Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition

Guangyu Lei; Huahao Ding; Junwei Wu; Longyu Zhou; Qinyu Zhang; Tianhao Liang; Tingting Zhang; Weijie Yuan; Xingjian Zhang; Xinglin Chen

REVIEW 4 major objections 4 minor 20 references

Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A cloud MLLM reads sensor tracks and names drone threats.

desk verdict A clearly written position/architecture paper whose central feasibility claim rests on a single unvalidated demo; the survey and architecture are useful, but the evidence is not there yet. read the letter →

arxiv 2509.06312 v1 pith:ADI7OH2K submitted 2025-09-08 eess.SY cs.LGcs.SY

Guangyu Lei , Tianhao Liang , Yuqi Ping , Xinglin Chen , Longyu Zhou , Junwei Wu , Xiyuan Zhang , Huahao Ding

show 4 more authors

Xingjian Zhang Weijie Yuan Tingting Zhang Qinyu Zhang

This is my paper · ORCID

classification eess.SYcs.LGcs.SY

keywords UAVintentrecognitionmultimodallargelanguagemodelsfusionperceptionlow-altitudeairspacesecuritythreatassessmentpromptengineeringdronedefense

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 paper proposes a system in which a multimodal large language model (MLLM) performs intent recognition of non-cooperative drones by combining structured perception data—payload and motion information from cameras, radar, and infrared—with environmental context, prior knowledge, and tactical preferences supplied in a prompt. The authors argue that this generative, reasoning-based approach overcomes the rigidity of traditional classifiers that rely on fixed label sets and complete inputs. A simulated low-altitude confrontation shows the MLLM labeling each drone's likely intent, assigning threat levels with self-reported confidence, and recommending defensive actions. If the claim holds, low-altitude defense systems could turn existing sensor tracks into actionable intent labels and decision support without task-specific retraining.

What carries the argument

The carrying mechanism is the structured-input-plus-prompt pipeline that bridges sensor fusion and generative reasoning. Onboard visual and radar processing (a real-time detection model for payload and class, radar tracking for position and velocity) compresses raw multimodal data into compact structured records, with images sent to the cloud only when the visual classifier is uncertain. The MLLM then reads these records together with scene information and mission instructions in the prompt, using chain-of-thought reasoning to produce per-target intent labels, pairwise formation insights, and action recommendations. This design is what lets the same model adapt to different tactical preferences and mission types without retraining.

What would settle it

Take the same structured inputs used in the low-altitude use case (payload, position, velocity, RCS, time) and run them through the MLLM with the true simulated intent of each drone withheld; if the model's predicted intent labels and threat levels match the ground truth at or below the accuracy of a simple nearest-neighbor classifier on the same features, the claim that MLLMs add intent-recognition capability would be falsified. A second check: vary the prompt wording while holding the input data fixed; if the recognized intent changes with phrasing, the recognition is driven by language priors rather than sensor evidence.

Watch

Extended reading notes

Core claim

The paper's central claim is that an MLLM can serve as the intent-recognition core of a low-altitude airspace defense system, accepting structured multimodal observations and producing intent labels, threat assessments, and suggested responses. The authors build this around an architecture with two modules: a multimodal perception front end that fuses RGB, infrared, and radar data into compact structured records, and an MLLM inference module that reasons over those records with prompts encoding environment, prior knowledge, and tactical preferences. In the use case, the MLLM analyzes two adversary drones, infers 'armed probing' and 'ISR' intents, estimates high and medium threat levels with confidence 0.85 and 0.80, predicts that one drone is scouting for the other, and outputs a five-step defensive action sequence. The demonstration is offered as evidence of feasibility, not as a benchmarked evaluation.

Load-bearing premise

The load-bearing premise is that the MLLM's natural-language outputs and self-reported confidence scores are accepted as valid intent recognition, even though the use case provides no ground-truth labels or comparison to known behaviors; if those outputs are not actually correct, the demonstration shows only that the model can produce fluent text about drones.

Editorial extensions

If this is right

A single MLLM could replace task-specific classifiers across different low-altitude missions by changing only the prompt, not the model weights.
Structured transmission from edge sensors to a cloud MLLM reduces communication load compared to streaming raw images and point clouds, making real-time intent recognition feasible with limited links.
The MLLM's free-text rationale and step-by-step recommendations can feed directly into human operator displays and downstream scheduling systems, closing the loop from perception to action.
Because the model can name intents not in a fixed label set, it may handle novel or adversarial behaviors that supervised classifiers would fail to recognize.

Reading between the lines

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

The architecture as presented treats the MLLM's self-reported confidence as a meaningful threat probability; a natural extension would be to calibrate these outputs against labeled flight data, since LLM confidence scores are not inherently calibrated.
The same structured-input design could be applied beyond defense—for example, airspace traffic management for delivery drones, where intent labels (landing, loitering, returning) could support deconfliction.
The paper's feasibility demonstration does not compare against a baseline classifier; a head-to-head test with supervised intent recognition on the same sensor records would clarify where generative reasoning adds value.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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

First, what you should know: this is a position/architecture paper, not a results paper. The architecture is coherent and the writing is clear. But the central claim—that an MLLM can perform UAV intent recognition—is supported only by a single illustrative demo with no ground truth, baseline, or metric. The paper does not establish the capability it advertises in the title.

What it does well: the proposed pipeline is sensible. The authors suggest turning raw sensor data into structured text (IDs, positions, velocities, payload labels) and only pushing full images to the cloud when the onboard classifier is uncertain. That addresses a real constraint: bandwidth and inference cost. The related-work review is organized and useful, explicitly mapping prior work onto the proposed architecture. The AirSim demo is a nice illustration of the concept—it shows an MLLM can produce contextually relevant reasoning from structured input. The paper is honest that this is a use case, not a systematic study.

Where it falls short: the feasibility claim in Section IV.C rests entirely on the demo in Figure 3. The model's threat assessments and confidence scores (0.85, 0.80) are self-reports; nothing checks them against the actual scripted behaviors in AirSim. Since the simulation defines the ground truth, adding a simple comparison is straightforward, and its absence is glaring. Without that, the paper shows fluent text generation, not intent recognition. A second issue is novelty: the overlap with [8] and [19] is substantial. This paper's contribution is the synthesis and the demo, which is fine for a position paper, but it should not be sold as a new result. Minor issues: Table I's reference numbers don't match the bibliography in places, and there are typos in the figures.

Who should read it: anyone working on low-altitude defense or MLLM-based sensing who wants a compact map of the design space and an illustrated example. It is not evidence that the approach works.

My recommendation on review: send it out. A good referee can help the authors add the missing evaluation, and the architecture plus survey are worth preserving. But accept only conditionally on a real validation.

Referee Report

4 major / 4 minor

Summary. The manuscript proposes an MLLM-enabled UAV intent recognition architecture that couples a multimodal UAV perception module (RGB, infrared, radar, and fusion) with a cloud-hosted MLLM inference module. It surveys recent work on multimodal UAV perception and intent recognition, then demonstrates the architecture in an AirSim low-altitude confrontation scenario in which structured perception data, an RGB key frame, and prior/tactical context are sent to Qwen-vl-plus, which outputs per-target intent assessments, threat levels, confidence values, and recommended actions. The authors claim this use case establishes feasibility and discuss five future challenges. The paper does not present a derivation, a fitted model, or a quantitative evaluation of the recognition output.

Significance. The architectural concept is coherent and addresses a real operational bottleneck: it reduces raw-sensor transmission by fusing structured perception data and key frames, and prompt-based control provides flexible adaptation to tactical preferences. The related-work table and narrative give a useful entry point to RGB/IR/radar perception and recent intent-recognition methods. If the central feasibility claim were backed by a ground-truth comparison, baselines, and calibrated confidence, the paper would be a valuable early demonstration of MLLM-based intent recognition for low-altitude defense. As it stands, the contribution is primarily a proposal plus an anecdotal use case; the architectural components are not themselves new, and the claimed recognition capability is unsupported.

major comments (4)

[Section IV.C, Figure 3] The paper's central claim, stated in Section IV.C, is that the MLLM 'can infer the likely intentions, assess the potential threat level of each UAV, and predict their subsequent maneuvers.' The only evidence offered is the single qualitative output in Figure 3. There is no ground-truth labeling, no baseline algorithm, no accuracy metric, and no repeated or varied scenario. Because the AirSim adversary behaviors are scripted, their true intents are known and a quantitative evaluation is directly available; its absence means the use case demonstrates only that an MLLM can produce fluent, contextually plausible text from structured inputs, not that it performs intent recognition correctly.
[Section IV.C, Figure 3] The confidence values 0.85 and 0.80 in Figure 3 are model self-reports and are presented as if they measure recognition reliability. No calibration experiment or comparison of these values with actual classification accuracy is provided, and MLLM self-reported confidence is not a valid error estimate without such calibration. The authors should either validate these scores against ground truth or explicitly label them as uncalibrated qualitative outputs.
[Section III.B vs. Section IV.C] No comparison is made between the MLLM and any of the intent-recognition methods reviewed in Section III.B, such as GAT/AP/LSTM, CNN-GAF/Transformer, or the generative token-based method. The assertion that MLLMs overcome the scalability and flexibility limitations of prior methods is therefore not empirically supported. A comparative evaluation on the same simulated scenario, ideally including a rule-based sanity check, is needed to justify the claimed advantage.
[Section IV.A] The structured-input pipeline is a load-bearing component of the architecture, but the use case does not report detection or tracking accuracy for the perception outputs fed to the MLLM. The visual detection confidence 0.78 shown in Figure 3 is not verified, and the proposed key-frame transmission strategy is not evaluated. This matters because recognition errors caused by inaccurate perception inputs cannot be separated from recognition errors of the MLLM without such measurements.

minor comments (4)

[Table I] The reference numbers in Table I are inconsistent with the text. For example, the YOLOv7/ByteTrack system is cited as Ref [12] in Section III.A but as Ref [8] in Table I, and the ViT classifier is Ref [16] in the text but Ref [12] in the table. These correspondences should be corrected.
[Section IV, footnote 2] The footnote stating that only the RGB camera is used because the AirSim lighting is ideal should be moved into the main evaluation and acknowledged as a limitation on the multimodal claim; as written, the demonstration does not exercise the infrared modality even though the architecture emphasizes multimodal fusion.
[Section II] The sentence ending 'will be provided as follows 1.' in Section II should be completed and punctuated properly, and the manuscript contains several typographical artifacts such as 'UA V' with a space in the title and running text.
[Section IV.A and Figure 2] The caption of Figure 2 lists 'YOLO' as part of the perception pipeline, but the text in Section IV.A refers only to 'embedded visual classification models'; the specific detector should be named and cited for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper's use case is a qualitative demonstration, not a derivation whose outputs reduce to its inputs.

full rationale

The paper contains no fitted parameters, no equations whose predictions are defined by their inputs, and no load-bearing citation chain. The central feasibility claim rests on a single use case in which structured perception data are fed to Qwen-vl-plus and its natural-language output is presented as intent recognition. That output is unvalidated against ground truth and lacks baselines, but this is a validation limitation, not circularity: the MLLM is an external black-box model, and no parameter is tuned to force the displayed result. The self-citation [8] used to motivate MLLM capabilities is accompanied by an external reference [9] and by the paper's own demonstration, so the central claim does not reduce to an unverified self-citation. The architecture is presented as a proposal and review rather than a derived result, so no step is self-definitional or equivalent to its inputs by construction.

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

The central claim rests on three domain assumptions: simulation fidelity, information sufficiency of structured inputs, and the validity of unvalidated LLM outputs. No free parameters are fitted and no invented entities are introduced.

assumptions (3)

domain assumption The AirSim simulation with ideal lighting (RGB only) is representative of real low-altitude UAV confrontation scenarios.
The use case in Section IV is run in AirSim under ideal lighting and the authors note only RGB is used; no weather, night, or interference conditions are tested.
domain assumption Structured feature vectors (ID, position, velocity, RCS, payload label) preserve enough information for valid intent inference; downsampling raw images does not remove critical cues.
Section IV.A proposes structured data and key frame fusion to reduce communication load; the sufficiency of this compression for intent recognition is assumed, not measured.
domain assumption Qwen-vl-plus's natural language reasoning and self-reported confidence correctly reflect the true intent of simulated adversary UAVs.
Section IV.C treats the MLLM output (threat level, confidence 0.85 and 0.80) as valid intent recognition without ground-truth comparison.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition." pith.science (2026). https://pith.science/paper/ADI7OH2K

@misc{pith2026250906312,
  author       = {Pith},
  title        = {Pith review of: Enhancing Low-Altitude Airspace Security: MLLM-Enabled UAV Intent Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ADI7OH2K}},
  note         = {Machine review of arXiv:2509.06312}
}

read the original abstract

The rapid development of the low-altitude economy emphasizes the critical need for effective perception and intent recognition of non-cooperative unmanned aerial vehicles (UAVs). The advanced generative reasoning capabilities of multimodal large language models (MLLMs) present a promising approach in such tasks. In this paper, we focus on the combination of UAV intent recognition and the MLLMs. Specifically, we first present an MLLM-enabled UAV intent recognition architecture, where the multimodal perception system is utilized to obtain real-time payload and motion information of UAVs, generating structured input information, and MLLM outputs intent recognition results by incorporating environmental information, prior knowledge, and tactical preferences. Subsequently, we review the related work and demonstrate their progress within the proposed architecture. Then, a use case for low-altitude confrontation is conducted to demonstrate the feasibility of our architecture and offer valuable insights for practical system design. Finally, the future challenges are discussed, followed by corresponding strategic recommendations for further applications.

Figures

Figures reproduced from arXiv: 2509.06312 by the authors.

**Figure 2.** The system architecture of the use case. Important pr [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** The demonstration of use case, by inputting visual re [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

20 extracted references · 13 canonical work pages

[8]

Multimodal Large Language Models- Enabled UA V Swarm: Towards Efﬁcient and Intelligent Autono mous Aerial Systems,

Y . Ping, T. Liang, H. Ding, G. Lei, J. Wu, X. Zou, K. Shi, R. S hao, C. Zhang, W. Zhang et al. , “Multimodal Large Language Models- Enabled UA V Swarm: Towards Efﬁcient and Intelligent Autono mous Aerial Systems,” arXiv preprint arXiv:2506.12710 , 2025

arXiv 2025
[19]

From Behavior to Natural Language: Generative Approach for Unmanned Aerial V ehicle Intent Recognition,

L. Li, R. Y ang, M. Lv, A. Wu, and Z. Zhao, “From Behavior to Natural Language: Generative Approach for Unmanned Aerial V ehicle Intent Recognition,” IEEE Transactions on Artiﬁcial Intelligence , vol. 5, no. 12, pp. 6196–6209, 2024

work page 2024
[1]

Toward Seamless Locali zation and Communication: A Satellite-UA V NTN Architecture,

T. Liang, T. Zhang, and Q. Zhang, “Toward Seamless Locali zation and Communication: A Satellite-UA V NTN Architecture,” IEEE Network , vol. 38, no. 4, pp. 103–110, 2024

2024
[2]

From Ground to Sky: Architectures, Ap- plications, and Challenges Shaping Low-Altitude Wireless Networks,

W. Y uan, Y . Cui, J. Wang, F. Liu, G. Sun, T. Xiang, J. Xu, S. J in, D. Niyato, S. Coleri et al. , “From Ground to Sky: Architectures, Ap- plications, and Challenges Shaping Low-Altitude Wireless Networks,” arXiv preprint arXiv:2506.12308 , 2025

arXiv 2025
[3]

UA V -Aided Localization and Communication: Joint Frame Structure, Be amwidth, and Power Allocation,

T. Liang, T. Zhang, S. Zhou, W. Liu, D. Li, and Q. Zhang, “UA V -Aided Localization and Communication: Joint Frame Structure, Be amwidth, and Power Allocation,” IEEE Journal of Selected Areas in Sensors , vol. 1, pp. 154–165, 2024

2024
[4]

Age of Information Based Scheduling for UA V Aided Localiza tion and Communication,

T. Liang, T. Zhang, Q. Wu, W. Liu, D. Li, Z. Xie, D. Li, and Q. Zhang, “Age of Information Based Scheduling for UA V Aided Localiza tion and Communication,” IEEE Transactions on Wireless Communications , vol. 23, no. 5, pp. 4610–4626, 2024

2024
[5]

UA V -Ai ded Positioning Systems for Ground Devices: Fundamental Limit s and Algorithms,

T. Liang, T. Zhang, J. Y ang, D. Feng, and Q. Zhang, “UA V -Ai ded Positioning Systems for Ground Devices: Fundamental Limit s and Algorithms,” IEEE Internet of Things Journal , vol. 9, no. 15, pp. 13 470–13 485, 2022

work page 2022
[6]

Sensing, Commun ication, and Control Co-Design for Energy-Efﬁcient UA V -Aided Data C ollec- tion,

T. Liang, T. Zhang, B. Cao, and Q. Zhang, “Sensing, Commun ication, and Control Co-Design for Energy-Efﬁcient UA V -Aided Data C ollec- tion,” IEEE Wireless Communications Letters, vol. 13, no. 10, pp. 2852– 2856, 2024

2024

Show all 20 references

[7]

Generative AI for Integrated Sensing and Communication: I nsights From the Physical Layer Perspective,

J. Wang, H. Du, D. Niyato, J. Kang, S. Cui, X. Shen, and P . Zh ang, “Generative AI for Integrated Sensing and Communication: I nsights From the Physical Layer Perspective,” IEEE Wireless Communications, vol. 31, no. 5, pp. 246–255, 2024

2024
[9]

Large La nguage Models Empower Multimodal Integrated Sensing and Communic ation,

L. Cheng, H. Zhang, B. Di, D. Niyato, and L. Song, “Large La nguage Models Empower Multimodal Integrated Sensing and Communic ation,” IEEE Communications Magazine , vol. 63, no. 5, pp. 190–197, 2025

2025
[10]

Y ou Only Look Once: Uniﬁed, Real-Time Object Detection,

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “Y ou Only Look Once: Uniﬁed, Real-Time Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 779– 788

2016
[11]

Anti-UA V: A Large-Scale Benchmark for Vision-Based UA V Tracking,

N. Jiang, K. Wang, X. Peng, X. Y u, Q. Wang, J. Xing, G. Li, G . Guo, Q. Y e, J. Jiao, J. Zhao, and Z. Han, “Anti-UA V: A Large-Scale Benchmark for Vision-Based UA V Tracking,” IEEE Transactions on Multimedia, vol. 25, pp. 486–500, 2023

2023
[12]

Local Point Matching f or Collab- orative Image Registration and RGBT Anti-UA V Tracking,

J. Y u, T. Xu, X. Zhu, and X.-J. Wu, “Local Point Matching f or Collab- orative Image Registration and RGBT Anti-UA V Tracking,” in Pattern Recognition and Computer Vision , Z. Lin, M.-M. Cheng, R. He, K. Ubul, W. Silamu, H. Zha, J. Zhou, and C.-L. Liu, Eds. Singap ore: Spring...

2025
[13]

Multi-Modal UA V Detection, Classiﬁcation and Tracking Algorithm – Technical Report fo r CVPR 2024 UG2 Challenge,

T. Deng, Y . Zhou, W. Wu, M. Li, J. Huang, S. Liu, Y . Song, H. Zuo, Y . Wang, Y . Y ue, H. Wang, and W. Chen, “Multi-Modal UA V Detection, Classiﬁcation and Tracking Algorithm – Technical Report fo r CVPR 2024 UG2 Challenge,” 2024

2024
[14]

A V ehicle-Moun ted Radar-Vision System for Precisely Positioning Clustering UA Vs,

G. Wu, F. Zhou, K. Kit Wong, and X.-Y . Li, “A V ehicle-Moun ted Radar-Vision System for Precisely Positioning Clustering UA Vs,”IEEE Journal on Selected Areas in Communications , vol. 42, no. 10, pp. 2688–2703, 2024

2024
[15]

In frared and Visible Camera Integration for Detection and Tracking of Sm all UA Vs: Systematic Evaluation,

A. Pereira, S. Warwick, A. Moutinho, and A. Suleman, “In frared and Visible Camera Integration for Detection and Tracking of Sm all UA Vs: Systematic Evaluation,” Drones, vol. 8, no. 11, 2024

2024
[16]

Distinguishing Mal icious Drones Using Vision Transformer,

S. Jamil, M. S. Abbas, and A. M. Roy, “Distinguishing Mal icious Drones Using Vision Transformer,” AI, vol. 3, no. 2, pp. 260–273, 2022

2022
[17]

Aerial Intruder Interception Based on Threat Classiﬁcation for Enhanced Situational Awareness,

E. Khanapuri, A. Chakraborty, R. Thayer, J. Ammapalli, J. Liu, S. W. Lin, and J. Y adegar, “Aerial Intruder Interception Based on Threat Classiﬁcation for Enhanced Situational Awareness,” in 2025 IEEE/ION Position, Location and Navigation Symposium (PLANS) , 2025, pp. 1488–1499

2025
[18]

An Intent Re cognition Method for Aerial Swarm Based on Attention Pooling Mechanis m,

H. He, Z. Peng, P . Shang, W. Wang, and X. Pei, “An Intent Re cognition Method for Aerial Swarm Based on Attention Pooling Mechanis m,” Journal of Advanced Computational Intelligence and Intell igent Infor- matics, vol. 29, no. 1, pp. 5–11, 2025

2025
[20]

Low-Com plexity Channel Estimation in OTFS Systems With Fractional Effects ,

G. Lei, Y . Qiao, T. Liang, W. Y uan, and T. Zhang, “Low-Com plexity Channel Estimation in OTFS Systems With Fractional Effects ,” IEEE Wireless Communications Letters , pp. 1–1, 2025

2025

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [8]

Multimodal Large Language Models- Enabled UA V Swarm: Towards Efﬁcient and Intelligent Autono mous Aerial Systems,

Y . Ping, T. Liang, H. Ding, G. Lei, J. Wu, X. Zou, K. Shi, R. S hao, C. Zhang, W. Zhang et al. , “Multimodal Large Language Models- Enabled UA V Swarm: Towards Efﬁcient and Intelligent Autono mous Aerial Systems,” arXiv preprint arXiv:2506.12710 , 2025

arXiv 2025

[2] [19]

From Behavior to Natural Language: Generative Approach for Unmanned Aerial V ehicle Intent Recognition,

L. Li, R. Y ang, M. Lv, A. Wu, and Z. Zhao, “From Behavior to Natural Language: Generative Approach for Unmanned Aerial V ehicle Intent Recognition,” IEEE Transactions on Artiﬁcial Intelligence , vol. 5, no. 12, pp. 6196–6209, 2024

work page 2024

[3] [1]

Toward Seamless Locali zation and Communication: A Satellite-UA V NTN Architecture,

T. Liang, T. Zhang, and Q. Zhang, “Toward Seamless Locali zation and Communication: A Satellite-UA V NTN Architecture,” IEEE Network , vol. 38, no. 4, pp. 103–110, 2024

2024

[4] [2]

From Ground to Sky: Architectures, Ap- plications, and Challenges Shaping Low-Altitude Wireless Networks,

W. Y uan, Y . Cui, J. Wang, F. Liu, G. Sun, T. Xiang, J. Xu, S. J in, D. Niyato, S. Coleri et al. , “From Ground to Sky: Architectures, Ap- plications, and Challenges Shaping Low-Altitude Wireless Networks,” arXiv preprint arXiv:2506.12308 , 2025

arXiv 2025

[5] [3]

UA V -Aided Localization and Communication: Joint Frame Structure, Be amwidth, and Power Allocation,

T. Liang, T. Zhang, S. Zhou, W. Liu, D. Li, and Q. Zhang, “UA V -Aided Localization and Communication: Joint Frame Structure, Be amwidth, and Power Allocation,” IEEE Journal of Selected Areas in Sensors , vol. 1, pp. 154–165, 2024

2024

[6] [4]

Age of Information Based Scheduling for UA V Aided Localiza tion and Communication,

T. Liang, T. Zhang, Q. Wu, W. Liu, D. Li, Z. Xie, D. Li, and Q. Zhang, “Age of Information Based Scheduling for UA V Aided Localiza tion and Communication,” IEEE Transactions on Wireless Communications , vol. 23, no. 5, pp. 4610–4626, 2024

2024

[7] [5]

UA V -Ai ded Positioning Systems for Ground Devices: Fundamental Limit s and Algorithms,

T. Liang, T. Zhang, J. Y ang, D. Feng, and Q. Zhang, “UA V -Ai ded Positioning Systems for Ground Devices: Fundamental Limit s and Algorithms,” IEEE Internet of Things Journal , vol. 9, no. 15, pp. 13 470–13 485, 2022

work page 2022

[8] [6]

Sensing, Commun ication, and Control Co-Design for Energy-Efﬁcient UA V -Aided Data C ollec- tion,

T. Liang, T. Zhang, B. Cao, and Q. Zhang, “Sensing, Commun ication, and Control Co-Design for Energy-Efﬁcient UA V -Aided Data C ollec- tion,” IEEE Wireless Communications Letters, vol. 13, no. 10, pp. 2852– 2856, 2024

2024