A survey of indoor localization systems and technologies,” IEEE Commun. Surv. Tutorials , vol. 21, no. 3, pp. 2568–2599, 2019

2019
[3]

Efficient processing of deep neural networks: A tutorial and survey,

V . Sze, Y .-H. Chenet al., “Efficient processing of deep neural networks: A tutorial and survey,” Proc. IEEE, vol. 105, no. 12, pp. 2295–2329, 2017

2017
[4]

Less data, more knowledge: Building next-generation semantic communication networks,

C. Chaccour, W. Saad et al. , “Less data, more knowledge: Building next-generation semantic communication networks,” IEEE Commun. Surv. Tutorials, vol. 27, no. 1, pp. 37–76, 2025

2025
[5]

A survey on xai for 5g and beyond security: Technical aspects, challenges and research directions,

T. Senevirathna, V . H. La et al., “A survey on xai for 5g and beyond security: Technical aspects, challenges and research directions,” IEEE Commun. Surv. Tutorials, vol. 27, no. 2, pp. 941–973, 2025

2025
[6]

A survey on TOA based wireless localization and NLOS mitigation techniques,

J. A. del Peral-Rosado, R. Raulefs et al. , “A survey on TOA based wireless localization and NLOS mitigation techniques,”IEEE Commun. Surv. Tutorials, vol. 11, no. 3, pp. 107–124, 2009

2009
[7]

Wi-fi fingerprint-based indoor posi- tioning: Recent advances and comparisons,

A. Shastri, N. Valecha et al. , “Wi-fi fingerprint-based indoor posi- tioning: Recent advances and comparisons,” IEEE Commun. Surv. Tutorials, vol. 18, no. 1, pp. 466–490, 2016

2016
[8]

Modern WLAN fingerprinting indoor positioning methods and deployment challenges,

A. Khalajmehrabadi, N. Gatsis et al. , “Modern WLAN fingerprinting indoor positioning methods and deployment challenges,” IEEE Com- mun. Surv. Tutorials, vol. 19, no. 3, pp. 1974–2002, 2017

1974

Show all 300 references

[9]

Jammer localization in multi-hop wireless network: A comprehensive survey,

X. Wei, Q. Wang et al. , “Jammer localization in multi-hop wireless network: A comprehensive survey,” IEEE Commun. Surv. Tutorials , vol. 19, no. 2, pp. 765–799, 2017

2017
[10]

A survey of enabling technologies for network localization, tracking, and navigation,

C. Laoudias, A. Moreira et al., “A survey of enabling technologies for network localization, tracking, and navigation,” IEEE Commun. Surv. Tutorials, vol. 20, no. 4, pp. 3607–3644, 2018. IEEE XXXX XXXXX, VOL. X, NO. X, JANUARY 2025 31

2018
[11]

Indoor positioning technologies without offline fingerprinting map: A survey,

B. Jang and H. Kim, “Indoor positioning technologies without offline fingerprinting map: A survey,” IEEE Commun. Surv. Tutorials, vol. 21, no. 1, pp. 508–525, 2019

2019
[12]

Ubiquitous localization (UbiLoc): A survey and taxonomy on device free localization for smart world,

R. C. Shit, S. Sharma et al. , “Ubiquitous localization (UbiLoc): A survey and taxonomy on device free localization for smart world,” IEEE Commun. Surv. Tutorials , vol. 21, no. 4, pp. 3532–3564, 2019

2019
[13]

A survey on fusion-based indoor positioning,

X. Guo, N. Ansari et al., “A survey on fusion-based indoor positioning,” IEEE Commun. Surv. Tutorials , vol. 22, no. 1, pp. 566–594, 2020

2020
[14]

Indoor intelligent fingerprint-based localization: Principles, approaches and challenges,

X. Zhu, W. Qu et al., “Indoor intelligent fingerprint-based localization: Principles, approaches and challenges,” IEEE Commun. Surv. Tutorials, vol. 22, no. 4, pp. 2634–2657, 2020

2020
[15]

A comprehensive survey of machine learning based localization with wireless signals,

D. Burghal, A. T. Ravi et al. , “A comprehensive survey of machine learning based localization with wireless signals,” 2020

2020
[16]

D2D-based cooperative positioning paradigm for future wireless systems: A survey,

N. Chukhno, S. Trilles et al. , “D2D-based cooperative positioning paradigm for future wireless systems: A survey,” IEEE Sensors J. , vol. 22, no. 6, pp. 5101–5112, 2022

2022
[17]

A tutorial on terahertz-band localization for 6G communication systems,

H. Chen, H. Sarieddeen et al., “A tutorial on terahertz-band localization for 6G communication systems,” IEEE Commun. Surv. Tutorials , vol. 24, no. 3, pp. 1780–1815, 2022

2022
[18]

A review of millimeter wave device-based localization and device-free sensing technologies and applications,

A. Shastri, N. Valecha et al., “A review of millimeter wave device-based localization and device-free sensing technologies and applications,” IEEE Commun. Surv. Tutorials , vol. 24, no. 3, pp. 1708–1749, 2022

2022
[19]

Reconfigurable intelligent surfaces for 6G IoT wireless positioning: A contemporary survey,

R. Chen, M. Liu et al., “Reconfigurable intelligent surfaces for 6G IoT wireless positioning: A contemporary survey,” IEEE Internet Things J., vol. 9, no. 23, pp. 23 570–23 582, 2022

2022
[20]

Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook,

S. E. Trevlakis, A.-A. A. Boulogeorgos et al. , “Localization as a key enabler of 6G wireless systems: A comprehensive survey and an outlook,” IEEE Open J. Commun. Soc. , vol. 4, 2023

2023
[21]

A tutorial on 5G positioning,

L. Italiano, B. C. Tedeschini et al. , “A tutorial on 5G positioning,” IEEE Commun. Surv. Tutorials , pp. 1–1, 2024

2024
[22]

On the ground and in the sky: A tutorial on radio localization in ground-air-space networks,

H. Sallouha, S. Saleh et al., “On the ground and in the sky: A tutorial on radio localization in ground-air-space networks,” IEEE Commun. Surv. Tutorials, pp. 1–1, 2024

2024
[23]

Positioning using wireless networks: Appli- cations, recent progress and future challenges,

Y . Yang, M. Chen et al., “Positioning using wireless networks: Appli- cations, recent progress and future challenges,” IEEE J. Select. Areas Commun., pp. 1–1, 2024

2024
[24]

Fundamentals of RIS-aided localization in the far-field,

D.-R. Emenonye, H. S. Dhillon et al. , “Fundamentals of RIS-aided localization in the far-field,” IEEE Trans. Wireless Commun. , vol. 23, no. 4, pp. 3408–3424, 2024

2024
[25]

Flexible-position MIMO for wireless com- munications: Fundamentals, challenges, and future directions,

J. Zheng, J. Zhang et al., “Flexible-position MIMO for wireless com- munications: Fundamentals, challenges, and future directions,” IEEE Wireless Commun., vol. 31, no. 5, pp. 18–26, 2024

2024
[26]

NTN-based 6G localization: Vision, role of LEOs, and open problems,

H. K. Dureppagari, C. Saha et al., “NTN-based 6G localization: Vision, role of LEOs, and open problems,” IEEE Wireless Commun. , vol. 30, no. 6, pp. 44–51, 2023

2023
[27]

6G localization and sensing in the near field: Features, opportunities, and challenges,

H. Chen, M. F. Keskin et al. , “6G localization and sensing in the near field: Features, opportunities, and challenges,” IEEE Wireless Commun., vol. 31, no. 4, pp. 260–267, 2024

2024
[28]

Fingerprint-based 3D hierarchical localization for cell-free massive MIMO systems,

R. Liu, Z. Wan et al., “Fingerprint-based 3D hierarchical localization for cell-free massive MIMO systems,” IEEE Trans. Veh. Technol. , vol. 73, no. 6, pp. 7980–7993, 2024

2024
[29]

Multi-frequency based CSI compression for vehicle localization in intelligent transportation system,

X. Yang, M. Gao et al., “Multi-frequency based CSI compression for vehicle localization in intelligent transportation system,” IEEE Trans. Intell. Transport. Syst. , vol. 25, no. 3, pp. 2719–2732, 2024

2024
[30]

Robust sparse direct localization of smart vehicle with partly calibrated time modulated arrays,

Y . Wang, M. S. Obaidat et al. , “Robust sparse direct localization of smart vehicle with partly calibrated time modulated arrays,” IEEE Trans. Intell. Transport. Syst., vol. 24, no. 11, pp. 12 582–12 596, 2023

2023
[31]

Joint active and passive beamforming for vehicle localization with reconfigurable intelligent surfaces,

Z. Feng, B. Wang et al. , “Joint active and passive beamforming for vehicle localization with reconfigurable intelligent surfaces,” IEEE Trans. Intell. Transport. Syst., vol. 25, no. 11, pp. 16 704–16 718, 2024

2024
[32]

Management of positioning functions in cellular networks for time-sensitive transportation applications,

Q. Liu, R. Liu et al., “Management of positioning functions in cellular networks for time-sensitive transportation applications,” IEEE Trans. Intell. Transport. Syst. , vol. 24, no. 11, pp. 13 260–13 275, 2023

2023
[33]

IRS-aided high-accuracy positioning for autonomous driving toward 6G: A tutorial,

Y . Zhu, B. Mao et al. , “IRS-aided high-accuracy positioning for autonomous driving toward 6G: A tutorial,”IEEE Vehicular Technology Magazine, vol. 19, no. 1, pp. 85–92, 2024

2024
[34]

Data-driven indoor positioning correction for infrastructure-enabled autonomous driving systems: A lifelong framework,

C. Zhao, A. Song et al. , “Data-driven indoor positioning correction for infrastructure-enabled autonomous driving systems: A lifelong framework,” IEEE Trans. Intell. Transport. Syst. , vol. 24, no. 4, pp. 3908–3921, 2023

2023
[35]

On scalable network communication for infrastructure-vehicle collaborative autonomous driving,

S. Yang, H. H. F. Yin et al., “On scalable network communication for infrastructure-vehicle collaborative autonomous driving,” IEEE Open J. Veh. Technol., vol. 4, pp. 310–324, 2023

2023
[36]

Location-dependent augmented reality services in wireless edge-enabled metaverse systems,

H. Zhang, S. Mao et al. , “Location-dependent augmented reality services in wireless edge-enabled metaverse systems,” IEEE Open J. Commun. Soc., vol. 4, pp. 171–183, 2023

2023
[37]

Federated echo state learning for mini- mizing breaks in presence in wireless virtual reality networks,

M. Chen, O. Semiari et al. , “Federated echo state learning for mini- mizing breaks in presence in wireless virtual reality networks,” IEEE Trans. Wireless Commun., vol. 19, no. 1, pp. 177–191, 2020

2020
[38]

Location-based augmented reality with pervasive smartphone sensors: Inside and beyond Pokemon Go!

R. Shea, D. Fu et al., “Location-based augmented reality with pervasive smartphone sensors: Inside and beyond Pokemon Go!” IEEE Access , vol. 5, pp. 9619–9631, 2017

2017
[39]

Virtual wireless device-constrained robust extended kalman filters for smartphone positioning in indoor corridor environment,

G. Guo, K. Yan et al. , “Virtual wireless device-constrained robust extended kalman filters for smartphone positioning in indoor corridor environment,” IEEE Sensors J. , vol. 23, no. 3, pp. 2815–2822, 2023

2023
[40]

Indoorwaze: A crowdsourcing-based context- aware indoor navigation system,

T. Li, D. Han et al. , “Indoorwaze: A crowdsourcing-based context- aware indoor navigation system,” IEEE Trans. Wireless Commun. , vol. 19, no. 8, pp. 5461–5472, 2020

2020
[41]

Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device,

A. W. S. Au, C. Feng et al. , “Indoor tracking and navigation using received signal strength and compressive sensing on a mobile device,” IEEE Trans. on Mobile Comput., vol. 12, no. 10, pp. 2050–2062, 2013

2013
[42]

Indoor positioning in 5G- advanced: Challenges and solution toward centimeter-level accuracy with carrier phase enhancements,

J. Nikonowicz, A. Mahmood et al. , “Indoor positioning in 5G- advanced: Challenges and solution toward centimeter-level accuracy with carrier phase enhancements,” IEEE Wireless Commun. , vol. 31, no. 4, pp. 268–275, 2024

2024
[43]

Survey of wireless communication technologies for public safety,

G. Baldini, S. Karanasios et al. , “Survey of wireless communication technologies for public safety,” IEEE Commun. Surv. Tutorials, vol. 16, no. 2, pp. 619–641, 2014

2014
[44]

HELPS for emergency location service: Hyper-enhanced local positioning system,

H. Moon, H. Park et al. , “HELPS for emergency location service: Hyper-enhanced local positioning system,” IEEE Wireless Commun. , vol. 31, no. 4, pp. 276–282, 2024

2024
[45]

5G new radio for public safety mission critical communications,

J. Li, K. K. Nagalapur et al., “5G new radio for public safety mission critical communications,” IEEE Communications Standards Magazine , vol. 6, no. 4, pp. 48–55, 2022

2022
[46]

A survey on indoor positioning systems for IoT-based applications,

P. S. Farahsari, A. Farahzadi et al. , “A survey on indoor positioning systems for IoT-based applications,” IEEE Internet Things J. , vol. 9, no. 10, pp. 7680–7699, 2022

2022
[47]

Challenges and solutions of surveillance systems in IoT-enabled smart campus: A survey,

T. Anagnostopoulos, P. Kostakos et al. , “Challenges and solutions of surveillance systems in IoT-enabled smart campus: A survey,” IEEE Access, vol. 9, pp. 131 926–131 954, 2021

2021
[48]

An internet of electronic-visual things indoor localization system using adaptive kalman filter,

P. Qin, Q. Hu et al. , “An internet of electronic-visual things indoor localization system using adaptive kalman filter,” IEEE Sensors J. , vol. 23, no. 14, pp. 16 058–16 067, 2023

2023
[49]

A survey on location and motion tracking technologies, methodologies and applications in precision sports,

J. Liu, G. Huang et al. , “A survey on location and motion tracking technologies, methodologies and applications in precision sports,” Expert Systems with Applications , vol. 229, p. 120492, 2023

2023
[50]

Performance analysis of multiple indoor positioning systems in a healthcare environment,

T. Van Haute, E. De Poorter et al., “Performance analysis of multiple indoor positioning systems in a healthcare environment,” International journal of health geographics , vol. 15, pp. 1–15, 2016

2016
[51]

RFDOA-Net: An efficient ConvNet for RF-based DOA estimation in UA V surveillance systems,

R. Akter, V .-S. Doan et al. , “RFDOA-Net: An efficient ConvNet for RF-based DOA estimation in UA V surveillance systems,” IEEE Trans. Veh. Technol., vol. 70, no. 11, pp. 12 209–12 214, 2021

2021
[52]

Uav positioning based on multi-sensor fusion,

J. Peng, P. Zhang et al., “Uav positioning based on multi-sensor fusion,” IEEE Access, vol. 8, pp. 34 455–34 467, 2020

2020
[53]

A review of indoor posi- tioning systems for uav localization with machine learning algorithms,

C. Sandamini, M. W. P. Maduranga et al. , “A review of indoor posi- tioning systems for uav localization with machine learning algorithms,” Electronics, vol. 12, no. 7, p. 1533, 2023

2023
[54]

T. S. Rappaport, Wireless communications: principles and practice . Cambridge University Press, 2024

2024
[55]

Improved Wi-Fi RSSI measurement for indoor localization,

W. Xue, W. Qiu et al., “Improved Wi-Fi RSSI measurement for indoor localization,” IEEE Sensors J. , vol. 17, no. 7, pp. 2224–2230, 2017

2017
[56]

TS 36.213, Release 18, Mar

Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures, 3GPP Std. TS 36.213, Release 18, Mar. 2024

2024
[57]

A survey on wireless position estimation,

S. Gezici, “A survey on wireless position estimation,” Wireless personal communications, vol. 44, pp. 263–282, 2008

2008
[58]

Spotfi: Decimeter level localization using wifi,

M. Kotaru, K. Joshi et al., “Spotfi: Decimeter level localization using wifi,” in Proc. ACM SIGCOMM’2015, 2015, pp. 269–282

2015
[59]

CSI-based indoor localization,

K. Wu, J. Xiao et al. , “CSI-based indoor localization,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 7, pp. 1300–1309, 2013

2013
[60]

CSI-based fingerprinting for indoor localiza- tion: A deep learning approach,

X. Wang, L. Gao et al., “CSI-based fingerprinting for indoor localiza- tion: A deep learning approach,” IEEE Trans. Veh. Technol. , vol. 66, no. 1, pp. 763–776, 2017

2017
[61]

Learning to localize: A 3D CNN approach to user positioning in massive MIMO-OFDM systems,

C. Wu, X. Yi et al., “Learning to localize: A 3D CNN approach to user positioning in massive MIMO-OFDM systems,” IEEE Trans. Wireless Commun., vol. 20, no. 7, pp. 4556–4570, 2021

2021
[62]

Best practice in RSS measurements and ranging,

A. Zanella, “Best practice in RSS measurements and ranging,” IEEE Commun. Surv. Tutorials, vol. 18, no. 4, pp. 2662–2686, 2016

2016
[63]

Outdoor localization based on rss ranging aided by pedestrian dead reckoning in GPS restricted scenario,

J.-M. Choi, K. Kim et al., “Outdoor localization based on rss ranging aided by pedestrian dead reckoning in GPS restricted scenario,” inProc. IEEE APWCS’2022, 2022, pp. 186–190

2022
[64]

On the hybrid TOA/RSS range estimation in wireless sensor networks,

A. Coluccia and A. Fascista, “On the hybrid TOA/RSS range estimation in wireless sensor networks,” IEEE Trans. Wireless Commun., vol. 17, no. 1, pp. 361–371, 2018. IEEE XXXX XXXXX, VOL. X, NO. X, JANUARY 2025 32

2018
[65]

RSS-based ranging by multichannel RSS averaging,

A. Zanella and A. Bardella, “RSS-based ranging by multichannel RSS averaging,” IEEE Wireless Commun. Lett. , vol. 3, no. 1, pp. 10–13, 2014

2014
[66]

AI-enabled fingerprinting and crowdsource-based vehicle localization for resilient and safe transporta- tion systems,

R. C. Shit, S. Sharma et al. , “AI-enabled fingerprinting and crowdsource-based vehicle localization for resilient and safe transporta- tion systems,” IEEE Trans. Intell. Transport. Syst. , vol. 22, no. 7, pp. 4660–4669, 2021

2021
[67]

Enhanced TOA estimation through tap candidate extraction for positioning in multipath channels,

I. Sobron, I. Landa et al. , “Enhanced TOA estimation through tap candidate extraction for positioning in multipath channels,” in Proc. PIMRC’2019, 2019, pp. 1–6

2019
[68]

Carrier phase ranging for indoor positioning with 5G NR signals,

L. Chen, X. Zhou et al., “Carrier phase ranging for indoor positioning with 5G NR signals,” IEEE Internet Things J. , vol. 9, no. 13, pp. 10 908–10 919, 2022

2022
[69]

Single-anchor two-way local- ization bounds for 5G mmwave systems,

Z. Abu-Shaban, H. Wymeersch et al. , “Single-anchor two-way local- ization bounds for 5G mmwave systems,” IEEE Trans. Veh. Technol., vol. 69, no. 6, pp. 6388–6400, 2020

2020
[70]

Three-dimensional positioning for LTE systems,

C.-Y . Chen and W.-R. Wu, “Three-dimensional positioning for LTE systems,” IEEE Trans. Veh. Technol. , vol. 66, no. 4, pp. 3220–3234, 2017

2017
[71]

Error analysis of the OTDOA from the resolved first arrival path in LTE,

S.-H. Kong and B. Kim, “Error analysis of the OTDOA from the resolved first arrival path in LTE,” IEEE Trans. Wireless Commun. , vol. 15, no. 10, pp. 6598–6610, 2016

2016
[72]

Super-resolution TOA estimation with diversity for indoor geolocation,

X. Li and K. Pahlavan, “Super-resolution TOA estimation with diversity for indoor geolocation,” IEEE Trans. Wireless Commun., vol. 3, no. 1, pp. 224–234, 2004

2004
[73]

An improved ESPRIT based time-of- arrival estimation algorithm for vehicular OFDM systems,

V . U. Prabhu and D. Jalihal, “An improved ESPRIT based time-of- arrival estimation algorithm for vehicular OFDM systems,” in Proc. VTC-Spring’2009, 2009, pp. 1–4

2009
[74]

A TOA-based localization algorithm with simultaneous NLOS mitigation and synchronization error elimination,

S. Wu, S. Zhang et al. , “A TOA-based localization algorithm with simultaneous NLOS mitigation and synchronization error elimination,” IEEE Sens. Lett. , vol. 3, no. 3, pp. 1–4, 2019

2019
[75]

Optimal two-way TOA localization and synchronization for moving user devices with clock drift,

S. Zhao, X.-P. Zhang et al., “Optimal two-way TOA localization and synchronization for moving user devices with clock drift,” IEEE Trans. Veh. Technol., vol. 70, no. 8, pp. 7778–7789, 2021

2021
[76]

Robust second-order cone relaxation for TW- TOA-based localization with clock imperfection,

S. Gao, S. Zhang et al., “Robust second-order cone relaxation for TW- TOA-based localization with clock imperfection,”IEEE Signal Process. Lett., vol. 23, no. 8, pp. 1047–1051, 2016

2016
[77]

Enhanced Wi-Fi RTT ranging: A sensor-aided learning approach,

J. Choi, “Enhanced Wi-Fi RTT ranging: A sensor-aided learning approach,” IEEE Trans. Veh. Technol., vol. 71, no. 4, pp. 4428–4437, 2022

2022
[78]

WhereArtThou: A WiFi-RTT-based indoor positioning system,

R. Jurdi, H. Chen et al. , “WhereArtThou: A WiFi-RTT-based indoor positioning system,” IEEE Access, vol. 12, pp. 41 084–41 101, 2024

2024
[79]

Wi-Fi RTT ranging performance characterization and positioning system design,

C. Ma, B. Wu et al., “Wi-Fi RTT ranging performance characterization and positioning system design,” IEEE Trans. on Mobile Comput. , vol. 21, no. 2, pp. 740–756, 2022

2022
[80]

A Wi-Fi RSS-RTT indoor positioning model based on dynamic model switching algorithm,

X. Feng, K. A. Nguyen et al., “A Wi-Fi RSS-RTT indoor positioning model based on dynamic model switching algorithm,” IEEE J. Ind. Sea. Pos. Nav., vol. 2, pp. 151–165, 2024

2024
[81]

TDOA localization with unknown signal propaga- tion speed and sensor position errors,

Y . Zou and H. Liu, “TDOA localization with unknown signal propaga- tion speed and sensor position errors,” IEEE Commun. Lett. , vol. 24, no. 5, pp. 1024–1027, 2020

2020
[82]

TDOA-based localization for semi-static targets in NLOS environments,

S. Li, M. Hedley et al. , “TDOA-based localization for semi-static targets in NLOS environments,” IEEE Wireless Commun. Lett. , vol. 4, no. 5, pp. 513–516, 2015

2015
[83]

Algorithms and bounds for distributed TDOA-based positioning using OFDM signals,

R. K. Martin, C. Yan et al. , “Algorithms and bounds for distributed TDOA-based positioning using OFDM signals,” IEEE Trans. Signal Process., vol. 59, no. 3, pp. 1255–1268, 2011

2011
[84]

Positioning for the internet of things: A 3GPP perspective,

X. Lin, J. Bergman et al. , “Positioning for the internet of things: A 3GPP perspective,” IEEE Commun. Mag., vol. 55, no. 12, pp. 179–185, 2017

2017
[85]

Generalized coprime array configura- tions for direction-of-arrival estimation,

S. Qin, Y . D. Zhang et al. , “Generalized coprime array configura- tions for direction-of-arrival estimation,” IEEE Trans. Signal Process. , vol. 63, no. 6, pp. 1377–1390, 2015

2015
[86]

Unified near-field and far-field localization for AOA and hybrid AOA-TDOA positionings,

Y . Wang and K. C. Ho, “Unified near-field and far-field localization for AOA and hybrid AOA-TDOA positionings,” IEEE Trans. Wireless Commun., vol. 17, no. 2, pp. 1242–1254, 2018

2018
[87]

AoA-based positioning for aerial intelligent reflecting surface-aided wireless communications: An angle-domain approach,

T. Zhou, K. Xu et al. , “AoA-based positioning for aerial intelligent reflecting surface-aided wireless communications: An angle-domain approach,” IEEE Wireless Commun. Lett. , vol. 11, no. 4, pp. 761–765, 2022

2022
[88]

5GNR indoor positioning by joint DL- TDoA and DL-AoD,

M. Ahadi and F. Kaltenberger, “5GNR indoor positioning by joint DL- TDoA and DL-AoD,” in Proc. WCNC’2023, 2023, pp. 1–6

2023
[89]

Angle-based positioning estimation leveraging diffuse scattering paths in millimeter-wave MIMO systems,

L. Guo, T. Lv et al. , “Angle-based positioning estimation leveraging diffuse scattering paths in millimeter-wave MIMO systems,” IEEE Sensors J., vol. 24, no. 24, pp. 41 597–41 609, 2024

2024
[90]

A 5G NR millimeter-wave-based AoD positioning field experiment,

J. Bao, S. Akkarakaran et al., “A 5G NR millimeter-wave-based AoD positioning field experiment,” in Proc. ICC Workshops’2024) , 2024, pp. 282–287

2024
[91]

Cramer–Rao bounds for hybrid TOA/DOA-based location estimation in sensor networks,

Y . Fu and Z. Tian, “Cramer–Rao bounds for hybrid TOA/DOA-based location estimation in sensor networks,” IEEE Signal Process. Lett. , vol. 16, no. 8, pp. 655–658, 2009

2009
[92]

Channel reconstruction-aided MUSIC algorithms for joint AoA&AoD estimation in MIMO systems,

T. Ma, Y . Xiaoet al., “Channel reconstruction-aided MUSIC algorithms for joint AoA&AoD estimation in MIMO systems,” IEEE Wireless Commun. Lett., vol. 12, no. 2, pp. 322–326, 2023

2023
[93]

Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC,

X. Zhang, L. Xu et al. , “Direction of departure (DOD) and direction of arrival (DOA) estimation in MIMO radar with reduced-dimension MUSIC,” IEEE Commun. Lett. , vol. 14, no. 12, pp. 1161–1163, 2010

2010
[94]

A generalized ESPRIT approach to direction-of-arrival estimation,

F. Gao and A. Gershman, “A generalized ESPRIT approach to direction-of-arrival estimation,” IEEE Signal Process. Lett. , vol. 12, no. 3, pp. 254–257, 2005

2005
[95]

Optimizing sensor array DOA esti- mation with the manifold reconstruction unitary ESPRIT algorithm,

V . D, K. S. Balamurugan et al. , “Optimizing sensor array DOA esti- mation with the manifold reconstruction unitary ESPRIT algorithm,” IEEE Sens. Lett. , vol. 7, no. 12, pp. 1–4, 2023

2023
[96]

A novel RSSI fingerprint positioning method based on virtual AP and convolutional neural network,

S. Wu, W. Huang et al., “A novel RSSI fingerprint positioning method based on virtual AP and convolutional neural network,” IEEE Sensors J., vol. 22, no. 7, pp. 6898–6909, 2022

2022
[97]

Smartphone-based indoor localization via network learning with fusion of FTM/RSSI measurements,

P. E. Numan, H. Park et al. , “Smartphone-based indoor localization via network learning with fusion of FTM/RSSI measurements,” IEEE Netw. Lett., vol. 5, no. 1, pp. 21–25, 2023

2023
[98]

Indoor localization with multi-beam of 5G new radio signals,

X. Zhou, L. Chen et al. , “Indoor localization with multi-beam of 5G new radio signals,” IEEE Trans. Wireless Commun. , 2024

2024
[99]

Localization with cellular signal rsrp fingerprint of multiband and multicell,

Z. Hu, X. Chen et al., “Localization with cellular signal rsrp fingerprint of multiband and multicell,” IEEE J. Select. Areas Commun. , vol. 42, no. 9, pp. 2380–2394, 2024

2024
[100]

DNN-based wireless positioning in an outdoor environment,

J.-Y . Lee, C. Eom et al. , “DNN-based wireless positioning in an outdoor environment,” in Proc. IEEE ICASSP’2018 , 2018, pp. 3799– 3803

2018
[101]

LTE user equipment RSRP difference elimina- tion method using multidimensional scaling for LTE fingerprint-based positioning system,

L. Ma, N. Jin et al. , “LTE user equipment RSRP difference elimina- tion method using multidimensional scaling for LTE fingerprint-based positioning system,” in Proc. IEEE ICC’2017 , 2017, pp. 1–6

2017
[102]

Scenario-agnostic localization system for cellular network based on feature engineering,

H. Q. Luo-Chen, E. J. Khatib et al. , “Scenario-agnostic localization system for cellular network based on feature engineering,” IEEE Open J. Commun. Soc. , vol. 5, pp. 4999–5012, 2024

2024
[103]

An HMM-based localization scheme using adap- tive forward algorithm for LTE networks,

D. Pei, J. Gong et al., “An HMM-based localization scheme using adap- tive forward algorithm for LTE networks,” in Proc. IEEE WCSP’2018, 2018, pp. 1–6

2018
[104]

An indoor passive positioning method using CSI fingerprint based on Adaboost,

Y . Zhang, D. Li et al. , “An indoor passive positioning method using CSI fingerprint based on Adaboost,” IEEE Sensors J. , vol. 19, no. 14, pp. 5792–5800, 2019

2019
[105]

A low-overhead indoor positioning system using CSI fingerprint based on transfer learning,

Y . Zhang, C. Wu et al. , “A low-overhead indoor positioning system using CSI fingerprint based on transfer learning,” IEEE Sensors J. , vol. 21, no. 16, pp. 18 156–18 165, 2021

2021
[106]

CSI fingerprinting localization with low human efforts,

X. Tong, Y . Wanet al., “CSI fingerprinting localization with low human efforts,” IEEE/ACM Trans. Networking , vol. 29, no. 1, pp. 372–385, 2021

2021
[107]

Indoor positioning with multibeam CSI from a single 5G base station,

X. Zhou, L. Chen et al., “Indoor positioning with multibeam CSI from a single 5G base station,” IEEE Sens. Lett., vol. 8, no. 1, pp. 1–4, 2024

2024
[108]

CSI amplitude fingerprinting-based nb-iot indoor localization,

Q. Song, S. Guo et al. , “CSI amplitude fingerprinting-based nb-iot indoor localization,” IEEE Internet Things J. , vol. 5, no. 3, pp. 1494– 1504, 2018

2018
[109]

Hybrid fingerprinting and ray extension localiza- tion in NLOS regions,

J. Li, I.-T. Lu et al., “Hybrid fingerprinting and ray extension localiza- tion in NLOS regions,” IEEE Trans. Intell. Transport. Syst. , vol. 23, no. 12, pp. 23 503–23 516, 2022

2022
[110]

LoRa ToA-based localization using fingerprint method,

G. Y . Ha, S. B. Seo et al. , “LoRa ToA-based localization using fingerprint method,” in Proc. IEEE ICTC’2019 , 2019, pp. 349–353

2019
[111]

Wireless channel charting: Theory, practice, and applications,

P. Ferrand, M. Guillaud et al. , “Wireless channel charting: Theory, practice, and applications,” IEEE Commun. Mag. , vol. 61, no. 6, pp. 124–130, 2023

2023
[112]

Multipoint channel charting for wireless networks,

J. Deng, S. Medjkouh et al., “Multipoint channel charting for wireless networks,” in Proc. IEEE ACSSC’2018, 2018, pp. 286–290

2018
[113]

Unsupervised charting of wireless channels,

S. Medjkouh, E. Gonultas et al. , “Unsupervised charting of wireless channels,” in Proc. IEEE GLOBECOM’2018 , 2018, pp. 1–7

2018
[114]

Improving channel charting with representation-constrained autoencoders,

P. Huang, O. CastaNeda et al. , “Improving channel charting with representation-constrained autoencoders,” in Proc. IEEE SPAWC’2019, 2019, pp. 1–5

2019
[115]

Optimizing multicarrier multi- antenna systems for LoS channel charting,

T. Yassine, L. L. Magoarou et al. , “Optimizing multicarrier multi- antenna systems for LoS channel charting,” IEEE Trans. Wireless Commun., vol. 23, no. 10, pp. 14 702–14 714, 2024

2024
[116]

SNR prediction in cellular systems based on channel charting,

P. Kazemi, H. Al-Tous et al., “SNR prediction in cellular systems based on channel charting,” in Proc. IEEE ComNet’2020 , 2020, pp. 1–8. IEEE XXXX XXXXX, VOL. X, NO. X, JANUARY 2025 33

2020
[117]

Channel charting aided pilot allo- cation in multi-cell massive MIMO mMTC networks,

L. Ribeiro, M. Leinonealn et al. , “Channel charting aided pilot allo- cation in multi-cell massive MIMO mMTC networks,” in Proc. IEEE SPAWC’2022, 2022, pp. 1–5

2022
[118]

Pilot assignment based on AoA information using channel charting in massive MIMO systems,

B. Shaikh, P. G. Burguera et al. , “Pilot assignment based on AoA information using channel charting in massive MIMO systems,” in Proc. IEEE SPAWC’2024, 2024, pp. 166–170

2024
[119]

Channel charting aided pilot reuse for massive MIMO systems with spatially correlated channels,

L. Ribeiro, M. Leinonen et al. , “Channel charting aided pilot reuse for massive MIMO systems with spatially correlated channels,” IEEE Open J. Commun. Soc. , vol. 3, pp. 2390–2406, 2022

2022
[120]

Channel charting assisted beam track- ing,

P. Kazemi, H. Al-Tous et al. , “Channel charting assisted beam track- ing,” in Proc. IEEE VTC-Spring’2022 , 2022, pp. 1–5

2022
[121]

Energy consumption constrained resource cell optimization based on multipoint channel charting,

Y . Ye, Z. Chen et al., “Energy consumption constrained resource cell optimization based on multipoint channel charting,” in Proc. IEEE WCSP’2023, 2023, pp. 856–861

2023
[122]

Channel charting: Locating users within the radio environment using channel state information,

C. Studer, S. Medjkouh et al., “Channel charting: Locating users within the radio environment using channel state information,” IEEE Access, vol. 6, pp. 47 682–47 698, 2018

2018
[123]

Multi-point channel charting for long range wireless networks: an experimental study,

H. Aghajari, M. N. Soorki et al. , “Multi-point channel charting for long range wireless networks: an experimental study,” in Proc. IEEE MeditCom’2023, 2023, pp. 399–405

2023
[124]

Absolute positioning with unsuper- vised multipoint channel charting for 5G networks,

J. Pihlajasalo, M. Koivisto et al., “Absolute positioning with unsuper- vised multipoint channel charting for 5G networks,” in Proc. IEEE VTC-Fall’2020), 2020, pp. 1–5

2020
[125]

Not-too-deep channel charting (n2d- CC),

P. Agostini, Z. Utkovski et al. , “Not-too-deep channel charting (n2d- CC),” in Proc. IEEE WCNC’2022 , 2022, pp. 2160–2165

2022
[126]

Federated learning for multipoint channel charting,

——, “Federated learning for multipoint channel charting,” in Proc. IEEE SPAWC’2022, 2022, pp. 1–5

2022
[127]

Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting,

T. Yassine, L. L. Magoarou et al., “Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting,” in Proc. IEEE SPAWC’2022, 2022, pp. 1–5

2022
[128]

Channel charting for streaming CSI data,

S. Taner, M. Guillaud et al., “Channel charting for streaming CSI data,” in Proc. IEEE ACSSC’2023, 2023, pp. 1648–1653

2023
[129]

Angle-delay profile-based and timestamp-aided dissimilarity metrics for channel charting,

P. Stephan, F. Euchner et al. , “Angle-delay profile-based and timestamp-aided dissimilarity metrics for channel charting,” IEEE Trans. Commun., vol. 72, no. 9, pp. 5611–5625, 2024

2024
[130]

Triplet-based wireless channel chart- ing: Architecture and experiments,

P. Ferrand, A. Decurninge et al., “Triplet-based wireless channel chart- ing: Architecture and experiments,” IEEE J. Select. Areas Commun. , vol. 39, no. 8, 2021

2021
[131]

Multi-site wireless channel charting through latent space alignment,

Y . Vindas and M. Guillaud, “Multi-site wireless channel charting through latent space alignment,” in Proc. IEEE SPAWC’2024 , 2024, pp. 826–830

2024
[132]

How soon will channel charting be inapplicable in user moving scenarios?: Some answers,

Z. Wang, Y . Xu et al., “How soon will channel charting be inapplicable in user moving scenarios?: Some answers,” in Proc. IEEE GLOBE- COM’2022, 2022, pp. 1350–1355

2022
[133]

Multipath estimating delay lock loop for LTE signal TOA estimation in indoor and urban environments,

P. Wang and Y . J. Morton, “Multipath estimating delay lock loop for LTE signal TOA estimation in indoor and urban environments,” IEEE Trans. Wireless Commun., vol. 19, no. 8, pp. 5518–5530, 2020

2020
[134]

On the impact of hardware impairments on RIS-aided localization,

C. Öztürk, M. F. Keskin et al., “On the impact of hardware impairments on RIS-aided localization,” in Proc. IEEE ICC’2022. IEEE, 2022, pp. 2846–2851

2022
[135]

RIS-aided joint channel estimation and localization at mmWave under hardware impairments: A dictionary learning-based approach,

M. Bayraktar, N. González-Prelcic et al. , “RIS-aided joint channel estimation and localization at mmWave under hardware impairments: A dictionary learning-based approach,” IEEE Trans. Wireless Commun. , pp. 1–16, 2024

2024
[136]

V oronoi tessellation based interpolation method for Wi-Fi radio map construction,

M. Lee and D. Han, “V oronoi tessellation based interpolation method for Wi-Fi radio map construction,” IEEE Commun. Lett., vol. 16, no. 3, pp. 404–407, 2012

2012
[137]

Fingerprint-based localization for massive MIMO-OFDM system with deep convolutional neural networks,

X. Sun, C. Wu et al. , “Fingerprint-based localization for massive MIMO-OFDM system with deep convolutional neural networks,” IEEE Trans. Veh. Technol., vol. 68, no. 11, pp. 10 846–10 857, 2019

2019
[138]

Single-site localization based on a new type of fingerprint for massive MIMO-OFDM systems,

X. Sun, X. Gao et al. , “Single-site localization based on a new type of fingerprint for massive MIMO-OFDM systems,” IEEE Trans. Veh. Technol., vol. 67, no. 7, pp. 6134–6145, 2018

2018
[139]

CSI-based fingerprinting for indoor localiza- tion: A deep learning approach,

X. Wang, L. Gao et al., “CSI-based fingerprinting for indoor localiza- tion: A deep learning approach,” IEEE Trans. Veh. Technol. , vol. 66, no. 1, pp. 763–776, 2016

2016
[140]

Channel state identification in complex indoor environments with ST-CNN and transfer learning,

Z. Sun, K. Wang et al., “Channel state identification in complex indoor environments with ST-CNN and transfer learning,” IEEE Commun. Lett., vol. 27, no. 2, pp. 546–550, 2023

2023
[141]

Self-calibrating indoor localization with crowdsourcing fingerprints and transfer learning,

C. Xiang, S. Zhang et al. , “Self-calibrating indoor localization with crowdsourcing fingerprints and transfer learning,” in Proc. IEEE ICC’2021, 2021, pp. 1–6

2021
[142]

Updating radio maps without pain: An enhanced transfer learning approach,

J. Yang, X. Zhao et al. , “Updating radio maps without pain: An enhanced transfer learning approach,” IEEE Internet Things J. , vol. 8, no. 13, pp. 10 693–10 705, 2021

2021
[143]

RME-GAN: A learning framework for radio map estimation based on conditional generative adversarial network,

S. Zhang, A. Wijesinghe et al. , “RME-GAN: A learning framework for radio map estimation based on conditional generative adversarial network,” IEEE Internet Things J. , no. 20, pp. 18 016–18 027, 2023

2023
[144]

3D radio map reconstruction based on generative adversarial networks under constrained aircraft trajectories,

T. Hu, Y . Huang et al. , “3D radio map reconstruction based on generative adversarial networks under constrained aircraft trajectories,” IEEE Trans. Veh. Technol., vol. 72, no. 6, pp. 8250–8255, 2023

2023
[145]

A novel weighted KNN algorithm based on RSS similarity and position distance for Wi-Fi fingerprint positioning,

B. Wang, X. Gan et al., “A novel weighted KNN algorithm based on RSS similarity and position distance for Wi-Fi fingerprint positioning,” IEEE Access, vol. 8, pp. 30 591–30 602, 2020

2020
[146]

Advanced deep learning models for 6G: overview, opportunities and challenges,

L. Jiao, Y . Shao et al. , “Advanced deep learning models for 6G: overview, opportunities and challenges,” IEEE Access, 2024

2024
[147]

Mlp-mixer: An all-mlp architecture for vision,

I. O. Tolstikhin, N. Houlsby et al., “Mlp-mixer: An all-mlp architecture for vision,” Proc. NIPS’2021, vol. 34, pp. 24 261–24 272, 2021

2021
[148]

S2-MLP: Spatial-shift MLP architecture for vision,

T. Yu, X. Li et al., “S2-MLP: Spatial-shift MLP architecture for vision,” in Proc. IEEE/CVF WACV’2022, 2022, pp. 297–306

2022
[149]

Applying deep neural network (dnn) for robust indoor localization in multi-building environment,

A. B. Adege, H.-P. Lin et al. , “Applying deep neural network (dnn) for robust indoor localization in multi-building environment,” Applied Sciences, vol. 8, no. 7, p. 1062, 2018

2018
[150]

Deep learning based OTDOA positioning for NB-IoT communication systems,

G. Pan, T. Wang et al. , “Deep learning based OTDOA positioning for NB-IoT communication systems,” in Proc. IEEE Mobimedia’2020, 2020

2020
[151]

Deep residual learning for image recognition,

K. He, X. Zhang et al., “Deep residual learning for image recognition,” in Proc. CVPR’2016, 2016, pp. 770–778

2016
[152]

Imagenet classification with deep convolutional neural networks,

A. Krizhevsky, I. Sutskever et al. , “Imagenet classification with deep convolutional neural networks,” in Proc. NIPS’2012, vol. 25, 2012

2012
[153]

A novel three-dimensional direction-of-arrival estimation approach using a deep convolutional neural network,

C. M. Mylonakis and Z. D. Zaharis, “A novel three-dimensional direction-of-arrival estimation approach using a deep convolutional neural network,” IEEE Open J. Veh. Technol. , vol. 5, pp. 643–657, 2024

2024
[154]

High accurate time-of-arrival estimation with fine-grained feature generation for internet-of-things applications,

G. Pan, T. Wang et al. , “High accurate time-of-arrival estimation with fine-grained feature generation for internet-of-things applications,” IEEE Wireless Commun. Lett. , vol. 9, no. 11, pp. 1980–1984, 2020

1980
[155]

A lightweight CIR-based CNN with MLP for NLOS/LOS identification in a UWB positioning system,

M. Si, Y . Wang et al. , “A lightweight CIR-based CNN with MLP for NLOS/LOS identification in a UWB positioning system,” IEEE Commun. Lett., vol. 27, no. 5, pp. 1332–1336, 2023

2023
[156]

Automatic indoor radio map construction and localization via multipath fingerprint extrapolation,

Q. Li, X. Liao et al. , “Automatic indoor radio map construction and localization via multipath fingerprint extrapolation,” IEEE Trans. Wireless Commun., vol. 22, no. 9, pp. 5814–5827, 2023

2023
[157]

RadioUNet: Fast radio map estimation with convolutional neural networks,

R. Levie, Ç. Yapar et al. , “RadioUNet: Fast radio map estimation with convolutional neural networks,” IEEE Trans. Wireless Commun. , vol. 20, no. 6, pp. 4001–4015, 2021

2021
[158]

Long short-term memory,

S. Hochreiter, “Long short-term memory,” Neural Computation MIT- Press, 1997

1997
[159]

Deep learning with long short-term memory for time series prediction,

Y . Hua, Z. Zhao et al. , “Deep learning with long short-term memory for time series prediction,” IEEE Commun. Mag. , vol. 57, no. 6, pp. 114–119, 2019

2019
[160]

Gate-variants of gated recurrent unit (GRU) neural networks,

R. Dey and F. M. Salem, “Gate-variants of gated recurrent unit (GRU) neural networks,” in Proc. MWSCAS’2017, 2017, pp. 1597–1600

2017
[161]

Hybrid speech recognition with deep bidirectional LSTM,

A. Graves, N. Jaitly et al. , “Hybrid speech recognition with deep bidirectional LSTM,” in Proc. ASRU Workshops’2013, 2013, pp. 273– 278

2013
[162]

Temporal convolutional networks for action segmentation and detection,

C. Lea, M. D. Flynn et al., “Temporal convolutional networks for action segmentation and detection,” in Proc. CVPR’2017, 2017, pp. 156–165

2017
[163]

Deep learning-based multi-user positioning in wireless FDMA cellular networks,

Z. Chen, Z. Zhang et al., “Deep learning-based multi-user positioning in wireless FDMA cellular networks,” IEEE J. Select. Areas Commun., vol. 41, no. 12, pp. 3848–3862, 2023

2023
[164]

Diffractive RSS based multinetwork aided 3D positioning for distributed massive MIMO systems,

X. Wu and M. Jiang, “Diffractive RSS based multinetwork aided 3D positioning for distributed massive MIMO systems,” IEEE Trans. Commun., vol. 70, no. 6, pp. 3865–3878, 2022

2022
[165]

Attention is all you need,

A. Vaswani, “Attention is all you need,” in Proc. NIPS’2017, 2017

2017
[166]

An image is worth 16x16 words: Transformers for image recognition at scale,

A. Dosovitskiy, “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929 , 2020

2010 arXiv
[167]

Estimating multipath component delays with Transformer models,

J. Ott, M. Stahlke et al., “Estimating multipath component delays with Transformer models,” IEEE J. Ind. Sea. Pos. Nav., vol. 2, pp. 219–229, 2024

2024
[168]

Soft: Softmax-free transformer with linear complexity,

J. Lu, J. Yao et al. , “Soft: Softmax-free transformer with linear complexity,” in Proc. NIPS’2021, vol. 34, 2021, pp. 21 297–21 309

2021
[169]

Self-supervised and invariant representa- tions for wireless localization,

A. Salihu, M. Rupp et al. , “Self-supervised and invariant representa- tions for wireless localization,” IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 8281–8296, 2024

2024
[170]

Swin-Loc: Transformer-based CSI fingerprinting indoor localization with MIMO ISAC system,

X. Xu, F. Zhu et al., “Swin-Loc: Transformer-based CSI fingerprinting indoor localization with MIMO ISAC system,” IEEE Trans. Veh. Technol., 2024

2024
[171]

Transformer-aided mobile positioning for 6G ultra-dense networks,

H. J. Cho, Y . Ahn et al. , “Transformer-aided mobile positioning for 6G ultra-dense networks,” IEEE Trans. Veh. Technol., 2024. IEEE XXXX XXXXX, VOL. X, NO. X, JANUARY 2025 34

2024
[172]

Sensing-assisted high reliable communication: A Transformer-based beamforming approach,

Y . Cui, J. Nie et al., “Sensing-assisted high reliable communication: A Transformer-based beamforming approach,” IEEE J. Sel. Top. Signal Process., vol. 18, no. 5, pp. 782–795, 2024

2024
[173]

A survey on transfer learning,

S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. Knowl. Data Eng. , vol. 22, no. 10, pp. 1345–1359, 2009

2009
[174]

What is being transferred in transfer learning?

B. Neyshabur, H. Sedghi et al., “What is being transferred in transfer learning?” in Proc. NIPS’2020, vol. 33, 2020, pp. 512–523

2020
[175]

Meta-learning in neural networks: A survey,

T. Hospedales, A. Antoniou et al., “Meta-learning in neural networks: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 44, no. 9, pp. 5149–5169, 2022

2022
[176]

MetaLoc: Learning to learn wireless localiza- tion,

J. Gao, D. Wu et al. , “MetaLoc: Learning to learn wireless localiza- tion,” IEEE J. Select. Areas Commun. , vol. 41, no. 12, pp. 3831–3847, 2023

2023
[177]

Bayesian meta-learning: Toward fast adaptation in neural network positioning techniques,

Q. Pu, Y . Chen et al., “Bayesian meta-learning: Toward fast adaptation in neural network positioning techniques,” IEEE Internet Things J. , vol. 11, no. 8, pp. 14 924–14 937, 2024

2024
[178]

A continual learning survey: Defying forgetting in classification tasks,

M. De Lange, R. Aljundi et al., “A continual learning survey: Defying forgetting in classification tasks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 7, pp. 3366–3385, 2022

2022
[179]

Rainbow memory: Continual learning with a memory of diverse samples,

J. Bang, H. Kim et al., “Rainbow memory: Continual learning with a memory of diverse samples,” in Proc. IEEE/CVF CVPR’2021 , 2021, pp. 8214–8223

2021
[180]

Expert gate: Lifelong learning with a network of experts,

R. Aljundi, P. Chakravarty et al., “Expert gate: Lifelong learning with a network of experts,” in Proc. IEEE/CVF CVPR’2017, 2017, pp. 7120– 7129

2017
[181]

Class-incremental learning: survey and performance evaluation on image classification,

M. Masana, X. Liu et al. , “Class-incremental learning: survey and performance evaluation on image classification,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 5, pp. 5513–5533, 2022

2022
[182]

Class-incremental learning: A survey,

D.-W. Zhou, Q.-W. Wang et al., “Class-incremental learning: A survey,” IEEE Trans. Pattern Anal. Mach. Intell. , 2024

2024
[183]

Generative adversarial nets,

I. Goodfellow, J. Pouget-Abadie et al. , “Generative adversarial nets,” Proc. NIPS’2014, vol. 27, 2014

2014
[184]

A review on generative adversarial networks: Algorithms, theory, and applications,

J. Gui, Z. Sun et al. , “A review on generative adversarial networks: Algorithms, theory, and applications,” IEEE Trans. Knowl. Data Eng. , vol. 35, no. 4, pp. 3313–3332, 2021

2021
[185]

A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,

J. Yim, D. Joo et al. , “A gift from knowledge distillation: Fast optimization, network minimization and transfer learning,” in Proc. IEEE/CVF CVPR’2017, 2017, pp. 7130–7138

2017
[186]

A survey of ensemble learning: Concepts, algorithms, applications, and prospects,

I. D. Mienye and Y . Sun, “A survey of ensemble learning: Concepts, algorithms, applications, and prospects,” IEEE Access , vol. 10, pp. 99 129–99 149, 2022

2022
[187]

Self-supervised representation learning: Introduction, advances, and challenges,

L. Ericsson, H. Gouk et al. , “Self-supervised representation learning: Introduction, advances, and challenges,” IEEE Signal Processing Mag- azine, vol. 39, no. 3, pp. 42–62, 2022

2022
[188]

An overview of the challenges and progress in meeting the E-911 requirement for location service,

J. H. Reed, K. J. Krizman et al., “An overview of the challenges and progress in meeting the E-911 requirement for location service,” IEEE Commun. Mag., vol. 36, no. 4, pp. 30–37, 1998

1998
[189]

TS 22.071, Release 1999, Oct

Location services (LCS); service description , 3GPP Std. TS 22.071, Release 1999, Oct. 1999

1999
[190]

TS 25.305, Release 7, Jun

Stage 2 functional specification of user equipment (UE) positioning in UTRAN, 3GPP Std. TS 25.305, Release 7, Jun. 2005

2005
[191]

TS 36.305, Release 9, Jan

Stage 2 functional specification of UE positioning in E-UTRAN , 3GPP Std. TS 36.305, Release 9, Jan. 2013

2013
[192]

TS 36.809, Release 12, Sep

RF pattern matching location method in LTE , 3GPP Std. TS 36.809, Release 12, Sep. 2013

2013
[193]

TS 36.855, Release 13, Jan

Feasibility of positioning enhancements for E-UTRA , 3GPP Std. TS 36.855, Release 13, Jan. 2015

2015
[194]

TR 22.872, Release 16, Sept

Study on positioning use cases , 3GPP Std. TR 22.872, Release 16, Sept. 2018

2018
[195]

TR 22.261, Release 19, Sept

Service requirements for the 5G system, 3GPP Std. TR 22.261, Release 19, Sept. 2024

2024
[196]

TS 38.455, Release 18, Dec

NR Positioning Protocol A (NRPPa) , 3GPP Std. TS 38.455, Release 18, Dec. 2024

2024
[197]

TS 38.857, Release 16, Dec

Study on NR positioning enhancements , 3GPP Std. TS 38.857, Release 16, Dec. 2019

2019
[198]

TS 38.845, Release 17, Otc

Study on scenarios and requirements of in-coverage, partial coverage, and out-of-coverage NR positioning use cases , 3GPP Std. TS 38.845, Release 17, Otc. 2021

2021
[199]

TS 38.859, Release 18, Jul

Study on expanded and improved NR positioning, 3GPP Std. TS 38.859, Release 18, Jul. 2024

2024
[200]

TS 38.843, Release 18, Jan

Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface , 3GPP Std. TS 38.843, Release 18, Jan. 2024

2024
[201]

Large wireless localization model (lwlm): A foundation model for positioning in 6G networks,

G. Pan, K. Huang et al. , “Large wireless localization model (lwlm): A foundation model for positioning in 6G networks,” arXiv preprint arXiv:2505.10134, 2025

2025 arXiv
[202]

Digital twin empowered wireless positioning: Prospects, architecture, and challenges,

X. Shi, X. Xuan et al., “Digital twin empowered wireless positioning: Prospects, architecture, and challenges,” IEEE Network, 2025

2025
[203]

Energy optimization of multi-task DNN infer- ence in MEC-assisted XR devices: A lyapunov-guided reinforcement learning approach,

Y . Sun, J. Qiu et al. , “Energy optimization of multi-task DNN infer- ence in MEC-assisted XR devices: A lyapunov-guided reinforcement learning approach,” IEEE Internet Things J. , 2025

2025
[204]

Non-line-of-sight identification based on unsupervised machine learning in ultra wideband systems,

J. Fan and A. S. Awan, “Non-line-of-sight identification based on unsupervised machine learning in ultra wideband systems,” IEEE Access, vol. 7, pp. 32 464–32 471, 2019

2019
[205]

NLOS identification for wideband mmWave systems at 28 GHz,

A. Huang, L. Tian et al., “NLOS identification for wideband mmWave systems at 28 GHz,” in 2019 IEEE 89th Vehicular Technology Confer- ence (VTC2019-Spring), vol. 2008, 2019, pp. 1–6

2019
[206]

Wireless channel propagation scenarios identification: A perspective of machine learning,

J. Zhang, L. Liu et al. , “Wireless channel propagation scenarios identification: A perspective of machine learning,” IEEE Access, vol. 8, pp. 47 797–47 806, 2020

2020
[207]

Identification of NLOS and multi- path conditions in UWB localization using machine learning methods,

C. L. Sang, B. Steinhagen et al. , “Identification of NLOS and multi- path conditions in UWB localization using machine learning methods,” Applied Sciences, vol. 10, no. 11, p. 3980, 2020

2020
[208]

Machine learning-enabled LOS/NLOS identification for MIMO systems in dynamic environments,

C. Huang, A. F. Molisch et al., “Machine learning-enabled LOS/NLOS identification for MIMO systems in dynamic environments,” IEEE Trans. Wireless Commun., vol. 19, no. 6, pp. 3643–3657, 2020

2020
[209]

A simple efficient lightweight CNN method for LOS/NLOS identification in wireless communication systems,

Y . Zhu, B. Xu et al., “A simple efficient lightweight CNN method for LOS/NLOS identification in wireless communication systems,” IEEE Commun. Lett., vol. 27, no. 6, pp. 1515–1519, 2023

2023
[210]

Channel non-line-of-sight identification based on convolutional neural networks,

Q. Zheng, R. He et al., “Channel non-line-of-sight identification based on convolutional neural networks,” IEEE Wireless Commun. Lett. , vol. 9, no. 9, pp. 1500–1504, 2020

2020
[211]

An UWB channel impulse response de-noising method for NLOS/LOS classification boosting,

C. Jiang, S. Chen et al., “An UWB channel impulse response de-noising method for NLOS/LOS classification boosting,” IEEE Commun. Lett. , vol. 24, no. 11, pp. 2513–2517, 2020

2020
[212]

LOS/NLOS identification for indoor UWB positioning based on morlet wavelet transform and convolutional neural networks,

Z. Cui, Y . Gao et al. , “LOS/NLOS identification for indoor UWB positioning based on morlet wavelet transform and convolutional neural networks,” IEEE Commun. Lett. , vol. 25, no. 3, pp. 879–882, 2021

2021
[213]

Multi-classification of UWB signal propagation channels based on one-dimensional wavelet packet analysis and CNN,

J. Wang, K. Yu et al., “Multi-classification of UWB signal propagation channels based on one-dimensional wavelet packet analysis and CNN,” IEEE Trans. Veh. Technol., vol. 71, no. 8, pp. 8534–8547, 2022

2022
[214]

A novel channel identification architecture for mmWave systems based on eigen features,

Y . Zhang, J. Sun et al., “A novel channel identification architecture for mmWave systems based on eigen features,” inProc. IEEE WCSP’2022, 2022, pp. 550–555

2022
[215]

UWB NLOS identification and mitigation based on gramian angular field and parallel deep learning model,

B. Deng, T. Xu et al. , “UWB NLOS identification and mitigation based on gramian angular field and parallel deep learning model,” IEEE Sensors J., vol. 23, no. 22, pp. 28 513–28 525, 2023

2023
[216]

Transfer learning-based NLOS identifi- cation for UWB in dynamic obstructed settings,

R. E. Nkrow, B. Silva et al., “Transfer learning-based NLOS identifi- cation for UWB in dynamic obstructed settings,” IEEE Trans. Ind. Inf., vol. 20, no. 3, pp. 4839–4849, 2024

2024
[217]

On the latent space of mmWave MIMO channels for NLOS identification in 5G-advanced systems,

B. C. Tedeschini, M. Nicoli et al. , “On the latent space of mmWave MIMO channels for NLOS identification in 5G-advanced systems,” IEEE J. Select. Areas Commun. , vol. 41, no. 6, pp. 1655–1669, 2023

2023
[218]

Cooperative deep-learning positioning in mmWave 5G-advanced networks,

B. C. Tedeschini and M. Nicoli, “Cooperative deep-learning positioning in mmWave 5G-advanced networks,” IEEE J. Select. Areas Commun. , vol. 41, no. 12, pp. 3799–3815, 2023

2023
[219]

UWB LOS/NLOS identification in multiple indoor environments using deep learning methods,

Q. Liu, Z. Yin et al. , “UWB LOS/NLOS identification in multiple indoor environments using deep learning methods,” Physical Commu- nication, vol. 52, p. 101695, 2022

2022
[220]

UWB positioning system based on LSTM classification with mitigated NLOS effects,

D.-H. Kim, A. Farhad et al., “UWB positioning system based on LSTM classification with mitigated NLOS effects,” IEEE Internet Things J. , vol. 10, no. 2, pp. 1822–1835, 2023

2023
[221]

UWB localization based on dual-channel neural network and total least square method,

H. Lv, J. Feng et al., “UWB localization based on dual-channel neural network and total least square method,” IEEE Sensors J., vol. 24, no. 3, pp. 3477–3487, 2024

2024
[222]

UWB (N)LOS identification based on deep learning and transfer learning,

J. Li, S. Liu et al., “UWB (N)LOS identification based on deep learning and transfer learning,” IEEE Commun. Lett. , vol. 28, no. 9, pp. 2111– 2115, 2024

2024
[223]

Deep learning and hybrid fusion based LOS/NLOS identification in substation scenarios for power internet of things,

T. Zhou, Y . Wang et al. , “Deep learning and hybrid fusion based LOS/NLOS identification in substation scenarios for power internet of things,” IEEE Internet Things J. , pp. 1–1, 2024

2024
[224]

Fuzzy Transformer machine learning for UWB NLOS identification and ranging mitigation,

H. Yang, Y . Wang et al. , “Fuzzy Transformer machine learning for UWB NLOS identification and ranging mitigation,” IEEE Transactions on Instrumentation and Measurement , 2025

2025
[225]

Entropy-based TOA estimation and SVM-based ranging error mitigation in UWB ranging systems,

Z. Yin, K. Cui et al., “Entropy-based TOA estimation and SVM-based ranging error mitigation in UWB ranging systems,” Sensors, vol. 15, no. 5, pp. 11 701–11 724, 2015

2015
[226]

A semi-supervised learning approach for UWB ranging error mitigation,

T. Wang, K. Hu et al., “A semi-supervised learning approach for UWB ranging error mitigation,” IEEE Wireless Commun. Lett., vol. 10, no. 3, pp. 688–691, 2021

2021
[227]

Machine learning for time-of-arrival estimation with 5G signals in indoor positioning,

Z. Liu, L. Chen et al., “Machine learning for time-of-arrival estimation with 5G signals in indoor positioning,” IEEE Internet Things J., vol. 10, no. 11, pp. 9782–9795, 2023. IEEE XXXX XXXXX, VOL. X, NO. X, JANUARY 2025 35

2023
[228]

Kernel methods for accurate UWB- based ranging with reduced complexity,

V . Savic, E. G. Larsson et al. , “Kernel methods for accurate UWB- based ranging with reduced complexity,” IEEE Trans. Wireless Com- mun., vol. 15, no. 3, pp. 1783–1793, 2016

2016
[229]

A machine learning approach for Wi-Fi RTT ranging,

N. Dvorecki, O. Bar-Shalom et al., “A machine learning approach for Wi-Fi RTT ranging,” in The International Technical Meeting of the The Institute of Navigation , 2019, pp. 435–444

2019
[230]

ToA and TDoA estimation using artificial neural networks for high-accuracy ranging,

A. Kirmaz, T. ¸ Sahin et al., “ToA and TDoA estimation using artificial neural networks for high-accuracy ranging,” IEEE J. Select. Areas Commun., vol. 41, no. 12, pp. 3816–3830, 2023

2023
[231]

A deep neural network approach for time-of-arrival estimation in multipath channels,

O. Bialer, N. Garnett et al. , “A deep neural network approach for time-of-arrival estimation in multipath channels,” in Proc. IEEE ICASSP’2018. IEEE, 2018, pp. 2936–2940

2018
[232]

Deep learning based preamble detection and TOA estimation,

H. Sun, A. O. Kaya et al. , “Deep learning based preamble detection and TOA estimation,” in Proc. IEEE GLOBECOM’2019, vol. 13, 2019, pp. 1–6

2019
[233]

Super-resolution time-of-arrival estimation using neural networks,

Y .-S. Hsiao, M. Yanget al., “Super-resolution time-of-arrival estimation using neural networks,” in Proc. IEEE EUSIPCO’2020, vol. 15. IEEE, 2021, pp. 1692–1696

2020
[234]

TOA estimation scheme based on CNN for b-IFDM-based preambles,

Z. Luo, T. Tao et al. , “TOA estimation scheme based on CNN for b-IFDM-based preambles,” in Proc. IEEE VTC-Spring’2019 , vol. 37, 2019, pp. 1–5

2019
[235]

A deep learning approach to position estimation from channel impulse responses,

A. Niitsoo, T. Edelhäußer et al., “A deep learning approach to position estimation from channel impulse responses,” Sensors, vol. 19, no. 5, p. 1064, 2019

2019
[236]

Robust ToA-estimation using convolutional neural networks on randomized channel models,

T. Feigl, E. Eberlein et al., “Robust ToA-estimation using convolutional neural networks on randomized channel models,” in Proc. IEEE IPIN’2021, 2021, pp. 1–8

2021
[237]

Novel cascade CNN algorithm for UWB signal denoising, compressing, and ToA estimation,

A. Abbasi and H. Liu, “Novel cascade CNN algorithm for UWB signal denoising, compressing, and ToA estimation,” in Proc. IEEE CCWC’2021. IEEE, 2021, pp. 0721–0725

2021
[238]

A deep-learning-based time of arrival estimation using kernel sparse encoding scheme,

S. Wei, H. Pan et al., “A deep-learning-based time of arrival estimation using kernel sparse encoding scheme,” Signal Processing, vol. 209, p. 109047, 2023

2023
[239]

A variational learning approach for concur- rent distance estimation and environmental identification,

Y . Li, S. Mazuelas et al., “A variational learning approach for concur- rent distance estimation and environmental identification,” IEEE Trans. Wireless Commun., vol. 22, no. 9, pp. 6252–6266, 2023

2023
[240]

Localisation in wireless networks using deep bidirectional recurrent neural networks,

D. Lynch, L. Ho et al., “Localisation in wireless networks using deep bidirectional recurrent neural networks,” in Proc. IEEE IJCNN’2020 . IEEE, 2020, pp. 1–8

2020
[241]

Multipath delay estimation in complex environments using Transformer,

J. Ott, M. Stahlke et al. , “Multipath delay estimation in complex environments using Transformer,” in Proc. IEEE IPIN’2023, 2023, pp. 1–6

2023
[242]

Convolutional neural networks based joint AOA/TOF estimation,

S. Jang, H. Lee et al. , “Convolutional neural networks based joint AOA/TOF estimation,” in Proc. IEEE ICEIC’2021 , 2021, pp. 1–4

2021
[243]

2d-CNN-based AoA-ToA estimation in presence of angle-dependent phase errors using pico-cells,

H. Wang, S. Liu et al. , “2d-CNN-based AoA-ToA estimation in presence of angle-dependent phase errors using pico-cells,” in Proc. Radar’2021, 2021, pp. 1817–1821

2021
[244]

A DOA and TOA joint estimation algorithm based on deep transfer learning,

H. Pan and S. Wei, “A DOA and TOA joint estimation algorithm based on deep transfer learning,” Electronics Letters, vol. 59, no. 3, 2023

2023
[245]

Sensor-aided learning for Wi-Fi positioning with beacon channel state information,

J. Choi, “Sensor-aided learning for Wi-Fi positioning with beacon channel state information,” IEEE Trans. Wireless Commun. , vol. 21, no. 7, pp. 5251–5264, 2022

2022
[246]

Machine-learning-based fast angle-of-arrival recognition for vehicular communications,

M. Yang, B. Ai et al. , “Machine-learning-based fast angle-of-arrival recognition for vehicular communications,” IEEE Trans. Veh. Technol., vol. 70, no. 2, pp. 1592–1605, 2021

2021
[247]

Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system,

H. Huang, J. Yang et al., “Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system,” IEEE Trans. Veh. Technol., vol. 67, no. 9, pp. 8549–8560, 2018

2018
[248]

A novel phase enhancement method for low- angle estimation based on supervised DNN learning,

H. Xiang, B. Chen et al., “A novel phase enhancement method for low- angle estimation based on supervised DNN learning,” IEEE Access , vol. 7, pp. 82 329–82 336, 2019

2019
[249]

Deep neural network for estimation of direction of arrival with antenna array,

M. Chen, Y . Gong et al. , “Deep neural network for estimation of direction of arrival with antenna array,” IEEE Access , vol. 8, pp. 140 688–140 698, 2020

2020
[250]

DoA estimation using neural network- based covariance matrix reconstruction,

A. Barthelme and W. Utschick, “DoA estimation using neural network- based covariance matrix reconstruction,” IEEE Signal Process. Lett. , vol. 28, pp. 783–787, 2021

2021
[251]

Angle-of-arrival estimation using an adaptive machine learning framework,

A. Khan, S. Wang et al., “Angle-of-arrival estimation using an adaptive machine learning framework,” IEEE Commun. Lett., vol. 23, no. 2, pp. 294–297, 2019

2019
[252]

Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections,

Z.-M. Liu, C. Zhang et al. , “Direction-of-arrival estimation based on deep neural networks with robustness to array imperfections,” IEEE Trans. Antennas Propagat., vol. 66, no. 12, pp. 7315–7327, 2018

2018
[253]

Two-dimensional DOA estimation via deep ensemble learning,

W. Zhu, M. Zhang et al., “Two-dimensional DOA estimation via deep ensemble learning,” IEEE Access, vol. 8, pp. 124 544–124 552, 2020

2020
[254]

Improved de-multipath neural network models with self-paced feature-to-feature learning for DOA estimation in multipath environment,

H. Xiang, B. Chen et al. , “Improved de-multipath neural network models with self-paced feature-to-feature learning for DOA estimation in multipath environment,” IEEE Trans. Veh. Technol., vol. 69, no. 5, pp. 5068–5078, 2020

2020
[255]

Deep networks for direction- of-arrival estimation in low SNR,

G. Papageorgiou, M. Sellathurai et al., “Deep networks for direction- of-arrival estimation in low SNR,”IEEE Trans. Signal Process., vol. 69, pp. 3714–3729, 2021

2021
[256]

SDOA-net: An efficient deep-learning-based DOA estimation network for imperfect array,

P. Chen, Z. Chen et al., “SDOA-net: An efficient deep-learning-based DOA estimation network for imperfect array,” IEEE Trans. Instrum. Meas., vol. 73, pp. 1–12, 2024

2024
[257]

Model-driven deep neural network for enhanced AoA estimation using 5G gNB,

S. Liu, X. Li et al., “Model-driven deep neural network for enhanced AoA estimation using 5G gNB,” in Proc. AAAI’2024, 2024, pp. 214– 221

2024
[258]

Complex ResNet aided DoA estimation for near- field MIMO systems,

Y . Cao, T. Lv et al., “Complex ResNet aided DoA estimation for near- field MIMO systems,” IEEE Trans. Veh. Technol., vol. 69, no. 10, pp. 11 139–11 151, 2020

2020
[259]

Complex neural network based joint AoA and AoD estimation for bistatic ISAC,

S. Naoumi, A. Bazzi et al. , “Complex neural network based joint AoA and AoD estimation for bistatic ISAC,” IEEE J. Sel. Top. Signal Process., pp. 1–15, 2024

2024
[260]

A deep learning based AoA estimation method in NLOS environments,

T. Wang, Y . Manet al., “A deep learning based AoA estimation method in NLOS environments,” in Proc. IEEE Globecom Workshops’2021 . IEEE, 2021, pp. 1–6

2021
[261]

Deep neural networks for direction of arrival estimation of multiple targets with sparse prior for line-of-sight scenarios,

S. Xu, A. Brighente et al. , “Deep neural networks for direction of arrival estimation of multiple targets with sparse prior for line-of-sight scenarios,” IEEE Trans. Veh. Technol., vol. 72, no. 4, pp. 4683–4696, 2023

2023
[262]

Improving DOA estimation via an optimal deep residual neural network classifier on uniform linear arrays,

H. A. Kassir, N. V . Kantartzis et al., “Improving DOA estimation via an optimal deep residual neural network classifier on uniform linear arrays,” IEEE Open J. Antennas Propag. , vol. 5, no. 2, pp. 460–473, 2024

2024
[263]

Deep learning-based DOA estimation,

S. Zheng, Z. Yang et al., “Deep learning-based DOA estimation,” IEEE Trans. Cogn. Commun. Netw. , vol. 10, no. 3, pp. 819–835, 2024

2024
[264]

Vehicle positioning with deep-learning-based direction-of-arrival estimation of incoherently distributed sources,

Y . Tian, S. Liu et al. , “Vehicle positioning with deep-learning-based direction-of-arrival estimation of incoherently distributed sources,” IEEE Internet Things J. , vol. 9, no. 20, pp. 20 083–20 095, 2022

2022
[265]

Transfer learning for angle of arrivals estimation in massive MIMO system,

Z. Guo, K. Lin et al., “Transfer learning for angle of arrivals estimation in massive MIMO system,” in Proc. IEEE ICCC’2022, 2022, pp. 506– 511

2022
[266]

Improved direction-of-arrival estimation method based on LSTM neural networks with robustness to array imperfections,

H. Xiang, B. Chen et al. , “Improved direction-of-arrival estimation method based on LSTM neural networks with robustness to array imperfections,” Appl. Intell., vol. 51, no. 7, pp. 4420–4433, 2021

2021
[267]

A transformer-based signal denoising network for AoA estimation in NLoS environments,

J. Liu, T. Wang et al., “A transformer-based signal denoising network for AoA estimation in NLoS environments,” IEEE Commun. Lett. , vol. 26, no. 10, pp. 2336–2339, 2022

2022
[268]

TransAoA: Transformer-based angle of arrival estimation for BLE indoor localization,

W. Wu, D. Zhou et al., “TransAoA: Transformer-based angle of arrival estimation for BLE indoor localization,” IEEE Trans. Instrum. Meas. , vol. 74, pp. 1–12, 2025

2025
[269]

Learning RSSI feature via ranking model for Wi-Fi fingerprinting localization,

P. Chen, J. Shang et al., “Learning RSSI feature via ranking model for Wi-Fi fingerprinting localization,” IEEE Trans. Veh. Technol., vol. 69, no. 2, pp. 1695–1705, 2019

2019
[270]

Machine learning methods for RSS- based user positioning in distributed massive MIMO,

K. S. V . Prasad, E. Hossain et al., “Machine learning methods for RSS- based user positioning in distributed massive MIMO,” IEEE Trans. Wireless Commun., vol. 17, no. 12, pp. 8402–8417, 2018

2018
[271]

Indoor positioning based on fingerprint-image and deep learning,

W. Shao, H. Luo et al., “Indoor positioning based on fingerprint-image and deep learning,” IEEE Access, vol. 6, pp. 74 699–74 712, 2018

2018
[272]

Dfops: Deep-learning-based finger- printing outdoor positioning scheme in hybrid networks,

G. B. Tarekegn, R.-T. Juang et al., “Dfops: Deep-learning-based finger- printing outdoor positioning scheme in hybrid networks,”IEEE Internet Things J., vol. 8, no. 5, pp. 3717–3729, 2021

2021
[273]

Neural network fingerprinting and GNSS data fusion for improved localization in 5G,

R. Klus, J. Talvitie et al. , “Neural network fingerprinting and GNSS data fusion for improved localization in 5G,” in Proc. IEEE ICL- GNSS’2021, 2021, pp. 1–6

2021
[274]

5G1M: Indoor fingerprint positioning using a single 5G module,

C. Wang, X. Jin et al., “5G1M: Indoor fingerprint positioning using a single 5G module,” IEEE Sensors J. , pp. 1–1, 2024

2024
[275]

Outdoor positioning based on deep learning and wireless network fingerprint technology,

D. Li, Y . Lei et al., “Outdoor positioning based on deep learning and wireless network fingerprint technology,” Int J RF Microw Comput Aided Eng, vol. 30, no. 12, 2020

2020
[276]

DelFin: A deep learning based CSI fingerprinting indoor localization in IoT context,

B. Berruet, O. Baala et al. , “DelFin: A deep learning based CSI fingerprinting indoor localization in IoT context,” in Proc. IEEE IPIN’2018, 2018, pp. 1–8

2018
[277]

Multi-environment based meta-learning with CSI fingerprints for radio based positioning,

A. Foliadis, M. H. Castañeda Garcia et al., “Multi-environment based meta-learning with CSI fingerprints for radio based positioning,” in Proc. IEEE WCNC’2023 , 2023, pp. 1–6

2023
[278]

Exploiting semantic localization in highly dynamic wireless networks using deep homoscedastic domain adapta- tion,

L. Chu, A. Alghafis et al., “Exploiting semantic localization in highly dynamic wireless networks using deep homoscedastic domain adapta- tion,” IEEE Trans. Commun. , pp. 1–1, 2024. IEEE XXXX XXXXX, VOL. X, NO. X, JANUARY 2025 36

2024
[279]

Dyloc: Dynamic localization for massive MIMO using predictive recurrent neural networks,

F. Hejazi, K. Vuckovic et al., “Dyloc: Dynamic localization for massive MIMO using predictive recurrent neural networks,” in Proc. IEEE INFOCOM’2021, 2021, pp. 1–9

2021
[280]

Cooperative fingerprint positioning for cell-free massive MIMO systems,

J. Qiu, K. Xu et al., “Cooperative fingerprint positioning for cell-free massive MIMO systems,” in Proc. IEEE WCSP’2020 , 2020, pp. 382– 387

2020
[281]

Cross-region fusion and fast adaptation for multi- scenario fingerprint-based localization in cell-free massive MIMO systems,

H. Xu, R. Liu et al., “Cross-region fusion and fast adaptation for multi- scenario fingerprint-based localization in cell-free massive MIMO systems,” IEEE Wireless Commun. Lett. , vol. 13, no. 10, pp. 2882– 2886, 2024

2024
[282]

Deep learning based fingerprint positioning for multi-cell massive MIMO-OFDM systems,

X. Gong, X. Liu et al., “Deep learning based fingerprint positioning for multi-cell massive MIMO-OFDM systems,” IEEE Trans. Veh. Technol., 2023

2023
[283]

iPos-5G: Indoor positioning via commercial 5G NR CSI,

Y . Ruan, L. Chen et al., “iPos-5G: Indoor positioning via commercial 5G NR CSI,” IEEE Internet Things J. , vol. 10, no. 10, pp. 8718–8733, 2023

2023
[284]

Transfer learning-enhanced instantaneous multi- person indoor localization by CSI,

Z. He, K. Deng et al., “Transfer learning-enhanced instantaneous multi- person indoor localization by CSI,” arXiv preprint arXiv:2403.01153 , 2024

2024 arXiv
[285]

GRIDLoc: A gradient blending and deep learning-based localization approach combining RSS and CSI,

Q. Dai, B. Qian et al. , “GRIDLoc: A gradient blending and deep learning-based localization approach combining RSS and CSI,” IEEE Wireless Commun. Lett., vol. 13, no. 9, pp. 2620–2624, 2024

2024
[286]

Fingerprint-based localization using com- mercial LTE signals: A field-trial study,

H. Zhang, Z. Zhang et al., “Fingerprint-based localization using com- mercial LTE signals: A field-trial study,” inProc. IEEE VTC-Fall’2019, 2019, pp. 1–5

2019
[287]

SWiBluX: Multi-sensor deep learning fingerprint for precise real-time indoor tracking,

A. Belmonte-Hernandez, G. Hernandez-Penaloza et al. , “SWiBluX: Multi-sensor deep learning fingerprint for precise real-time indoor tracking,” IEEE Sensors J. , vol. 19, no. 9, pp. 3473–3486, 2019

2019
[288]

AoA-and-amplitude fingerprint based indoor intelligent localization scheme for 5G wireless communications,

Z. Zhang, L. Wu et al., “AoA-and-amplitude fingerprint based indoor intelligent localization scheme for 5G wireless communications,” in Proc. IEEE WCSP’2021 . IEEE, 2021, pp. 1–5

2021
[289]

Influence of measured radio map interpolation on indoor positioning algorithms,

T. Bravenec, M. Gould et al. , “Influence of measured radio map interpolation on indoor positioning algorithms,” IEEE Sensors J. , vol. 23, no. 17, pp. 20 044–20 054, 2023

2023
[290]

Space-frequency-interpolated radio map,

K. Sato, K. Suto et al., “Space-frequency-interpolated radio map,”IEEE Trans. Veh. Technol., vol. 70, no. 1, pp. 714–725, 2021

2021
[291]

Multi-phase fingerprint map based on interpola- tion for indoor localization using iBeacons,

J. Zuo, S. Liu et al., “Multi-phase fingerprint map based on interpola- tion for indoor localization using iBeacons,” IEEE Sensors J. , vol. 18, no. 8, pp. 3351–3359, 2018

2018
[292]

Kriging-based interference power constraint: Integrated design of the radio environment map and transmission power,

K. Sato and T. Fujii, “Kriging-based interference power constraint: Integrated design of the radio environment map and transmission power,” IEEE Trans. Cogn. Commun. Netw. , vol. 3, no. 1, pp. 13–25, 2017

2017
[293]

Radio environment map construction based on gaussian process with positional uncertainty,

P. Zhen, B. Zhang et al. , “Radio environment map construction based on gaussian process with positional uncertainty,” IEEE Wireless Commun. Lett., vol. 11, no. 8, pp. 1639–1643, 2022

2022
[294]

Structured group sparsity: A novel indoor WLAN localization, outlier detection, and radio map interpolation scheme,

A. Khalajmehrabadi, N. Gatsis et al. , “Structured group sparsity: A novel indoor WLAN localization, outlier detection, and radio map interpolation scheme,” IEEE Trans. Veh. Technol. , vol. 66, no. 7, pp. 6498–6510, 2017

2017
[295]

On the performance of neural network residual kriging in radio environment mapping,

K. Sato, K. Inage et al. , “On the performance of neural network residual kriging in radio environment mapping,” IEEE Access, vol. 7, pp. 94 557–94 568, 2019

2019
[296]

DeepREM: Deep-learning- based radio environment map estimation from sparse measurements,

A. Chaves-Villota and C. A. Viteri-Mera, “DeepREM: Deep-learning- based radio environment map estimation from sparse measurements,” IEEE Access, vol. 11, pp. 48 697–48 714, 2023

2023
[297]

Deep-learning-based radio map recon- struction for V2X communications,

S. Roger, M. Brambilla et al., “Deep-learning-based radio map recon- struction for V2X communications,”IEEE Trans. Veh. Technol., vol. 73, no. 3, pp. 3863–3871, 2024

2024
[298]

Radio map reconstruction based on Trans- former from sparse measurement,

Z. Chen, D. Guo et al. , “Radio map reconstruction based on Trans- former from sparse measurement,” in Proc. IEEE ICCT’2024 , 2024, pp. 917–923

2024
[299]

Sparsely self-supervised generative adversarial nets for radio frequency estimation,

Z. Li, J. Cao et al. , “Sparsely self-supervised generative adversarial nets for radio frequency estimation,” IEEE J. Select. Areas Commun. , vol. 37, no. 11, pp. 2428–2442, 2019

2019
[300]

Deep completion autoencoders for radio map estimation,

Y . Teganya and D. Romero, “Deep completion autoencoders for radio map estimation,” IEEE Trans. Wireless Commun. , vol. 21, no. 3, pp. 1710–1724, 2022

2022

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [1]

Survey of cellular mobile radio localization methods: From 1g to 5G,

J. A. del Peral-Rosado, R. Raulefs et al. , “Survey of cellular mobile radio localization methods: From 1g to 5G,” IEEE Commun. Surv. Tutorials, vol. 20, no. 2, pp. 1124–1148, 2018

2018

[2] [2]

A survey of indoor localization systems and technologies,

R. C. Shit, S. Sharma et al., “A survey of indoor localization systems and technologies,” IEEE Commun. Surv. Tutorials , vol. 21, no. 3, pp. 2568–2599, 2019

2019

[3] [3]

Efficient processing of deep neural networks: A tutorial and survey,

V . Sze, Y .-H. Chenet al., “Efficient processing of deep neural networks: A tutorial and survey,” Proc. IEEE, vol. 105, no. 12, pp. 2295–2329, 2017

2017

[4] [4]

Less data, more knowledge: Building next-generation semantic communication networks,

C. Chaccour, W. Saad et al. , “Less data, more knowledge: Building next-generation semantic communication networks,” IEEE Commun. Surv. Tutorials, vol. 27, no. 1, pp. 37–76, 2025

2025

[5] [5]

A survey on xai for 5g and beyond security: Technical aspects, challenges and research directions,

T. Senevirathna, V . H. La et al., “A survey on xai for 5g and beyond security: Technical aspects, challenges and research directions,” IEEE Commun. Surv. Tutorials, vol. 27, no. 2, pp. 941–973, 2025

2025

[6] [6]

A survey on TOA based wireless localization and NLOS mitigation techniques,

J. A. del Peral-Rosado, R. Raulefs et al. , “A survey on TOA based wireless localization and NLOS mitigation techniques,”IEEE Commun. Surv. Tutorials, vol. 11, no. 3, pp. 107–124, 2009

2009

[7] [7]

Wi-fi fingerprint-based indoor posi- tioning: Recent advances and comparisons,

A. Shastri, N. Valecha et al. , “Wi-fi fingerprint-based indoor posi- tioning: Recent advances and comparisons,” IEEE Commun. Surv. Tutorials, vol. 18, no. 1, pp. 466–490, 2016

2016

[8] [8]

Modern WLAN fingerprinting indoor positioning methods and deployment challenges,

A. Khalajmehrabadi, N. Gatsis et al. , “Modern WLAN fingerprinting indoor positioning methods and deployment challenges,” IEEE Com- mun. Surv. Tutorials, vol. 19, no. 3, pp. 1974–2002, 2017

1974