Sensing in Low-altitude Wireless Networks: Systems, Techniques, and Developments

Hongtao Zhao; Yiming Zhao; Ying Cui; Zihao Tao; Zijun Gong

REVIEW 3 major objections 4 minor 15 references

Sensing in Low-altitude Wireless Networks: Systems, Techniques, and Developments

T0 review · 3 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A sparse radar-camera fusion method for low-altitude wireless networks detects and estimates UAV trajectories with 83.20% mAP and 0.317 ATE at 17 ms inference latency, outperforming camera-only and existing radar-camera fusion baselines…

desk verdict A sound, well-organized survey of LAWN sensing with a genuinely new but under-documented case study whose headline numbers I cannot verify. read the letter →

arxiv 2608.10555 v1 pith:KV7M3YZR submitted 2026-08-11 cs.NI

Zihao Tao , Yiming Zhao , Hongtao Zhao , Zijun Gong , Ying Cui This is my paper · ORCID

classification cs.NI

keywords low-altitudewirelessnetworkssensingsystemsmulti-modalfusionradar-cameramodel-and-data-drivensmall-RCStargetsreal-timeUAVdetectioncooperative

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

This article argues that sensing is a core component of low-altitude wireless networks (LAWNs), not an auxiliary add-on, and that existing sensing techniques need to be re-examined against the specific demands of low-altitude airspace. It builds a three-part framework: sensing system concepts, services, tasks, nodes, targets, and scenarios; a comparative analysis of RF versus optical, non-cooperative versus cooperative, model-driven versus data-driven, and single-modal versus multi-modal sensing; and a set of future research directions organized around model-and-data-driven multi-modal fusion. The paper's original contribution is a case study of a sparse radar-camera fusion method for real-time UAV sensing, reporting 83.20% mAP for detection, 0.317 absolute trajectory error, and 17 ms inference latency under a tiny-model setting, beating image-only and existing fusion baselines. A sympathetic reader would take this as evidence that lightweight, sparse multi-modal fusion is a viable path for deployable LAWN sensing.

What carries the argument

The carrying mechanism is a sparse radar-camera fusion architecture with query initialization and distance-and-velocity-aware refinement. Object queries are seeded using both image proposals and radar points, then updated by a fusion module that uses radar Doppler and spatial-distance cues to associate sparse radar points with visual queries, avoiding dense bird's-eye-view construction and reducing compute. The analytic machinery of the survey is a four-axis taxonomy—propagation medium, cooperation mode, modeling methodology, and sensing modality—that organizes the field's trade-offs and motivates the case study.

What would settle it

Rerun the proposed sparse radar-camera fusion and the four baselines on a public drone-detection dataset under the same small-model protocol; if any baseline reaches or exceeds 83.20% mAP with latency at or below 17 ms, or if the method's mAP drops below the leading baseline, the Table IV outperformance claim is refuted.

Watch

Extended reading notes

Core claim

The central discovery claimed in the case study is that sparse radar-camera fusion can deliver accurate real-time aerial-target sensing on resource-constrained platforms. Instead of building dense bird's-eye-view representations, the method initializes object queries from both image proposals and radar points and refines them through a distance-and-velocity-aware fusion mechanism, so millimeter-wave radar Doppler and spatial cues are associated with visual queries. On the paper's collected G2A dataset, the method reaches 83.20% mAP and 0.317 ATE at 17 ms latency, outperforming BEVFormer-tiny, SparseBEV-tiny, RCM-tiny, and RaCFormer-tiny under the same tiny-model setting; drone-type ablation shows the larger M350 RTK yields 95.4% mAP, and nighttime conditions drop mAP by over 20% and triple ATE. The paper positions this as a working example of the model-and-data-driven multi-modal sensing direction it recommends.

Load-bearing premise

The headline numbers in the case study assume the privately collected dataset and the small-model setup are a fair, representative comparison; the paper gives no dataset size, environment or lighting distribution, hyperparameters, error bars, or code, so a different evaluation protocol could change the ranking.

Editorial extensions

If this is right

If the case-study numbers hold, real-time UAV detection and trajectory estimation are feasible on small neural models rather than requiring large edge servers.
The nighttime results, with mAP dropping by more than 20% and ATE tripling, indicate that camera-radar fusion still has a low-light ceiling, and the paper's recommended model-and-data-driven multi-modal sensing direction targets exactly this failure mode.
The comparative analysis implies that no single sensing technique satisfies LAWN's requirements, so deployable systems will need combinations across the four axes, particularly model-driven physical priors with data-driven adaptability.
The future-research agenda ties specific bottlenecks, such as occlusion, glint ambiguity, small RCS, time-frequency misalignment, signaling overhead, and feature imbalance, to concrete techniques like long-time coherent integration, clustered cooperative architectures, and motion-compensated multi-modal alignment.

Reading between the lines

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

The paper leaves implicit that the 17 ms latency figure, if reproduced on other platforms, would move the practical bottleneck from inference speed to sensor synchronization and data transfer between radar and camera streams.
Because the case-study data are private and the baselines are not described in full detail, the specific margins in Table IV are the most fragile part of the paper; a public benchmark or an independent reproduction would be the natural next test.
The taxonomy suggests a testable extension: evaluating the same sparse-fusion design across A2A and A2G scenarios, not just G2A, since the small-RCS and clutter challenges differ.
One could combine the paper's distance-and-velocity-aware query fusion with a cooperative multi-node setup, since the survey's cooperative-sensing section implies that radar-camera fusion is currently single-node and multi-node sparse fusion is an untouched direction.

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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: the survey part is a competent, well-organized synthesis of LAWN sensing, and the case study is the only genuinely new empirical bit—and it is under-reported to the point that I can't verify the headline numbers.

What's new: the four-way taxonomy (propagation medium, cooperation, methodology, modality) is a reasonable organizing device, and the comparison tables are useful for someone entering the field. The case study method SRCF-UAV-tiny recombines known components like BEVFormer, SparseBEV, RaCFormer, and RCM-Fusion, but the specific sparse radar-camera fusion with query initialization is a new combination, and the reported mAP/ATE/latency numbers are new. Sections II–IV are coherent and cite the relevant ISAC and LAWN surveys; I don't see obvious citation gaps or a self-citation chain.

Where it's soft: Section V and Table IV. The stress-test note is spot on. The claim of "same tiny-model setting" is not established because the paper gives no dataset size, no train/validation split, no hyperparameters, no seeds, no error bars, and no hardware description for the 17 ms latency. The mAP gap of 6.88 points over the best baseline could shrink or reverse if the baselines were less tuned or the private dataset is small. ATE of 0.317 is given without units or trajectory length. The paper says the data covers different UAV types, outdoor environments, and lighting conditions, but gives no counts. This is load-bearing for contribution 4, and the survey body is not affected.

Minor: the service-level requirements in Section II (over 95% detection accuracy, decimeter-level localization, etc.) are stated without citation or derivation. Those look like target specs, not measured results.

Who this is for: readers who want a structured map of LAWN sensing techniques and open problems. The case study is a teaser, not a benchmark. I'd send it to review because the survey is useful and the case study could be fixed with releases and statistical rigor. But I'd condition acceptance on that.

Recommendation: engage with it; a serious referee should ask for dataset/code/error bars, or the case study should be trimmed to a brief illustration without the quantitative superiority claim.

Referee Report

3 major / 4 minor

Summary. This manuscript is a survey of sensing in low-altitude wireless networks (LAWN), organized around a four-dimensional taxonomy: propagation medium (RF vs. optical), cooperation mode (non-cooperative vs. cooperative), modeling methodology (model-driven vs. data-driven), and sensing modality (single-modal vs. multi-modal). Sections II and III develop the system framework and compare existing techniques; Section IV outlines future research directions; Section V presents a case study of a sparse radar-camera fusion method (SRCF-UAV-tiny) for real-time UAV detection and trajectory estimation, reporting a mean average precision of 83.20%, an absolute trajectory error of 0.317, and an inference latency of 17 ms. The abstract and introduction frame the article as a comprehensive, LAWN-focused review, with the case study serving as a demonstration of a model-and-data-driven multi-modal approach.

Significance. If the survey's taxonomic framework and the case-study results hold, the manuscript would provide a useful organizing reference for LAWN sensing, and the SRCF-UAV-tiny result would be a meaningful data point for lightweight radar-camera fusion on resource-constrained platforms. The comparative analysis in Sections II and III is clearly structured and the tables (Tables I–III) consolidate relevant information. However, the case study in Section V is the only original quantitative contribution, and its current reporting makes the central performance claim unverifiable. The paper ships no code, no dataset description, and no detailed experimental protocol, so the case study cannot currently be assessed by readers. The survey body is sound as a literature review, but the strength of the overall contribution hinges on the experimental evidence.

major comments (3)

[Section V, Table IV] The central claim that SRCF-UAV-tiny outperforms all baselines 'under the same tiny-model setting' is not verifiable from the reported information. The manuscript gives no dataset size, number of annotated frames, train/validation split, training hyperparameters (epochs, learning rate, batch size, optimizer, augmentation), number of random seeds, or error bars. Without these, the 6.88 percentage point mAP gap over RaCFormer-tiny (83.20% vs. 76.32%) could reflect unequal training budgets, test-set overfitting, or a small dataset that disadvantages data-hungry baselines. This is load-bearing for contribution 4, and the 'same setting' condition must be demonstrated by a full experimental protocol and variability measures.
[Section V, Table IV] The ATE value of 0.317 is reported without units, and the trajectory length over which it is computed is not stated. Likewise, the mAP metric is not defined with respect to IoU threshold, 3D vs. 2D detection, distance range, or size of the evaluation region. These omissions prevent external comparison of the reported numbers. Please specify the exact evaluation metrics and the data conditions under which they were measured.
[Section V, Fig. 2 and surrounding text] The method description is limited to a brief illustration: image features are extracted, radar points are encoded by a 'lightweight radar encoder,' and object queries are initialized and refined by a 'distance-and-velocity-aware fusion mechanism.' No architecture details, loss functions, training schedules, or inference hardware are provided. Because the case study is presented as one of the four contributions, this level of description is insufficient to support the claimed contribution. Either provide a complete and reproducible specification of the method, or explicitly reposition the case study as a preliminary demonstration requiring fuller validation.

minor comments (4)

[Section II.A] The service-level requirements such as 'over 95% detection accuracy' and 'a lower than 0.5% false alarm rate' are stated without citation or derivation; these appear to be target values rather than established standards, and should be sourced or rephrased as design goals.
[Abstract and throughout] There are several typographical and formatting issues, including 'LA WN' with a spurious space in the abstract, 'UA Vs' with a capital V in multiple sections, and inconsistent hyphenation in 'model-and-data-driven.' A careful proofreading pass is needed.
[Section V, final paragraph] The validation across drone types and lighting conditions is reported only in relative terms ('mAP dropping by over 20% and ATE tripling'). Please provide the actual numerical values for the M350 RTK and Mavic 3E cases, and for the day/night comparisons, so that the degradation can be assessed quantitatively.
[Section V, Fig. 2] The caption refers to a 'real-world radar-camera data collection platform,' but the figure itself is not described in sufficient detail to understand sensor placement, baseline distances, or calibration. A schematic with coordinate frames and sensor specifications would improve clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey taxonomy and case-study benchmarks are externally grounded, and no derived quantity reduces to its own inputs.

full rationale

This manuscript is a survey followed by an original case study, not a derivation chain. Sections II\u2013IV present a taxonomy of LAWN sensing (RF/optical, cooperative/non-cooperative, model/data-driven, single/multi-modal) and qualitative comparisons; these are classifications and literature summaries, not results derived from themselves. The only quantitative contribution, Section V and Table IV, reports mAP, ATE, and latency for SRCF-UAV-tiny against external baselines (BEVFormer-tiny, SparseBEV-tiny, RCM-tiny, RaCFormer-tiny) \u201cunder the same tiny-model setting.\u201d No table entry is defined in terms of the method\u2019s own output, and no fitted parameter is renamed as a prediction; the comparison is against independently named external methods. The self-citations [1] and [8] include co-author Cui, but they support background definitions of low-altitude wireless networks and hierarchical air-traffic management, not the survey\u2019s taxonomy and not the case-study numbers; removing those citations would not change any derived claim. No uniqueness theorem is imported from the authors\u2019 prior work, and no ansatz is smuggled in via citation. The reproducibility limitation that the private dataset size, hyperparameters, seeds, and latency hardware are not reported is a verifiability concern, not a circularity, because the evaluation metric is not a self-defined quantity. Therefore the analysis is self-contained and no circular step is present.

Assumptions & free parameters 1 free parameters · 3 assumptions · 0 invented entities

No new physical entities are introduced. The main numeric dependence is on the trained SRCF-UAV-tiny weights and on assumptions about data collection, sensor alignment, and evaluation fairness.

free parameters (1)

SRCF-UAV-tiny model weights = undisclosed
The reported mAP, ATE, and latency are produced by a trained network on a private dataset. Without the weights or a detailed training procedure, the central numerical comparison in Table IV cannot be reproduced or independently checked.

assumptions (3)

domain assumption Radar point clouds and RGB images can be aligned in space and time via the distance-and-velocity-aware fusion mechanism
The method's gains depend on accurate multi-modal association, but the paper does not quantify synchronization error or alignment precision.
domain assumption The RTK tracking system provides centimeter-level ground truth across all environments and lighting conditions
Section V states that RTK provides a reference, but gives no details on precision, consistency, or coverage across the collected data.
domain assumption The baselines are evaluated fairly under the same tiny-model setting
Section V asserts this, but does not provide hyperparameters, training schedules, validation protocols, or any evidence that all methods received equal effort.

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Cite this review

Pith. "Pith review of Sensing in Low-altitude Wireless Networks: Systems, Techniques, and Developments." pith.science (2026). https://pith.science/paper/KV7M3YZR

@misc{pith2026260810555,
  author       = {Pith},
  title        = {Pith review of: Sensing in Low-altitude Wireless Networks: Systems, Techniques, and Developments},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KV7M3YZR}},
  note         = {Machine review of arXiv:2608.10555}
}

read the original abstract

The highly dynamic and safety-critical characteristics of low-altitude airspace render sensing an indispensable component of low-altitude wireless networks (LAWN). Although sensing techniques have been extensively studied under diverse paradigms, a prominent mismatch persists between state-of-the-art sensing schemes and the practical sensing demands of LAWN. To fill this research gap, this article systematically reviews LAWN-oriented sensing from the dimensions of system framework, core technologies, and research trends. Specifically, we first analyze the sensing system framework, covering concepts, services and tasks, nodes and targets, and scenarios for LAWN sensing. Next, we conduct a comparative analysis of existing sensing techniques from the perspectives of propagation medium, cooperation, methodology, and modality, analyzing their advantages and limitations. Then, we summarize promising future research directions for deployable LAWN sensing systems, covering non-cooperative and cooperative sensing, model-driven and data-driven sensing, and model-and-data-driven multi-modal sensing. Finally, we present a case study of a model-and-data-driven multi-modal method for real-time aerial target sensing. Compared with existing surveys on LAWN or sensing, this article delivers a more comprehensive, targeted review exclusively centered on LAWN sensing.

Figures

Figures reproduced from arXiv: 2608.10555 by the authors.

**Figure 1.** Overview of LAWN system. edge servers, undertaking information processing, situation analysis, and route planning. Control nodes include cloud and edge servers and FCUs, realizing hierarchical management from global scheduling to onboard execution. Reference nodes include global navigation satellite system (GNSS), RTK, time, and automatic dependent surveillance-broadcast (ADS-B) reference stations, providing unifie… view at source ↗

**Figure 2.** Real-world radar-camera data collection platform and sparse radar [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

15 extracted references · 12 canonical work pages

[1]

Co-design of sensing, communications, and control for low-altitude wireless networks,

H. Jin, J. Wu, W. Yuan, F. Liu, and Y . Cui, “Co-design of sensing, communications, and control for low-altitude wireless networks,”IEEE Trans. Mobile Comput., vol. 24, no. 11, pp. 12 035–12 048, Nov. 2025

2025
[2]

Integrated sensing and communication for low altitude economy: Opportunities and challenges,

Y . Jiang, X. Li, G. Zhu, H. Li, J. Deng, K. Han, C. Shen, Q. Shi, and R. Zhang, “Integrated sensing and communication for low altitude economy: Opportunities and challenges,”IEEE Commun. Mag., vol. 63, no. 12, pp. 72–78, Dec. 2025

2025
[3]

Towards a low-altitude aerial intelligent network: Vision, challenges, and key technologies,

H. Yuhong, D. Haiyu, C. Weiyan, K. Luting, D. Wei, L. Xin, L. Yang, W. Guizhen, and L. Liang, “Towards a low-altitude aerial intelligent network: Vision, challenges, and key technologies,”China Commun., vol. 22, no. 9, pp. 1–21, Oct. 2025

work page 2025
[4]

AI-enhanced integrated sensing and communications: Ad- vancements, challenges, and prospects,

N. Wu, R. Jiang, X. Wang, L. Yang, K. Zhang, W. Yi, and A. Nal- lanathan, “AI-enhanced integrated sensing and communications: Ad- vancements, challenges, and prospects,”IEEE Commun. Mag., vol. 62, no. 9, pp. 144–150, Sep. 2024

work page 2024
[5]

ISAC-aided UA V swarms: From networked perception to capability evolution,

Y . Zhang, J. Wang, G. Du, J. Chen, J. Wang, and Q. Li, “ISAC-aided UA V swarms: From networked perception to capability evolution,”IEEE Commun. Mag., vol. 62, no. 9, pp. 60–66, Sep. 2024

work page 2024
[6]

Integrated sensing, communications, and computation services in low-altitude economy networks: A comprehensive survey,

Z. Liang, B. Hu, S. Yang, J. Du, R. Pei, and Y . Yin, “Integrated sensing, communications, and computation services in low-altitude economy networks: A comprehensive survey,” inProc. IEEE CIoTSC, Nov. 2025, pp. 1–6

work page 2025
[7]

Low-altitude wireless networks: A comprehensive survey,

W. Jun, Y . Yaoqi, Y . Weijie, L. Wenchao, W. Jiacheng, M. Tianqi, Z. Lin, C. Yuanhao, L. Fan, S. Geng, M. Yiyan, W. Nan, Z. Dezhi, X. Jindan, M. Nan, F. Zhiyong, X. Wei, D. Niyato, C. Yuen, J. Xiaojun, S. Zhiguo, A. Bo, J. Shi, D. I. Kim, W. Jiangzhou, Z. Ping, Y . Hao, and Z. Jun, “Low-altitude wireless networks: A comprehensive survey,” China Commun., ...

work page 2026
[8]

Hierarchical low-altitude wireless network empowered air traffic management,

Z. Jia, J. He, Y . Cui, Q. Zhu, L. Yuan, F. Zhou, Q. Wu, D. Niyato, and Z. Han, “Hierarchical low-altitude wireless network empowered air traffic management,”IEEE Commun. Mag., pp. 1–8, Mar. 2026

work page 2026

Show all 15 references

[9]

MIMO radar with colocated antennas,

J. Li and P. Stoica, “MIMO radar with colocated antennas,”IEEE Signal Process. Mag., vol. 24, no. 5, pp. 106–114, Sep. 2007

2007
[10]

Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and measurements,

C. B. Barneto, T. Riihonen, M. Turunen, L. Anttila, M. Fleischer, K. Sta- dius, J. Ryyn¨anen, and M. Valkama, “Full-duplex OFDM radar with LTE and 5G NR waveforms: Challenges, solutions, and measurements,”IEEE Trans. Microwave Theory Tech., vol. 67, no. 10, pp. 4042–4054, Oct. 2019

2019
[11]

Time and phase synchronization of a broadband multistatic imaging radar network using non-cooperative signals,

F. Hochberg, M. Jirousek, S. Anger, M. Peichl, and T. Zwick, “Time and phase synchronization of a broadband multistatic imaging radar network using non-cooperative signals,”IEEE Trans. Radar Syst., vol. 4, pp. 373– 386, Jan. 2026

2026
[12]

Full-duplex communication for ISAC: Joint beamforming and power optimization,

Z. He, W. Xu, H. Shen, D. W. K. Ng, Y . C. Eldar, and X. You, “Full-duplex communication for ISAC: Joint beamforming and power optimization,”IEEE J. Select. Areas Commun., vol. 41, no. 9, pp. 2920– 2936, Sep. 2023

2023
[13]

Cognitive radar network: Cooper- ative adaptive beamsteering for integrated search-and-track application,

R. A. Romero and N. A. Goodman, “Cognitive radar network: Cooper- ative adaptive beamsteering for integrated search-and-track application,” IEEE Trans. Aerosp. Electron. Syst., vol. 49, no. 2, pp. 915–931, Apr. 2013

2013
[14]

Advanced learning algorithms for integrated sensing and communication (ISAC) systems in 6G and beyond: A comprehensive survey,

N. C. Luong, T. Huynh-The, T.-H. Vu, D. Van Le, H. T. Nguyen, N. D. Hai, G.-V . Nguyen, N. D. D. Anh, D. Niyato, D. I. Kim, and Q.-V . Pham, “Advanced learning algorithms for integrated sensing and communication (ISAC) systems in 6G and beyond: A comprehensive survey,”IEEE Com...

2026
[15]

Integrating multi- modal sensors: A review of fusion techniques for intelligent vehicles,

C. Wei, Z. Qin, Z. Zhang, G. Wu, and M. J. Barth, “Integrating multi- modal sensors: A review of fusion techniques for intelligent vehicles,” inProc. IEEE IV, Jun. 2025, pp. 1817–1824. BIOGRAPHIES Zihao Tao(ztao341@connect.hkust-gz.edu.cn) is currently pursuing his Ph.D. degre...

2025

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Co-design of sensing, communications, and control for low-altitude wireless networks,

H. Jin, J. Wu, W. Yuan, F. Liu, and Y . Cui, “Co-design of sensing, communications, and control for low-altitude wireless networks,”IEEE Trans. Mobile Comput., vol. 24, no. 11, pp. 12 035–12 048, Nov. 2025

2025

[2] [2]

Integrated sensing and communication for low altitude economy: Opportunities and challenges,

Y . Jiang, X. Li, G. Zhu, H. Li, J. Deng, K. Han, C. Shen, Q. Shi, and R. Zhang, “Integrated sensing and communication for low altitude economy: Opportunities and challenges,”IEEE Commun. Mag., vol. 63, no. 12, pp. 72–78, Dec. 2025

2025

[3] [3]

Towards a low-altitude aerial intelligent network: Vision, challenges, and key technologies,

H. Yuhong, D. Haiyu, C. Weiyan, K. Luting, D. Wei, L. Xin, L. Yang, W. Guizhen, and L. Liang, “Towards a low-altitude aerial intelligent network: Vision, challenges, and key technologies,”China Commun., vol. 22, no. 9, pp. 1–21, Oct. 2025

work page 2025

[4] [4]

AI-enhanced integrated sensing and communications: Ad- vancements, challenges, and prospects,

N. Wu, R. Jiang, X. Wang, L. Yang, K. Zhang, W. Yi, and A. Nal- lanathan, “AI-enhanced integrated sensing and communications: Ad- vancements, challenges, and prospects,”IEEE Commun. Mag., vol. 62, no. 9, pp. 144–150, Sep. 2024

work page 2024

[5] [5]

ISAC-aided UA V swarms: From networked perception to capability evolution,

Y . Zhang, J. Wang, G. Du, J. Chen, J. Wang, and Q. Li, “ISAC-aided UA V swarms: From networked perception to capability evolution,”IEEE Commun. Mag., vol. 62, no. 9, pp. 60–66, Sep. 2024

work page 2024

[6] [6]

Integrated sensing, communications, and computation services in low-altitude economy networks: A comprehensive survey,

Z. Liang, B. Hu, S. Yang, J. Du, R. Pei, and Y . Yin, “Integrated sensing, communications, and computation services in low-altitude economy networks: A comprehensive survey,” inProc. IEEE CIoTSC, Nov. 2025, pp. 1–6

work page 2025

[7] [7]

Low-altitude wireless networks: A comprehensive survey,

W. Jun, Y . Yaoqi, Y . Weijie, L. Wenchao, W. Jiacheng, M. Tianqi, Z. Lin, C. Yuanhao, L. Fan, S. Geng, M. Yiyan, W. Nan, Z. Dezhi, X. Jindan, M. Nan, F. Zhiyong, X. Wei, D. Niyato, C. Yuen, J. Xiaojun, S. Zhiguo, A. Bo, J. Shi, D. I. Kim, W. Jiangzhou, Z. Ping, Y . Hao, and Z. Jun, “Low-altitude wireless networks: A comprehensive survey,” China Commun., ...

work page 2026

[8] [8]

Hierarchical low-altitude wireless network empowered air traffic management,

Z. Jia, J. He, Y . Cui, Q. Zhu, L. Yuan, F. Zhou, Q. Wu, D. Niyato, and Z. Han, “Hierarchical low-altitude wireless network empowered air traffic management,”IEEE Commun. Mag., pp. 1–8, Mar. 2026

work page 2026