Pith. sign in

REVIEW 6 major objections 5 minor 169 references

Sustainable Air-Ground Integrated Coverage Networks: ISCC Architecture, Technologies, and Testbed

T0 review · 6 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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

Pith's one-line read Air-ground coverage networks can be made sustainable by closing an integrated sensing, communication, and computation loop that adapts base-station beams in real time, and a field test shows this cuts radio power by about 20% while keeping

desk verdict The Zigong testbed is a real contribution, but the 20% power-saving number is a RAN-level figure that excludes the ISCC loop's own sensing, computation, and learning overhead; the paper deserves peer review but not a clean pass. read the letter →

arxiv 2607.16812 v1 pith:BAQNMZTQ submitted 2026-07-18 cs.IT cs.NImath.IT

J. Liu , X. Zhang , M. Sheng , R. Zhang , N. Zhao , J. Wang , J. Li This is my paper
classification cs.ITcs.NImath.IT
keywords Air-groundintegratedcoveragenetworkssensingcommunicationandcomputationSustainability6GDeepreinforcementlearningCoordinatedmultipointtransmissionBeamformingCarbonemissions
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 tries to establish that air-ground integrated coverage networks—networks that merge terrestrial base stations with aerial platforms—can be made sustainable by embedding them in an integrated sensing, communication, and computation (ISCC) control loop. The loop senses the positions and channel states of UAVs, forwards that state to edge servers, uses a deep reinforcement learning agent to choose beam patterns and adjust antenna tilts, and executes the decisions through triangulation-based coordinated multipoint transmission. The authors argue that this converts inter-cell interference into useful signal, allowing transmit power to be cut without losing coverage. They report a real-world testbed in which the approach reduces radio access network power consumption by roughly 20% to 27% at altitudes of 100–300 m while maintaining spatial signal coverage probability above 90%. If true, this would give 6G and low-altitude economy networks a practical path toward lower carbon emissions.

What carries the argument

The ISCC closed-loop control architecture is the central mechanism. It consists of five steps: holistic sensing, state forwarding, decision making, closed-loop execution, and learning and evolution. The decision-making step is carried out by a hierarchical DRL agent that combines discrete beam-pattern selection (from four standard high-altitude patterns) with continuous fine-tuning of tilt and beamwidth. The execution step uses triangulation-based coordinated multipoint transmission: Delaunay triangulation groups base stations into triangular clusters, forming a triangular-prism coverage region, which reduces spatial coverage overlap and turns interference into useful signal. The loop is tim

What would settle it

Conduct a flight test with the same base stations and UAVs, but meter total system power—including the UAVs' propulsion and onboard sensors, the edge servers running the DRL agent, and any added sensing/communication overhead—and compare the ISCC-controlled run against the static-beam baseline. If total energy per covered area is not lower under the ISCC loop, the paper's central efficiency claim collapses. A single route where overall power goes up while RAN power goes down would falsify it.

Watch

Extended reading notes

Core claim

The paper's central claim is that the ISCC-driven architecture—a closed loop that integrates sensing, communication, and computation—can make air-ground integrated coverage networks sustainable without sacrificing coverage. In this architecture, a sensing subsystem tracks UAV position, battery, and received signal quality, along with network state; the communication subsystem carries that data to edge servers; the computation subsystem runs a hierarchical deep reinforcement learning agent that selects one of four standard high-altitude beam patterns and then fine-tunes the tilt and beamwidths within small ranges; and the loop is closed when base stations execute the decisions through triangu

Load-bearing premise

The load-bearing premise is that the energy model used to compute the power saving—covering only the radio access network's transmission and processing power—is representative of the system's true energy footprint, excluding UAV propulsion, computing, sensing, and the learned-control overhead that the ISCC loop adds.

Editorial extensions

If this is right

  • Radio access power can be cut by roughly a fifth to a quarter at common low-altitude flight levels without dropping below 90% coverage, so energy savings hold across typical UAV operating altitudes.
  • Triangulation-based CoMP turns inter-cell interference into a cooperative gain, which is why beam adaptation can reduce transmit power without coverage loss.
  • The hierarchical DRL agent handles the hybrid discrete/continuous action space of real beam management, enabling practical fine-grained control on existing antenna hardware.
  • The scalability analysis indicates that using Delaunay triangulation and multi-agent clustering can keep interference management near-linear in the number of base stations, so the ISCC approach is not limited to the 17-BS testbed.
  • The three-layer sustainability metrics (operational efficiency, task-oriented, full life-cycle) provide a way to compare designs on carbon intensity, not just energy efficiency, which matters when electricity comes from high-carbon sources.

Reading between the lines

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

  • The reported power saving is for the radio access network only; if the energy of UAV propulsion, onboard sensors, edge servers, and the learning loop itself were included, the net saving could be smaller or negative, so a total-energy comparison would be a natural next test.
  • Because the method only requires selecting among four standard beam patterns and small angle adjustments, it could likely be implemented on current 5G base stations with software changes, which would make the path to deployment short.
  • The same closed-loop logic—sense, decide, execute, learn—could be transferred to terrestrial-only networks or space-air-ground integrated networks, wherever beam steering and interference coordination are the dominant energy levers.
  • The testbed results come from one region with specific terrain; the paper argues the adaptive loop should generalize, but the measured percentages are environment-specific, and a multi-site trial would be needed to confirm.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

6 major / 5 minor

Summary. The paper proposes an ISCC-driven architecture for sustainable air-ground integrated coverage networks (AGICNs), defines a multi-dimensional sustainability metric family, reviews enabling technologies (AI, beamforming, ISAC, SWIPT), and reports a real-world testbed in Zigong, Sichuan, China. The testbed uses ten 5G SA and seven NSA base stations plus multirotor UAVs flying at 100/200/300 m, with a DRL agent adjusting beam patterns and downtilt under triangulation-based CoMP. The principal experimental claim is a 20.8%–27.25% reduction in power consumption against a static 5G NR baseline while maintaining spatial signal coverage probability (SSCP) above 90%.

Significance. If the net system-level energy claim is substantiated, the testbed would be a valuable demonstration of closed-loop ISCC for green AGICNs, and the paper's synthesis of sustainability metrics and enabling technologies is useful. The authors are honest about the RAN-level scope in Eq. (1) and Table I, and the five-flight-repeat methodology is a reasonable effort at statistical robustness. However, because the reported power savings exclude ISCC-loop overhead and the >90% coverage target is hardwired into the DRL reward, the headline result is not yet established as a system-level sustainability gain.

major comments (6)
  1. [Sec. V-B, Eq. (9)] The optimization objective is internally inconsistent. P1 in Eq. (9) maximizes SSCP subject only to tilt bounds, while the text states 'We formulate energy minimization as the optimization objective while enforcing UAV's RSRP as a constraint.' These are different problems, and the paper does not specify how transmit power is set or how the reported power reductions are computed. Please clarify the actual objective, constraints, and power model used in the testbed; without this, the 20–27% power-reduction numbers cannot be attributed to the proposed method.
  2. [Sec. V-A, V-C, Table III] The coverage result is partly self-fulfilling. Section V-A states that 'a positive reward is obtained only if SSCP exceeds 90%.' The DRL agent is therefore trained to exceed 90%, and Table III reports all SSCP values between 91.44% and 98.67%. Reporting these values as a measure of achieved coverage is circular unless the baseline coverage and the distribution across the five repetitions are also reported. Please include error bars/confidence intervals and compare SSCP against the baseline under the same metric.
  3. [Sec. V-C vs. Eq. (1) and Table I] The claimed power reductions of 20.8%, 27.25%, and 25.48% are not net system-level savings. The paper's own REE metric (Eq. (1)) includes UAV propulsion, communication, and computing energy, but the testbed appears to compare only ground BS transmit/beamforming power. The ISCC loop adds sensing (GPS/IMU/LiDAR, 0.05–50 W in Table I), edge inference (10–100 W), three Dell R750 edge servers, and state-forwarding/control overhead. Section III-C argues these are offset, but no offset calculation or measurement is provided. Please either provide a net system-level energy balance or explicitly scope the claim to RAN transmit power.
  4. [Sec. V-C] The baseline is not sufficiently specified. The text says the baseline uses 'a set of predefined beam configurations' from Table II with 'fixed beam pattern selected ... without dynamic adaptation,' but it does not state how the pattern was chosen per trajectory segment, what transmit power and tilt were used, or whether the baseline was tuned to achieve the same coverage target. Without a clearly defined baseline, the relative power savings are not meaningful. Also, no statistical test is performed on the five repeated measurements.
  5. [Eq. (11)] The beam-asymmetry constraint ϕV = ϕH^(αV/αH) is introduced without derivation or empirical validation, and it applies path-loss exponents to angular quantities, which is dimensionally questionable. It is also unclear whether this constraint is actually enforced in the DRL testbed, since Section V-A describes continuous ±3° adjustments on both horizontal and vertical beamwidths. Please either derive the model from antenna-pattern physics, validate it with measurements, or remove it as an unsupported ad-hoc assumption.
  6. [Sec. V-D] The scalability analysis is not rigorous. The claim that Delaunay triangulation reduces interference links from O(N^2) to O(N) ignores that a UAV may receive non-negligible interference from base stations outside its serving triangle. Likewise, partitioning N base stations into K clusters does not generally yield K ≪ N, and inter-cluster interference is not modeled. The complexity conclusions are therefore speculative; please either support them with a scaling experiment or clearly label this subsection as qualitative discussion.
minor comments (5)
  1. [Sec. V-B, Step 2] 'PSPR measurements' appears to be a typo for 'RSRP measurements.'
  2. [Fig. 6] The figure is not described in the text. Please add axis labels, units, and a legend, and state whether the power values are absolute or normalized.
  3. [Eq. (13)] The sampled SSCP estimator in Eq. (13) is introduced only after the results are reported. Please define it earlier and state how the threshold γth = −90 dBm relates to the general threshold in Eq. (9).
  4. [Table I] The power ranges in Table I are broad and come from literature; if they are used to justify the claimed offset of ISCC overhead, actual measured or estimated values from the testbed should be reported.
  5. [General] There are several reference and formatting typos, e.g., 'U. Goverment' in [18], 'Power Syet.' in [27], and inconsistent 'Tab. I'/'Table I' usage. Please proofread.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the testbed results are empirical measurements, not derivations from the paper's own definitions or self-citations.

full rationale

The paper is primarily a survey/tutorial plus a field testbed. The central claims—>90% SSCP and 20–27% power reduction—are presented as measured outcomes from a real-world Zigong testbed, not as conclusions derived from the paper's definitions. The DRL reward uses SSCP >90% as a positive-reward threshold, and the reported coverage is measured with the same SSCP definition (Eq. 13); this is a standard training objective/evaluation loop, not a circular derivation, because the test measurements could in principle fall below the threshold. The power reduction is compared against a specified static 5G NR baseline (Table II, Section V-C) and is reported as an empirical saving; the paper acknowledges ISCC-loop overhead qualitatively (Section III-C, Table I) but does not subtract it, which is a completeness gap in the net-energy claim rather than a circularity. The scalability discussion relies in part on the triangulation-based CoMP framework of [137] and the triangular-prism results of [138]; [138] is authored by some of the present authors, but the complexity reduction to O(N) is argued analytically in the text, and the cited work is an externally published, checkable numerical study. No step in the paper's derivation chain reduces by construction to its own inputs, and no load-bearing uniqueness claim is imported from self-citation. Score 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The main unstated assumptions are the LoS channel model, the ad hoc beam-spread constraint, the interference-to-signal conversion via triangulation, and the net offset of ISCC overhead.

free parameters (3)
  • SSCP reward threshold (90%) = 0.9
    Chosen by hand as the success criterion for the DRL reward; the reported >90% coverage is partly a consequence of this threshold.
  • RSRP threshold gamma_th (coverage requirement) = -90 dBm
    Used in Eq. (9) and Eq. (13) to define SSCP; chosen as a standard coverage threshold, not fitted, but it determines the numerical coverage results.
  • Beam asymmetry exponent ratio alpha_V/alpha_H = not specified
    Eq. (11) imposes phi_V = phi_H^(alpha_V/alpha_H) without derivation or fitted values; it shapes the feasible beam-pattern search space.
assumptions (4)
  • domain assumption G2A propagation is modeled by the LoS-dominated path loss L(d) and beam gain G(theta; phi_H, phi_V) in Eq. (12).
    The optimization and testbed inference rely on this model; no validation of L(d) for the Zigong terrain is reported.
  • ad hoc to paper The constraint phi_V = phi_H^(alpha_V/alpha_H) captures asymmetric beam gain in horizontal and vertical directions.
    Introduced in Eq. (11) without derivation or independent justification; it constrains the feasible beam search space.
  • domain assumption Delaunay-triangulation CoMP converts inter-cell interference into useful signal, reducing effective interference links from O(N^2) to O(N).
    Used in Section V-D for scalability; not proven here and based on self-cited prior work [137], [138].
  • ad hoc to paper The energy overhead added by the ISCC loop (sensing, computation, learning) is offset by communication-energy savings, yielding net reduction.
    Section III-C and Table I acknowledge overhead, but the testbed power numbers do not subtract it; the net-savings claim assumes the offset.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Sustainable Air-Ground Integrated Coverage Networks: ISCC Architecture, Technologies, and Testbed." pith.science (2026). https://pith.science/paper/BAQNMZTQ

@misc{pith2026260716812,
  author       = {Pith},
  title        = {Pith review of: Sustainable Air-Ground Integrated Coverage Networks: ISCC Architecture, Technologies, and Testbed},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BAQNMZTQ}},
  note         = {Machine review of arXiv:2607.16812}
}
read the original abstract

The rapid emergence of sixth-generation (6G) networks and the low-altitude economy has accelerated the evolution of wireless infrastructures toward air-ground integrated coverage networks (AGICNs), which seamlessly fuse terrestrial and aerial communication resources. However, existing AGICN studies primarily focus on coverage enhancement, while ignoring sustainability. Pursuing sustainable AGICNs introduces new challenges due to the multidimensional resource coupling across heterogeneous air-ground segments. In view of this, this paper presents a comprehensive survey and tutorial on sustainable AGICNs, aiming to balance coverage capacity with carbon efficiency in low-altitude economies. An integrated sensing, communication, and computation (ISCC)-driven architecture, which enables dynamic resource orchestration through closed-loop control, is proposed. We thus introduce a multi-dimensional sustainability metric system, which covers operational efficiency, task-oriented performance, and full lifecycle carbon emissions, to quantify energy and carbon footprints. We review enabling technologies, including artificial intelligence, hybrid precoding, integrated sensing and communication, and simultaneous wireless information and power transfer, and discuss their integration into the ISCC framework to minimize energy consumption while maintaining robust coverage. Experimental results on a real-world testbed demonstrate a 20% reduction in power consumption while achieving over 90% coverage probability, highlighting the feasibility of sustainable AGICNs for future green networks.

Figures

Figures reproduced from arXiv: 2607.16812 by the authors.

Figure 1
Figure 1. Outline and structure of this work. energy sharing and wireless power transfer (WPT) to supply energy to UAVs, thereby reducing dependence on traditional high-carbon power grids. At the system level, the 6G vision calls for a more holistic framework in which sensing, communication, computation, and energy interact synergistically to enhance network in￾telligence, adaptability, and carbon efficiency. Studies [28] and… view at source ↗
Figure 2
Figure 2. The ISCC-based collaborative control loop for sustainable AGICNs. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. ISCC-enabled multi-UAV control architecture. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The terrestrial communication networks topology and the results of triangulation for G2A coverage in Zigong, Sichuan province, China. [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 5
Figure 5. Figure 5: The illustration of proposed energy-efficient G2A coverage enhance [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]
Figure 6
Figure 6. Figure 6: Experimental results of G2A coverage. Pr(x, y, z;{θm}; ϕH, ϕV ) =PtG({θm}; ϕH, ϕV )L −1 (d). (12) Here, Pt is transmit power, G({θm}; ϕH, ϕV ) is a func￾tion of both beam shape ϕH, ϕV and elevation {θm}, L −1 (d) is the path loss as a function of distance d. • The DRL-…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

169 extracted references · 7 linked inside Pith

  1. [1]

    Exploit security for low-altitude economy: A SWIPT-driven strategy With UA V-mounted MF-RIS,

    S. Zhao, L. Wen, D. Tang, T. Wu, M. Elkashlan, F. Adachi, N. Al- Dhahir, and C. Yuen, “Exploit security for low-altitude economy: A SWIPT-driven strategy With UA V-mounted MF-RIS,”IEEE Trans. Veh. Technol., pp. 1–6, 2025. Early Access. 17

    2025

  2. [2]

    Toward realiza- tion of low-altitude economy networks: Core architecture, integrated technologies, and future directions,

    Y . Wang, G. Sun, Z. Sun, J. Wang, J. Li, C. Zhao, J. Wu, S. Liang, M. Yin, P. Wang, D. Niyato, S. Sun, and D. I. Kim, “Toward realiza- tion of low-altitude economy networks: Core architecture, integrated technologies, and future directions,”IEEE Trans. Cogn. Commun. Networking, pp. 1–1, 2025. Early Access

    2025

  3. [3]

    Toward a sustainable low-altitude economy: A survey of energy-efficient RIS-UA V networks,

    M. Ahmed, A. A. Soofi, F. Khan, S. Raza, W. U. Khan, L. Su, F. Xu, and Z. Han, “Toward a sustainable low-altitude economy: A survey of energy-efficient RIS-UA V networks,”IEEE Internet Things J., pp. 1–1, Apr. 2025. Early Access

    2025

  4. [4]

    Mul- timode high-altitude platform stations for next-generation wireless networks: Selection mechanism, benefits, and potential challenges,

    S. Alfattani, W. Jaafar, H. Yanikomeroglu, and A. Yongaçoglu, “Mul- timode high-altitude platform stations for next-generation wireless networks: Selection mechanism, benefits, and potential challenges,” IEEE Veh. Technol. Mag, vol. 18, pp. 20–28, Sept. 2023

    2023

  5. [5]

    Structured satellite-UA V-terrestrial networks for 6G internet of things,

    W. Feng, Y . Wang, Y . Chen, N. Ge, and C.-X. Wang, “Structured satellite-UA V-terrestrial networks for 6G internet of things,”IEEE Network, vol. 38, pp. 48–54, Mar. 2024

    2024

  6. [6]

    Learning based dynamic cluster reconfiguration for UA V mobility management with 3D beamforming,

    I. A. Meer, K.-L. Besser, M. Ozger, D. Schupke, H. V . Poor, and C. Cavdar, “Learning based dynamic cluster reconfiguration for UA V mobility management with 3D beamforming,” in2024 IEEE Inter- national Conference on Machine Learning for Communication and Networking (ICMLCN), (Stockholm, Sweden), pp. 486–491, 2024

    2024

  7. [7]

    A survey on UA V placement and trajectory optimization in communication networks: From the perspective of air-to-ground channel models,

    J. Won, D.-Y . Kim, Y .-I. Park, and J.-W. Lee, “A survey on UA V placement and trajectory optimization in communication networks: From the perspective of air-to-ground channel models,”ICT Express, vol. 9, pp. 385–397, Jan. 2023

    2023

  8. [8]

    Space-air-ground integrated network: A survey,

    J. Liu, Y . Shi, Z. M. Fadlullah, and N. Kato, “Space-air-ground integrated network: A survey,”IEEE Commun. Surv. Tutor., vol. 20, pp. 2714–2741, Fourthquarter 2018

    2018

Show all 169 references
  1. [9]

    Accessing from the sky: A tutorial on UA V communications for 5G and beyond,

    Y . Zeng, Q. Wu, and R. Zhang, “Accessing from the sky: A tutorial on UA V communications for 5G and beyond,”Proc. IEEE, vol. 107, pp. 2327–2375, Dec. 2019

    2019

  2. [10]

    Joint energy and performance aware relay positioning in flying networks,

    Rodrigues, Hugo and Coelho, André and Ricardo, Manuel and Campos, Rui, “Joint energy and performance aware relay positioning in flying networks,”IEEE Access, vol. 10, pp. 43848–43864, Apr. 2022

    2022

  3. [11]

    Joint latency-oriented, energy consumption, and carbon emission for a space–air–ground integrated network with newly designed power tech- nology,

    Y . Wang, B. Li, J. He, J. Dai, Y . Liu, and Y . Yang, “Joint latency-oriented, energy consumption, and carbon emission for a space–air–ground integrated network with newly designed power tech- nology,”Electronics, vol. 12, Aug. 2023

    2023

  4. [12]

    A survey on energy optimization techniques in UA V-based cellular networks: From conventional to machine learning approaches,

    A. I. Abubakar, I. Ahmad, K. G. Omeke, M. Ozturk, C. Ozturk, A. M. Abdel-Salam, M. S. Mollel, Q. H. Abbasi, S. Hussain, and M. A. Imran, “A survey on energy optimization techniques in UA V-based cellular networks: From conventional to machine learning approaches,” Drones, vol....

    2023

  5. [13]

    A comprehensive re- view of energy-efficient techniques for UA V-assisted industrial wireless networks,

    Y . Zhang, R. Zhao, D. Mishra, and D. W. K. Ng, “A comprehensive re- view of energy-efficient techniques for UA V-assisted industrial wireless networks,”Energies, vol. 17, Sept. 2024

    2024

  6. [14]

    A 3D geometry- based stochastic model for 5G massive MIMO channels,

    Y . Xie, B. Li, X. Zuo, M. Yang, and Z. Yan, “A 3D geometry- based stochastic model for 5G massive MIMO channels,” in2015 11th International Conference on Heterogeneous Networking for Qual- ity, Reliability, Security and Robustness (QSHINE), (Taipei, China), pp. 216–222, 2015

    2015

  7. [15]

    A survey on 5G radio access network energy efficiency: massive MIMO, lean carrier design, sleep modes, and machine learning,

    D. López-Pérez, A. De Domenico, N. Piovesan, G. Xinli, H. Bao, S. Qitao, and M. Debbah, “A survey on 5G radio access network energy efficiency: massive MIMO, lean carrier design, sleep modes, and machine learning,”IEEE Commun. Surv. Tutor., vol. 24, pp. 653– 697, Firstquarter 2022

    2022

  8. [16]

    Energy model for UA V communications: Experimental validation and model generalization,

    N. Gao, Y . Zeng, J. Wang, D. Wu, C. Zhang, Q. Song, J. Qian, and S. Jin, “Energy model for UA V communications: Experimental validation and model generalization,”China Communications, vol. 18, pp. 253–264, July 2021

    2021

  9. [17]

    Zeng and R

    Y . Zeng and R. Zhang,Energy-efficient UAV communications, ch. 12, pp. 299–314. John Wiley & Sons, Ltd, 2020

    2020

  10. [18]

    Energy efficiency techniques in 5G/6G net- works: Green communication solutions,

    S. Maiti and S. Juneja, “Energy efficiency techniques in 5G/6G net- works: Green communication solutions,” inAdvances in Data-Driven Computing and Intelligent Systems, (Singapore), pp. 407–423, 2024

    2024

  11. [19]

    Energy White Paper: Our energy future-creating a low carbon economy,

    U. Goverment, “Energy White Paper: Our energy future-creating a low carbon economy,”London, UK, Department of Trade and Industry, 2003

    2003

  12. [20]

    Green growth strategy through achieving carbon neutrality in 2050,

    T. Ministry of Economy and Industry, “Green growth strategy through achieving carbon neutrality in 2050,” 2020

  13. [21]

    Key enabling technologies for 6G: The role of UA Vs, terahertz communication, and intelligent recon- figurable surfaces in shaping the future of wireless networks,

    W. M. Othman, A. A. Ateya, M. E. Nasr, A. Muthanna, M. ElAffendi, A. Koucheryavy, and A. A. Hamdi, “Key enabling technologies for 6G: The role of UA Vs, terahertz communication, and intelligent recon- figurable surfaces in shaping the future of wireless networks,”J. Sens. Actu...

    2025

  14. [22]

    On the road to 6G: Visions, requirements, key technologies, and testbeds,

    C.-X. Wang, X. You, X. Gao, X. Zhu, Z. Li, C. Zhang, H. Wang, Y . Huang, Y . Chen, H. Haas, J. S. Thompson, E. G. Larsson, M. D. Renzo, W. Tong, P. Zhu, X. Shen, H. V . Poor, and L. Hanzo, “On the road to 6G: Visions, requirements, key technologies, and testbeds,” IEEE Commun....

    2023

  15. [23]

    A survey of channel modeling for UA V communications,

    A. A. Khuwaja, Y . Chen, N. Zhao, M.-S. Alouini, and P. Dobbins, “A survey of channel modeling for UA V communications,”IEEE Commun. Surv. Tutor., vol. 20, pp. 2804–2821, Fourthquarter 2018

    2018

  16. [24]

    Space-air-ground integrated wireless networks for 6G: Basics, key technologies, and future trends,

    Y . Xiao, Z. Ye, M. Wu, H. Li, M. Xiao, M.-S. Alouini, A. Al- Hourani, and S. Cioni, “Space-air-ground integrated wireless networks for 6G: Basics, key technologies, and future trends,”IEEE J. Sel. Areas Commun., vol. 42, pp. 3327–3354, Dec. 2024

    2024

  17. [25]

    A model for power system interconnection planning under low-carbon economy with CO 2 emission constraints,

    Y . Wu, S. Lou, and S. Lu, “A model for power system interconnection planning under low-carbon economy with CO 2 emission constraints,” IEEE Trans. Sustainable Energy, vol. 2, pp. 205–214, July 2011

    2011

  18. [26]

    Energy consumption of 5G, wireless systems and the digital ecosystem

    Environmental Health Trust, “Energy consumption of 5G, wireless systems and the digital ecosystem.” https://ehtrust.org/science/ reports-on-power-consumption-and-increasing-energy-use-of-wireless-/ systems-and-digital-ecosystem/, 2025

    2025

  19. [27]

    Peer-to-peer joint electricity and carbon trading based on carbon-aware distribution locational marginal pricing,

    Z. Lu, L. Bai, J. Wang, J. Wei, Y . Xiao, and Y . Chen, “Peer-to-peer joint electricity and carbon trading based on carbon-aware distribution locational marginal pricing,”IEEE Trans. Power Syet., vol. 38, pp. 835– 852, Jan. 2023

    2023

  20. [28]

    A survey on integrated sensing, communication, and computation,

    D. Wen, Y . Zhou, X. Li, Y . Shi, K. Huang, and K. B. Letaief, “A survey on integrated sensing, communication, and computation,”IEEE Commun. Surv. Tutor., pp. 1–1, 2024. Early Access

    2024

  21. [29]

    Integrated sensing, computation, and communication: System framework and performance optimization,

    Y . He, G. Yu, Y . Cai, and H. Luo, “Integrated sensing, computation, and communication: System framework and performance optimization,” IEEE Trans. Wireless Commun., vol. 23, pp. 1114–1128, Feb. 2024

    2024

  22. [30]

    Joint trajectory and resource optimization for UA V-assisted SWIPT systems: A comparative study of linear and nonlinear energy harvesting models,

    K. Heo, H.-H. Choi, and K. Lee, “Joint trajectory and resource optimization for UA V-assisted SWIPT systems: A comparative study of linear and nonlinear energy harvesting models,”IEEE Internet Things J., vol. 11, pp. 40293–40305, Sept. 2024

    2024

  23. [31]

    Joint transmit beamforming and receive power splitting for MISO SWIPT systems,

    Q. Shi, L. Liu, W. Xu, and R. Zhang, “Joint transmit beamforming and receive power splitting for MISO SWIPT systems,”IEEE Trans. Wireless Commun., vol. 13, pp. 3269–3280, Jun. 2014

    2014

  24. [32]

    Energy efficiency optimization for UA V-RIS-assisted wireless powered communication networks,

    X. Shen, L. Gu, J. Yang, and S. Shen, “Energy efficiency optimization for UA V-RIS-assisted wireless powered communication networks,” Drones, vol. 9, May 2025

    2025

  25. [33]

    Energy-efficient UA V communication with trajectory optimization,

    Y . Zeng and R. Zhang, “Energy-efficient UA V communication with trajectory optimization,”IEEE Trans. Wireless Commun., vol. 16, pp. 3747–3760, Jun. 2017

    2017

  26. [34]

    The future of aerial communications: A survey of IRS-enhanced UA V communica- tion technologies,

    Z. Chkirbene, A. Gouissem, R. Hamila, and D. Unal, “The future of aerial communications: A survey of IRS-enhanced UA V communica- tion technologies,” in2024 IEEE 8th Energy Conference (ENERGY- CON), (Doha, Qatar), pp. 1–6, 2024

    2024

  27. [35]

    UA V- assisted communications with RF energy harvesting: A comprehensive survey,

    G. K. Pandey, D. S. Gurjar, S. Yadav, Y . Jiang, and C. Yuen, “UA V- assisted communications with RF energy harvesting: A comprehensive survey,”IEEE Commun. Surv. Tutor., vol. 27, pp. 782–838, Apr. 2025

    2025

  28. [36]

    Integrated communication, sensing, and computation framework for 6G networks,

    X. Chen, Z. Feng, J. A. Zhang, Z. Yang, X. Yuan, X. He, and P. Zhang, “Integrated communication, sensing, and computation framework for 6G networks,”Sensors, vol. 24, May 2024

    2024

  29. [37]

    Xiong, F

    Y . Xiong, F. Dong, and F. Liu,Information-theoretic limits of integrated sensing and communications, pp. 375–404. 2025

    2025

  30. [38]

    Inte- grated sensing-communication-computation for over-the-air edge AI inference,

    Z. Zhuang, D. Wen, Y . Shi, G. Zhu, S. Wu, and D. Niyato, “Inte- grated sensing-communication-computation for over-the-air edge AI inference,”IEEE Trans. Wireless Commun., vol. 23, pp. 3205–3220, Aug. 2024

    2024

  31. [39]

    Full-duplex integrated sensing, communication, and computation over low-altitude wireless networks,

    Y . Chen, W. Liu, X. Zhang, J. Ren, H. Xing, S. Wang, Y . Shen, and K.-F. Tsang, “Full-duplex integrated sensing, communication, and computation over low-altitude wireless networks,”arXiv preprint arXiv: 2504.18143, 2025

    2025 arXiv

  32. [40]

    Energy-efficient edge inference in integrated sensing, communication, and computation networks,

    J. Yao, W. Xu, G. Zhu, K. Huang, and S. Cui, “Energy-efficient edge inference in integrated sensing, communication, and computation networks,”IEEE J. Sel. Areas Commun., vol. 43, pp. 3580–3595, May 2025

    2025

  33. [41]

    UA V communication in space–air–ground integrated networks (SAGINs): Technologies, applications, and challenges,

    P. Zhang, S. Chen, X. Zheng, P. Li, G. Wang, R. Wang, J. Wang, and L. Tan, “UA V communication in space–air–ground integrated networks (SAGINs): Technologies, applications, and challenges,”Drones, vol. 9, Feb. 2025

    2025

  34. [42]

    Effectiveness of mobile emitter location by cooperative swarm of unmanned aerial vehicles in various environmental conditions,

    Kelner, Jan M. and Ziółkowski, Cezary, “Effectiveness of mobile emitter location by cooperative swarm of unmanned aerial vehicles in various environmental conditions,”Sensors, vol. 20, May 2020

    2020

  35. [43]

    Resource allocation in unmanned aerial vehicle networks: A review,

    S. Sai, S. Mishra, and V . Chamola, “Resource allocation in unmanned aerial vehicle networks: A review,”Veh. Commun., vol. 52, p. 100889, Apr. 2025

    2025

  36. [44]

    Joint beamforming design and trajectory optimization for UA V-enabled cell-free ISAC 18 MIMO systems,

    J. Zhong, J. Wu, Y . Li, C. Zhang, and P. Zhu, “Joint beamforming design and trajectory optimization for UA V-enabled cell-free ISAC 18 MIMO systems,”IEEE Commun. Lett., vol. 29, pp. 1849–1853, Aug. 2025

    2025

  37. [45]

    Carbon-aware orchestration of integrated satellite aerial terrestrial networks via digital twin,

    S. Javaid and N. Saeed, “Carbon-aware orchestration of integrated satellite aerial terrestrial networks via digital twin,”arXiv preprint arXiv: 2510.17825, 2025

    2025

  38. [46]

    Energy consumption models for UA V communications: A brief survey,

    H. Yan, S.-H. Yang, Y . Ding, and Y . Chen, “Energy consumption models for UA V communications: A brief survey,” in2022 IEEE Inter- national Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Compu...

    2022

  39. [47]

    MEET: Mobility-enhanced edge intelligence for smart and green 6G networks,

    Y . Sun, B. Xie, S. Zhou, and Z. Niu, “MEET: Mobility-enhanced edge intelligence for smart and green 6G networks,”IEEE Commun. Mag., vol. 61, pp. 64–70, Oct. 2023

    2023

  40. [48]

    Energy tradeoff in ground-to- UA V communication via trajectory design,

    D. Yang, Q. Wu, Y . Zeng, and R. Zhang, “Energy tradeoff in ground-to- UA V communication via trajectory design,”IEEE Trans. Veh. Technol., vol. 67, pp. 6721–6726, Mar. 2018

    2018

  41. [49]

    Optimal power scheduling using data- driven carbon emission flow modelling for carbon intensity control,

    Y . Wang, J. Qiu, and Y . Tao, “Optimal power scheduling using data- driven carbon emission flow modelling for carbon intensity control,” IEEE Trans. Power Syet., vol. 37, pp. 2894–2905, July 2022

    2022

  42. [50]

    Optimal charging profile design for solar-powered sustainable UA V communi- cation networks,

    L. Wang, S. Tripathi, R. Zhang, N. Cheng, and M. Wang, “Optimal charging profile design for solar-powered sustainable UA V communi- cation networks,”arXiv preprint arXiv: 2302.06092, 2023

    2023 arXiv

  43. [51]

    Modeling and analysis of data and coverage energy efficiency for different demographic areas in 5G networks,

    J. Lorincz, Z. Klarin, and D. Begusic, “Modeling and analysis of data and coverage energy efficiency for different demographic areas in 5G networks,”IEEE Syst. J., vol. 16, pp. 1056–1067, Mar. 2022

    2022

  44. [52]

    Environmental engineering (EE): Assessment of mobile net- work energy efficiency,

    ETSI, “Environmental engineering (EE): Assessment of mobile net- work energy efficiency,” Standard ES 203 228, European Telecommu- nications Standards Institute (ETSI), Oct. 2020

    2020

  45. [53]

    Energy-efficient beamforming design for integrated sensing and communications systems,

    J. Zou, S. Sun, C. Masouros, Y . Cui, Y .-F. Liu, and D. W. K. Ng, “Energy-efficient beamforming design for integrated sensing and communications systems,”IEEE Trans. Commun., vol. 72, pp. 3766– 3782, Jun. 2024

    2024

  46. [54]

    MIMO integrated sensing and communication: CRB-rate tradeoff,

    H. Hua, T. X. Han, and J. Xu, “MIMO integrated sensing and communication: CRB-rate tradeoff,”IEEE Trans. Wireless Commun., vol. 23, pp. 2839–2854, Apr. 2024

    2024

  47. [55]

    ESE: efficient speech recognition engine with sparse LSTM on FPGA,

    S. Han, J. Kang, H. Mao, Y . Hu, X. Li, Y . Li, D. Xie, H. Luo, S. Yao, Y . Wang,et al., “ESE: efficient speech recognition engine with sparse LSTM on FPGA,” inProceedings of the ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA ’17), (New York, NY , US...

    2017

  48. [56]

    Greenhouse gas emission trajectories for the information and communication technology sector compatible with the UNFCCC paris agreement,

    ITU-T, “Greenhouse gas emission trajectories for the information and communication technology sector compatible with the UNFCCC paris agreement,” Standard ITU-T L.1470, International Telecommunication Union, 2020

    2020

  49. [57]

    Integrating sensing, computing, and communication in 6G wireless networks: Design and optimization,

    Q. Qi, X. Chen, A. Khalili, C. Zhong, Z. Zhang, and D. W. K. Ng, “Integrating sensing, computing, and communication in 6G wireless networks: Design and optimization,”IEEE Trans. Commun., vol. 70, pp. 6212–6227, Jul. 2022

    2022

  50. [58]

    Advancements in UA V-based integrated sensing and communication: A comprehensive survey,

    M. Ahmed, A. A. Nasir, M. Masood, K. A. Memon, K. K. Qureshi, F. Khan, W. U. Khan, F. Xu, and Z. Han, “Advancements in UA V-based integrated sensing and communication: A comprehensive survey,”arXiv preprint arXiv: 2501.06526, 2025

    2025 arXiv

  51. [59]

    Air-to-ground channel characterization for low-height UA Vs in realistic network deploy- ments,

    Rodríguez-Piñeiro, José and Domínguez-Bolaño, Tomás and Cai, Xuesong and Huang, Zeyu and Yin, Xuefeng, “Air-to-ground channel characterization for low-height UA Vs in realistic network deploy- ments,”IEEE Trans. Antennas Propag., vol. 69, pp. 992–1006, Aug. 2021

    2021

  52. [60]

    Networked ISAC for low- altitude economy: Coordinated transmit beamforming and UA V tra- jectory design,

    G. Cheng, X. Song, Z. Lyu, and J. Xu, “Networked ISAC for low- altitude economy: Coordinated transmit beamforming and UA V tra- jectory design,”IEEE Trans. Commun., vol. 73, pp. 5832–5847, Aug. 2025

    2025

  53. [61]

    Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications,

    J. Kim, S. Kim, C. Ju, and H. I. Son, “Unmanned aerial vehicles in agriculture: A review of perspective of platform, control, and applications,”IEEE Access, vol. 7, pp. 105100–105115, Jul. 2019

    2019

  54. [62]

    UA V-Assisted ISCC Networks: Joint Resource and Trajectory Optimization,

    J. Chen, Y . Xu, D. Yang, and T. Zhang, “UA V-Assisted ISCC Networks: Joint Resource and Trajectory Optimization,”IEEE Wireless Commun. Lett., vol. 13, pp. 2372–2376, Jul. 2024

    2024

  55. [63]

    Task-oriented sensing, computation, and communication integration for multi-device edge AI,

    D. Wen, P. Liu, G. Zhu, Y . Shi, J. Xu, Y . C. Eldar, and S. Cui, “Task-oriented sensing, computation, and communication integration for multi-device edge AI,”IEEE Trans. Wireless Commun., vol. 23, pp. 2486–2502, Aug. 2024

    2024

  56. [64]

    GPS-aided deep learning for beam prediction and tracking in UA V mmWave communication,

    V . Ardianto Nugroho and B. Moo Lee, “GPS-aided deep learning for beam prediction and tracking in UA V mmWave communication,”IEEE Access, vol. 13, pp. 117065–117077, Jul. 2025

    2025

  57. [65]

    Integrated sensing, communication, and computation for over-the-air federated edge learning,

    D. Wen, S. Xie, X. Cao, Y . Cui, J. Xu, Y . Shi, and S. Cui, “Integrated sensing, communication, and computation for over-the-air federated edge learning,”IEEE Trans. Wireless Commun., pp. 1–1, 2025. Early Access

    2025

  58. [66]

    Over-the-air integrated sensing, communication, and computation in IoT networks,

    X. Li, Y . Gong, K. Huang, and Z. Niu, “Over-the-air integrated sensing, communication, and computation in IoT networks,”IEEE Wireless Commun., vol. 30, pp. 32–38, Feb. 2023

    2023

  59. [67]

    Adaptive digital twin for UA V-assisted integrated sensing, communication, and computation networks,

    B. Li, W. Liu, W. Xie, N. Zhang, and Y . Zhang, “Adaptive digital twin for UA V-assisted integrated sensing, communication, and computation networks,”IEEE Trans. Green Commun. Networking, vol. 7, pp. 1996– 2009, Jul. 2023

    1996

  60. [68]

    Reinforcement learning- based energy-saving path planning for UA Vs in turbulent wind,

    S. Chen, Y . Mo, X. Wu, J. Xiao, and Q. Liu, “Reinforcement learning- based energy-saving path planning for UA Vs in turbulent wind,” Electronics, vol. 13, Aug. 2024

    2024

  61. [69]

    GRU-based deep learning framework for real-time, accurate, and scalable UA V trajectory prediction,

    S. Yoon, D. Jang, H. Yoon, T. Park, and K. Lee, “GRU-based deep learning framework for real-time, accurate, and scalable UA V trajectory prediction,”Drones, vol. 9, Feb. 2025

    2025

  62. [70]

    Joint optimization of flying trajectory and task Offloading for UA V-enabled MEC networks: A digital twin-assisted hybrid learning approach,

    J. Wu, J. Luo, T. Wang, and L. Gao, “Joint optimization of flying trajectory and task Offloading for UA V-enabled MEC networks: A digital twin-assisted hybrid learning approach,” in2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring), (Singapore, Sin- gapore), pp. 1–6, 2024

    2024

  63. [71]

    Modeling and improving the energy performance of GPS receivers for location services,

    K. Chen, G. Tan, J. Cao, M. Lu, and X. Fan, “Modeling and improving the energy performance of GPS receivers for location services,”IEEE Sens. J., vol. 20, pp. 4512–4523, Apr. 2020

    2020

  64. [72]

    Power consumption analysis of UA Vs with varying payloads for next generation wireless networks,

    S. Ahmed and N. Emon, “Power consumption analysis of UA Vs with varying payloads for next generation wireless networks,”MIST INTERNATIONAL JOURNAL OF SCIENCE AND TECHNOLOGY, vol. 13, pp. 85–95, 12 2025

    2025

  65. [73]

    Small low-power UA V-borne SAR imaging system based on millimeter-wave radar and RTK,

    K. Liao, L. Xue, Q. Li, N. Xie, and H. Chen, “Small low-power UA V-borne SAR imaging system based on millimeter-wave radar and RTK,” in2024 7th International Conference on Electronics Technology (ICET), pp. 1046–1052, 2024

    2024

  66. [74]

    Benchmarking edge AI platforms: Performance analysis of NVIDIA Jetson and Raspberry Pi 5 with coral TPU,

    D. Minott, S. Siddiqui, and R. J. Haddad, “Benchmarking edge AI platforms: Performance analysis of NVIDIA Jetson and Raspberry Pi 5 with coral TPU,” inSoutheastCon 2025, pp. 1384–1389, 2025

    2025

  67. [75]

    Performance evaluation of modern GPU accelerator-based edge systems: A holistic approach,

    H. Lee and P. Kang, “Performance evaluation of modern GPU accelerator-based edge systems: A holistic approach,”IEEE Internet Things J., vol. 12, pp. 51716–51729, Dec. 2025

    2025

  68. [76]

    Performance analysis of edge and In-Sensor AI processors: A comparative review,

    L. Capogrosso, P. Bonazzi, and M. Magno, “Performance analysis of edge and In-Sensor AI processors: A comparative review,”arXiv preprint arXiv: 2603.08725, 2026

    2026

  69. [77]

    Green edge AI: A contemporary survey,

    Y . Mao, X. Yu, K. Huang, Y .-J. Angela Zhang, and J. Zhang, “Green edge AI: A contemporary survey,”Proc. IEEE, vol. 112, pp. 880–911, Jul. 2024

    2024

  70. [78]

    Power consumption modeling of different base station types in heterogeneous cellular networks,

    O. Arnold, F. Richter, G. Fettweis, and O. Blume, “Power consumption modeling of different base station types in heterogeneous cellular networks,” in2010 Future Network & Mobile Summit, pp. 1–8, 2010

    2010

  71. [79]

    A survey of energy-efficient wireless communications,

    D. Feng, C. Jiang, G. Lim, L. J. Cimini, G. Feng, and G. Y . Li, “A survey of energy-efficient wireless communications,”IEEE Commun. Surv. Tutor., vol. 15, pp. 167–178, Firstquarter 2013

    2013

  72. [80]

    Radio resource allocation for integrated sensing, communication, and computation networks,

    L. Zhao, D. Wu, L. Zhou, and Y . Qian, “Radio resource allocation for integrated sensing, communication, and computation networks,”IEEE Trans. Wireless Commun., vol. 21, pp. 8675–8687, Oct. 2022

    2022

  73. [81]

    Edge continual learning for dynamic digital twins over wireless networks,

    O. Hashash, C. Chaccour, and W. Saad, “Edge continual learning for dynamic digital twins over wireless networks,” in2022 IEEE 23rd International Workshop on Signal Processing Advances in Wireless Communication (SPAWC), (Oulu, Finland), pp. 1–5, 2022

    2022

  74. [82]

    Multi-agent drl for queue-aware task offloading in hierarchical mec-enabled air-ground networks,

    M. Hevesli, A. M. Seid, A. Erbad, and M. Abdallah, “Multi-agent drl for queue-aware task offloading in hierarchical mec-enabled air-ground networks,”IEEE Trans. Cogn. Commun. Networking, pp. 1–1, 2025. Early Access

    2025

  75. [83]

    UA V coverage path planning under varying power constraints using deep reinforcement learning,

    M. Theile, H. Bayerlein, R. Nai, D. Gesbert, and M. Caccamo, “UA V coverage path planning under varying power constraints using deep reinforcement learning,” in2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Las Vegas, NV , USA), pp. 1444–1449, 2020

    2020

  76. [84]

    Deep learning based channel estimation for UA Vs: A modified U-Net approach,

    C. GUPTA and S. Yadav, “Deep learning based channel estimation for UA Vs: A modified U-Net approach,”Adv. Electr. Comp. Eng., vol. 25, pp. 61–70, Feb. 2025

    2025

  77. [85]

    Optimizing space-air-ground integrated networks by artificial intelligence,

    N. Kato, Z. M. Fadlullah, F. Tang, B. Mao, S. Tani, A. Okamura, and J. Liu, “Optimizing space-air-ground integrated networks by artificial intelligence,”IEEE Wireless Commun., vol. 26, pp. 140–147, Jan. 2019

    2019

  78. [86]

    Graph neural network aided deep reinforcement learning for microservice de- ployment in cooperative edge computing,

    S. Chen, Q. Yuan, J. Li, H. He, S. Li, X. Jiang, and J. Yang, “Graph neural network aided deep reinforcement learning for microservice de- ployment in cooperative edge computing,”IEEE Trans. Serv. Comput., vol. 17, pp. 3742–3757, Jun. 2024. 19

    2024

  79. [87]

    Improve the training efficiency of DRL for wireless communication resource allocation: The role of generative diffusion models,

    X. Zhang and J. Yu, “Improve the training efficiency of DRL for wireless communication resource allocation: The role of generative diffusion models,”arXiv preprint arXiv: 2502.07211, 2025

    2025 arXiv

  80. [88]

    Generative diffusion models for resource allocation in wireless networks,

    Y . B. Uslu, S. Hadou, S. S. Bidokhti, and A. Ribeiro, “Generative diffusion models for resource allocation in wireless networks,”arXiv preprint arXiv: 2504.20277, 2025

    2025

  81. [89]

    Distributed con- ditional generative adversarial networks (GANs) for data-driven mil- limeter wave communications in UA V networks,

    Q. Zhang, A. Ferdowsi, W. Saad, and M. Bennis, “Distributed con- ditional generative adversarial networks (GANs) for data-driven mil- limeter wave communications in UA V networks,”IEEE Trans. Wireless Commun., vol. 21, no. 3, pp. 1438–1452, 2022

    2022

  82. [90]

    Diffusion model based resource allocation strategy in ultra-reliable wireless networked control sys- tems,

    A. Babazadeh Darabi and S. Coleri, “Diffusion model based resource allocation strategy in ultra-reliable wireless networked control sys- tems,”IEEE Commun. Lett., vol. 29, pp. 85–89, Nov. 2025

    2025

  83. [91]

    Generative neural network channel modeling for millimeter-wave UA V communication,

    W. Xia, S. Rangan, M. Mezzavilla, A. Lozano, G. Geraci, V . Semkin, and G. Loianno, “Generative neural network channel modeling for millimeter-wave UA V communication,”IEEE Trans. Wireless Com- mun., vol. 21, pp. 9417–9431, May 2022

    2022

  84. [92]

    UA V-enabled intelligent transportation systems for the smart city: Applications and challenges,

    H. Menouar, I. Guvenc, K. Akkaya, A. S. Uluagac, A. Kadri, and A. Tuncer, “UA V-enabled intelligent transportation systems for the smart city: Applications and challenges,”IEEE Commun. Mag., vol. 55, pp. 22–28, Mar. 2017

    2017

  85. [93]

    Multi-UA V collaborative search and attack mission decision-making in unknown environments,

    Z. Liang, Q. Li, and G. Fu, “Multi-UA V collaborative search and attack mission decision-making in unknown environments,”Sensors, vol. 23, Aug. 2023

    2023

  86. [94]

    A review of collaborative trajectory planning for multiple unmanned aerial vehicles,

    L. Wang, W. Huang, H. Li, W. Li, J. Chen, and W. Wu, “A review of collaborative trajectory planning for multiple unmanned aerial vehicles,”Processes, vol. 12, Jun. 2024

    2024

  87. [95]

    High-altitude platform stations (HAPS); regulatory obstacles blocking their deployment,

    S. Stellatou and C. Erotokritou, “High-altitude platform stations (HAPS); regulatory obstacles blocking their deployment,” in2024 International Conference on Unmanned Aircraft Systems (ICUAS), (Chania - Crete, Greece), pp. 363–369, 2024

    2024

  88. [96]

    Optimizing network performance and resource allocation in HAPS- UA V integrated sensing and communication systems for 6G,

    P. Kanani, M. J. Omidi, M. Modarres-Hashemi, and H. Yanikomeroglu, “Optimizing network performance and resource allocation in HAPS- UA V integrated sensing and communication systems for 6G,”IEEE Trans. Wireless Commun., pp. 1–1, 2025. Early Access

    2025

  89. [97]

    A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs),

    O. S. Oubbati, A. Lakas, F. Zhou, M. Güne¸ s, and M. B. Yagoubi, “A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs),”Veh. Commun., vol. 10, pp. 29–56, Oct. 2017

    2017

  90. [98]

    FANET and MANET, a support and composition relationship,

    C. A. T. Romero, J. H. Ortiz, L. C. Rodríguez Timaná, F. V . Varela, A. H. Aristizábal Montufar, and J. Gamboa-Cruzado, “FANET and MANET, a support and composition relationship,”Comput. Mater. Continua, vol. 82, pp. 1699–1732, Feb. 2025

    2025

  91. [99]

    A survey of security in UA Vs and FANETs: Issues, threats, analysis of attacks, and solutions,

    O. Ceviz, S. Sen, and P. Sadioglu, “A survey of security in UA Vs and FANETs: Issues, threats, analysis of attacks, and solutions,”IEEE Commun. Surv. Tutor., pp. 1–1, 2024. Early Access

    2024

  92. [100]

    A brief review on position-based routing protocols in flying Ad-hoc networks,

    H. Singh, S. Verma, and A. Adhya, “A brief review on position-based routing protocols in flying Ad-hoc networks,” in2022 5th International Conference on Contemporary Computing and Informatics (IC3I), (Uttar Pradesh, India), pp. 1423–1428, 2022

    2022

  93. [101]

    Flying ad- hoc networks (FANETs): A review of communication architectures, and routing protocols,

    M. A. Khan, A. Safi, I. M. Qureshi, and I. U. Khan, “Flying ad- hoc networks (FANETs): A review of communication architectures, and routing protocols,” in2017 First International Conference on Latest trends in Electrical Engineering and Computing Technologies (INTELLECT), pp. ...

    2017

  94. [102]

    Advancement challenges in UA V swarm formation control: A comprehensive review,

    Y . Bu, Y . Yan, and Y . Yang, “Advancement challenges in UA V swarm formation control: A comprehensive review,”Drones, vol. 8, May 2024

    2024

  95. [103]

    Secure communication and dy- namic formation control of intelligent drone swarms using blockchain technology,

    H. Li, P. Li, J. Liu, and P. Zhang, “Secure communication and dy- namic formation control of intelligent drone swarms using blockchain technology,”Information, vol. 16, Sept. 2025

    2025

  96. [104]

    Comparative analysis of centralized and distributed multi-UA V task allocation algo- rithms: A unified evaluation framework,

    Y . Song, Z. Ma, N. Chen, S. Zhou, and S. Srigrarom, “Comparative analysis of centralized and distributed multi-UA V task allocation algo- rithms: A unified evaluation framework,”Drones, vol. 9, Jul. 2025

    2025

  97. [105]

    Centralized network model improvement system integrated into UA V swarm,

    D. L. Nguyen, V . B. Tran, N. P. Dao, D. T. Tran, J. Lee, and A. Q. Nguyen, “Centralized network model improvement system integrated into UA V swarm,” in2023 International Conference on Advanced Tech- nologies for Communications (ATC), (Da Nang, Vietnam), pp. 190– 195, 2023

    2023

  98. [106]

    Decentralized UA V swarm control for multitarget tracking using approximate dy- namic programming,

    M. A. Azam, S. Dey, H. D. Mittelmann, and S. Ragi, “Decentralized UA V swarm control for multitarget tracking using approximate dy- namic programming,” in2021 IEEE World AI IoT Congress (AIIoT), (Seattle, W A, USA), pp. 0457–0461, 2021

    2021

  99. [107]

    Distributed offloading for multi-UA V swarms in MEC-assisted 5G heterogeneous networks,

    M. Ma and Z. Wang, “Distributed offloading for multi-UA V swarms in MEC-assisted 5G heterogeneous networks,”Drones, vol. 7, Mar. 2023

    2023

  100. [108]

    Central- ized and decentralized based swarm of leader and follower formation of autonomous,

    S. K. N T, M. Padmapriya, J. P. Reddy, and P. Rajalakshmi, “Central- ized and decentralized based swarm of leader and follower formation of autonomous,” in2023 IEEE 20th India Council International Conference (INDICON), (Hyderabad, India), pp. 1433–1439, 2023

    2023

  101. [109]

    A hierarchical reinforcement learning framework for multi-UA V combat using leader–follower strategy,

    J. Pang, J. He, N. M. A. A. Mohamed, C. Lin, Z. Zhang, and X. Hao, “A hierarchical reinforcement learning framework for multi-UA V combat using leader–follower strategy,”Know.-Based Syst., vol. 316, May 2025

    2025

  102. [110]

    Robust hierarchical formation control of unmanned aerial vehicles via neural-based observers,

    Y . Fei, Y . Sun, and P. Shi, “Robust hierarchical formation control of unmanned aerial vehicles via neural-based observers,”Drones, vol. 6, Feb. 2022

    2022

  103. [111]

    Multi-UA Vs formation flight control based on leader-follower pattern,

    W. Yuan, Q. Chen, Z. Hou, and Y . Li, “Multi-UA Vs formation flight control based on leader-follower pattern,” in2017 36th Chinese Control Conference (CCC), (Dalian, China), pp. 1276–1281, 2017

    2017

  104. [112]

    Distributed observer-based formation control for leader-follower multi-UA Vs with collision avoid- ance,

    J. Wang, J. Liu, P. Chen, and K. Chen, “Distributed observer-based formation control for leader-follower multi-UA Vs with collision avoid- ance,”IEEE Trans. Veh. Technol., vol. 74, pp. 11951–11962, Aug. 2025

    2025

  105. [113]

    Enhanced ant colony optimization for vehicular ad hoc networks using fittest node clustering,

    A. Bijalwan, I. Hussain, K. C. Purohit, and M. A. Kumar, “Enhanced ant colony optimization for vehicular ad hoc networks using fittest node clustering,”Sustainability, vol. 15, Nov. 2023

    2023

  106. [114]

    Modeling of bio-inspired algorithms AntHocNet and BeeAdHoc for Flying Ad Hoc Networks (FANETs),

    A. V . Leonov, “Modeling of bio-inspired algorithms AntHocNet and BeeAdHoc for Flying Ad Hoc Networks (FANETs),” in2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE), vol. 02, (Novosibirsk, Russia), pp. 90–99, 2016

    2016

  107. [115]

    Optimized routing of UA Vs using bio-inspired algorithm in FANET: A systematic review,

    T. R. Beegum, M. Y . I. Idris, M. N. B. Ayub, and H. A. Shehadeh, “Optimized routing of UA Vs using bio-inspired algorithm in FANET: A systematic review,”IEEE Access, vol. 11, pp. 15588–15622, Feb. 2023

    2023

  108. [116]

    Hierarchical aerial computing for internet of things via cooperation of HAPs and UA Vs,

    Z. Jia, Q. Wu, C. Dong, C. Yuen, and Z. Han, “Hierarchical aerial computing for internet of things via cooperation of HAPs and UA Vs,” IEEE Internet Things J., vol. 10, pp. 5676–5688, Apr. 2023

    2023

  109. [117]

    Multi-tier drone architec- ture for 5G/B5G cellular networks: Challenges, trends, and prospects,

    S. Sekander, H. Tabassum, and E. Hossain, “Multi-tier drone architec- ture for 5G/B5G cellular networks: Challenges, trends, and prospects,” IEEE Commun. Mag., vol. 56, pp. 96–103, Mar. 2018

    2018

  110. [118]

    Unmanned aerial vehicles (UA V) in disaster management applications,

    N. Nikhil, S. M. Shreyas, G. Vyshnavi, and S. Yadav, “Unmanned aerial vehicles (UA V) in disaster management applications,” in2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), (Tirunelveli, India), pp. 140–148, 2020

    2020

  111. [119]

    Design and deployment of UA V-aided post-disaster emergency network,

    K. G. Panda, S. Das, D. Sen, and W. Arif, “Design and deployment of UA V-aided post-disaster emergency network,”IEEE Access, vol. 7, pp. 102985–102999, Jul. 2019

    2019

  112. [120]

    Space-air-ground integrated 6G wireless communication networks: A review of antenna technologies and application scenarios,

    F. A. Dicandia, N. J. G. Fonseca, M. Bacco, S. Mugnaini, and S. Genovesi, “Space-air-ground integrated 6G wireless communication networks: A review of antenna technologies and application scenarios,” Sensors, vol. 22, no. 9, 2022

    2022

  113. [121]

    3D beamforming and han- dover analysis for UA V networks,

    A. Colpaert, E. Vinogradov, and S. Pollin, “3D beamforming and han- dover analysis for UA V networks,” in2020 IEEE Globecom Workshops (GC Wkshps, (Taipei, Taiwan), pp. 1–6, 2020

    2020

  114. [122]

    Aerial-ground interference mitigation for cellular-connected UA V,

    W. Mei and R. Zhang, “Aerial-ground interference mitigation for cellular-connected UA V,”IEEE Wireless Commun., vol. 28, pp. 167– 173, Feb. 2021

    2021

  115. [123]

    Hybrid beamforming for massive MIMO: A survey,

    A. F. Molisch, V . V . Ratnam, S. Han, Z. Li, S. L. H. Nguyen, L. Li, and K. Haneda, “Hybrid beamforming for massive MIMO: A survey,” IEEE Commun. Mag., vol. 55, pp. 134–141, Sept. 2017

    2017

  116. [124]

    Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network,

    Q. Wu and R. Zhang, “Towards smart and reconfigurable environment: Intelligent reflecting surface aided wireless network,”IEEE Commun. Mag., vol. 58, pp. 106–112, Jan. 2020

    2020

  117. [125]

    A survey on millimeter-wave beamforming enabled UA V communications and networking,

    Z. Xiao, L. Zhu, Y . Liu, P. Yi, R. Zhang, X.-G. Xia, and R. Schober, “A survey on millimeter-wave beamforming enabled UA V communications and networking,”IEEE Commun. Surv. Tutor., vol. 24, pp. 557–610, Firstquarter 2022

    2022

  118. [126]

    Space-air-ground IoT network and related key technologies,

    T. Hong, W. Zhao, R. Liu, and M. Kadoch, “Space-air-ground IoT network and related key technologies,”IEEE Trans. Wireless Commun., vol. 27, pp. 96–104, Apr. 2020

    2020

  119. [127]

    Hybrid digital and analog beamforming design for large-scale antenna arrays,

    F. Sohrabi and W. Yu, “Hybrid digital and analog beamforming design for large-scale antenna arrays,”IEEE J. Sel. Top. Sign. Proces., vol. 10, pp. 501–513, Apr. 2016

    2016

  120. [128]

    Deep learning for direct hybrid precoding in millimeter wave massive MIMO systems,

    X. Li and A. Alkhateeb, “Deep learning for direct hybrid precoding in millimeter wave massive MIMO systems,” in2019 53rd Asilomar Conference on Signals, Systems, and Computers, (Pacific Grove, CA, USA), pp. 800–805, 2019

    2019

  121. [129]

    Deep reinforcement learning-driven hybrid precoding for efficient mm-wave multi-user MIMO systems,

    A. Salh, M. A. Alhartomi, G. A. Hussain, C. J. Jing, N. S. M. Shah, S. Alzahrani, R. Alsulami, S. Alharbi, A. Hakimi, and F. S. Almehmadi, “Deep reinforcement learning-driven hybrid precoding for efficient mm-wave multi-user MIMO systems,”J. Sens. Actuar. Netw., vol. 14, Feb. 2025

    2025

  122. [130]

    Deep learning assisted calibrated beam training for millimeter-wave communication systems,

    K. Ma, D. He, H. Sun, Z. Wang, and S. Chen, “Deep learning assisted calibrated beam training for millimeter-wave communication systems,” IEEE Trans. Commun., vol. 69, pp. 6706–6721, Oct. 2021. 20

    2021

  123. [131]

    Deep learning for THz drones with flying intelligent surfaces: Beam and handoff prediction,

    N. Abuzainab, M. Alrabeiah, A. Alkhateeb, and Y . E. Sagduyu, “Deep learning for THz drones with flying intelligent surfaces: Beam and handoff prediction,” in2021 IEEE International Conference on Com- munications Workshops (ICC Workshops), (Montreal, QC, Canada), pp. 1–6, 2021

    2021

  124. [132]

    Entropy- based probing beam selection and beam prediction via deep learning,

    F. Meng, C. Zhang, Y . Huang, Z. Zhang, X. Bai, and Z. Lu, “Entropy- based probing beam selection and beam prediction via deep learning,” arXiv preprint arXiv: 2401.01609, 2024

    2024 arXiv

  125. [133]

    Interference management for cellular-connected UA Vs: A deep reinforcement learning approach,

    U. Challita, W. Saad, and C. Bettstetter, “Interference management for cellular-connected UA Vs: A deep reinforcement learning approach,” IEEE Trans. Wireless Commun., vol. 18, p. 2125–2140, Apr. 2019

    2019

  126. [134]

    Communication-enabled multi-agent decentralised deep reinforcement learning to optimise energy-efficiency in UA V-assisted networks,

    B. Omoniwa, B. Galkin, and I. Dusparic, “Communication-enabled multi-agent decentralised deep reinforcement learning to optimise energy-efficiency in UA V-assisted networks,”arXiv preprint arXiv: 2210.00041, 2022

    2022 arXiv

  127. [135]

    Multi-agent reinforce- ment learning in adversarial game environments: Personalized anti- interference strategies for heterogeneous UA V communication,

    Y . Qin, J. Tang, F. Tang, M. Zhao, and N. Kato, “Multi-agent reinforce- ment learning in adversarial game environments: Personalized anti- interference strategies for heterogeneous UA V communication,”IEEE Trans. Mob. Comput., vol. 24, no. 9, pp. 8886–8898, 2025

    2025

  128. [136]

    Hierarchical multi-agent DRL based dynamic cluster reconfiguration for UA V mobility management,

    I. A. Meer, K.-L. Besser, M. Ozger, D. Schupke, H. V . Poor, and C. Cavdar, “Hierarchical multi-agent DRL based dynamic cluster reconfiguration for UA V mobility management,”arXiv preprint arXiv: 2412.16167, 2024

    2024

  129. [137]

    Ground-to-air communications beyond 5G: A coordinated multipoint transmission based on poisson-delaunay trian- gulation,

    Y . Li and M. Xia, “Ground-to-air communications beyond 5G: A coordinated multipoint transmission based on poisson-delaunay trian- gulation,”IEEE Trans. Wireless Commun., vol. 22, pp. 1841–1854, Sept. 2023

    2023

  130. [138]

    Ground-to-air wireless coverage extension for 6G: A triangular prism structure-based approach,

    J. Liu, M. Sheng, J. Li, X. Chen, and C. Zhao, “Ground-to-air wireless coverage extension for 6G: A triangular prism structure-based approach,”Sci. China Inf. Sci, vol. 67, p. 224301, Dec. 2024

    2024

  131. [139]

    Cooper- ative beamforming design for anti-UA V ISAC systems,

    Y . Zhang, H. Shan, Y . Zhou, Z. Shi, L. Sheng, and Y . Liu, “Cooper- ative beamforming design for anti-UA V ISAC systems,”IEEE Trans. Wireless Commun., vol. 24, pp. 2249–2264, Mar. 2025

    2025

  132. [140]

    Hybrid-RIS empowered UA V-assisted ISAC sys- tems: Transfer learning-based DRL,

    P. Saikia, A. Jee, K. Singh, W.-J. Huang, A.-A. A. Boulogeorgos, and T. A. Tsiftsis, “Hybrid-RIS empowered UA V-assisted ISAC sys- tems: Transfer learning-based DRL,”IEEE Trans. Commun., vol. 73, pp. 8314–8329, Sept. 2025

    2025

  133. [141]

    Energy efficient UA V-RIS-aided integrated sensing and communication systems using deep reinforcement learning,

    Y . Chen, H. Yang, W. Xie, H. Lu, and C. Zhang, “Energy efficient UA V-RIS-aided integrated sensing and communication systems using deep reinforcement learning,”IEEE Trans. Veh. Technol., pp. 1–6, 2025. Early Access

    2025

  134. [142]

    Efficient optimization in RIS-assisted UA V system using deep reinforcement learning for mmWave-NOMA 6G communications,

    S. Sobhi-Givi, M. Nouri, M. G. Shayesteh, H. Behroozi, H. H. Kwon, and M. J. Piran, “Efficient optimization in RIS-assisted UA V system using deep reinforcement learning for mmWave-NOMA 6G communications,”IEEE Internet Things J., vol. 12, pp. 26042–26057, Jul. 2025

    2025

  135. [143]

    Help from the sky: Leveraging UA Vs for disaster management,

    M. Erdelj, E. Natalizio, K. R. Chowdhury, and I. F. Akyildiz, “Help from the sky: Leveraging UA Vs for disaster management,”IEEE Pervasive Comput., vol. 16, pp. 24–32, Jan.-Mar. 2017

    2017

  136. [144]

    Sensor planning for a symbiotic UA V and UGV system for precision agriculture,

    P. Tokekar, J. V . Hook, D. Mulla, and V . Isler, “Sensor planning for a symbiotic UA V and UGV system for precision agriculture,”IEEE Trans. Robot., vol. 32, pp. 1498–1511, Dec. 2016

    2016

  137. [145]

    A review on viewpoints and path planning for UA V-based 3-D reconstruction,

    M. Maboudi, M. Homaei, S. Song, S. Malihi, M. Saadatseresht, and M. Gerke, “A review on viewpoints and path planning for UA V-based 3-D reconstruction,”IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 16, pp. 5026–5048, May 2023

    2023

  138. [146]

    A joint UA V trajectory, user association, and beamforming design strategy for multi-UA V-assisted ISAC systems,

    R. Zhang, Y . Zhang, R. Tang, H. Zhao, Q. Xiao, and C. Wang, “A joint UA V trajectory, user association, and beamforming design strategy for multi-UA V-assisted ISAC systems,”IEEE Internet Things J., vol. 11, pp. 29360–29374, Sept. 2024

    2024

  139. [147]

    Energy-efficient multi-agent reinforcement learning for UA V trajectory optimization in cell-free massive MIMO networks,

    Z. Liu, J. Zhang, Y . Zeng, and B. Ai, “Energy-efficient multi-agent reinforcement learning for UA V trajectory optimization in cell-free massive MIMO networks,”IEEE Trans. Wireless Commun., vol. 24, pp. 5917–5930, Mar. 2025

    2025

  140. [148]

    Reinforcement learning-based energy-efficient data access for airborne users in civil aircrafts-enabled SAGIN,

    Q. Chen, W. Meng, S. Han, C. Li, and H.-H. Chen, “Reinforcement learning-based energy-efficient data access for airborne users in civil aircrafts-enabled SAGIN,”IEEE Trans. Green Commun. Netw, vol. 5, pp. 934–949, Feb. 2021

    2021

  141. [149]

    Energy efficient estimation in wireless sensor net- work with unmanned aerial vehicle,

    C. Zhan and G. Yao, “Energy efficient estimation in wireless sensor net- work with unmanned aerial vehicle,”IEEE Access, vol. 7, pp. 63519– 63530, May 2019

    2019

  142. [150]

    MIMO broadcasting for simultaneous wireless information and power transfer,

    R. Zhang and C. K. Ho, “MIMO broadcasting for simultaneous wireless information and power transfer,”IEEE Trans. Wireless Commun., vol. 12, pp. 1989–2001, Mar. 2013

    1989

  143. [151]

    Wireless networks with RF energy harvesting: A contemporary survey,

    X. Lu, P. Wang, D. Niyato, D. I. Kim, and Z. Han, “Wireless networks with RF energy harvesting: A contemporary survey,”IEEE Commun. Surv. Tutor., vol. 17, pp. 757–789, Nov. 2015

    2015

  144. [152]

    UA V-enabled SWIPT in IoT networks for emergency communications,

    W. Feng, J. Tang, Y . Yu, J. Song, N. Zhao, G. Chen, K.-K. Wong, and J. Chambers, “UA V-enabled SWIPT in IoT networks for emergency communications,”IEEE Wirel. Commun., vol. 27, pp. 140–147, Jul. 2020

    2020

  145. [153]

    UA V-assisted cooperative communications with time-sharing information and power transfer,

    S. Yin, Y . Zhao, L. Li, and F. R. Yu, “UA V-assisted cooperative communications with time-sharing information and power transfer,” IEEE Trans. Veh. Technol., vol. 69, pp. 1554–1567, Nov. 2020

    2020

  146. [154]

    UA V-assisted SWIPT in internet of things with power splitting: Tra- jectory design and power allocation,

    F. Huang, J. Chen, H. Wang, G. Ding, Z. Xue, Y . Yang, and F. Song, “UA V-assisted SWIPT in internet of things with power splitting: Tra- jectory design and power allocation,”IEEE Access, vol. 7, pp. 68260– 68270, May 2019

    2019

  147. [155]

    Simultaneous wireless information and power transfer for multiuser UA V-enabled IoT networks,

    C. Jeong and S. H. Chae, “Simultaneous wireless information and power transfer for multiuser UA V-enabled IoT networks,”IEEE Internet Things J., vol. 8, pp. 8044–8055, May 2021

    2021

  148. [156]

    Multiple- UA V-assisted SWIPT in internet of things: User association and power allocation,

    F. Huang, J. Chen, H. Wang, G. Ding, Y . Gong, and Y . Yang, “Multiple- UA V-assisted SWIPT in internet of things: User association and power allocation,”IEEE Access, vol. 7, pp. 124244–124255, Aug. 2019

    2019

  149. [157]

    User scheduling and trajectory optimization for energy-efficient IRS-UA V networks with SWIPT,

    S. Zargari, A. Hakimi, C. Tellambura, and S. Herath, “User scheduling and trajectory optimization for energy-efficient IRS-UA V networks with SWIPT,”IEEE Trans. Veh. Technol., vol. 72, pp. 1815–1830, Feb. 2023

    2023

  150. [158]

    Joint communi- cation and trajectory design for intelligent reflecting surface empowered UA V SWIPT networks,

    Z. Li, W. Chen, H. Cao, H. Tang, K. Wang, and J. Li, “Joint communi- cation and trajectory design for intelligent reflecting surface empowered UA V SWIPT networks,”IEEE Trans. Veh. Technol., vol. 71, pp. 12840– 12855, Dec. 2022

    2022

  151. [159]

    TD3 algorithm-based SWIPT with UA V-RIS assistance for MIMO communication,

    A. A. Puspitasari and B. M. Lee, “TD3 algorithm-based SWIPT with UA V-RIS assistance for MIMO communication,”IEEE Trans. Veh. Technol., vol. 74, pp. 6284–6293, Apr. 2025

    2025

  152. [160]

    Joint optimization of STAR-RIS assisted UA V communication systems,

    Q. Zhang, Y . Zhao, H. Li, S. Hou, and Z. Song, “Joint optimization of STAR-RIS assisted UA V communication systems,”IEEE Wireless Commun. Lett., vol. 11, pp. 2390–2394, Nov. 2022

    2022

  153. [161]

    IEEE standard for technical requirements for emergency cellular communication system based on fixed-wing unmanned aircraft system,

    “IEEE standard for technical requirements for emergency cellular communication system based on fixed-wing unmanned aircraft system,” IEEE Std 1937.12-2025, pp. 1–28, 2025

    1937

  154. [162]

    Toward near-space communication network in the 6G and beyond era,

    X. Liu, Z. Gao, Z. Wan, Z. Wu, T. Li, T. Mao, X. Liang, D. Zheng, and J. Zhang, “Toward near-space communication network in the 6G and beyond era,”Space: Science & Technology, vol. 5, p. 0337, 2025

    2025

  155. [163]

    Computing in the sky: A survey on intelligent ubiquitous computing for UA V-assisted 6G networks and industry 4.0/5.0,

    S. H. Alsamhi, A. V . Shvetsov, S. Kumar, J. Hassan, M. A. Alhartomi, S. V . Shvetsova, R. Sahal, and A. Hawbani, “Computing in the sky: A survey on intelligent ubiquitous computing for UA V-assisted 6G networks and industry 4.0/5.0,”Drones, vol. 6, Jul. 2022

    2022

  156. [164]

    A survey on UA V control with multi-agent reinforcement learning,

    C. C. Ekechi, T. Elfouly, A. Alouani, and T. Khattab, “A survey on UA V control with multi-agent reinforcement learning,”Drones, vol. 9, Jul. 2025

    2025

  157. [165]

    Deep learning for spatio-temporal fusion in land surface temperature estimation: A comprehensive survey, experimental analysis, and future trends,

    S. Bouaziz, A. Hafiane, R. Canals, and R. Nedjai, “Deep learning for spatio-temporal fusion in land surface temperature estimation: A comprehensive survey, experimental analysis, and future trends,” 2024

    2024

  158. [166]

    Digital twin-assisted space-air-ground integrated multi-access edge computing for low-altitude economy: An online decentralized optimization approach,

    L. He, G. Sun, Z. Sun, J. Wang, H. Du, D. Niyato, J. Liu, and V . C. M. Leung, “Digital twin-assisted space-air-ground integrated multi-access edge computing for low-altitude economy: An online decentralized optimization approach,”arXiv preprint arXiv: 2411.09712, 2025

    2025 arXiv

  159. [167]

    A comprehensive survey of digital twins: Applications, technologies and security challenges,

    S. R. Jeremiah, A. El Azzaoui, N. N. Xiong, and J. H. Park, “A comprehensive survey of digital twins: Applications, technologies and security challenges,”J. Syst. Architect., vol. 151, p. 103120, Jun. 2024

    2024

  160. [168]

    SAGIN-4C-6G: A space-air-ground integrated network for enhanced communication, computation, caching and control in 6G,

    J. Liu, M. Sheng, D. Zhou, Z. Han, M.-S. Alouini, and W. Wang, “SAGIN-4C-6G: A space-air-ground integrated network for enhanced communication, computation, caching and control in 6G,” in2025 IEEE Wireless Communications and Networking Conference (WCNC), (Milan, Italy), pp. 1–3, 2025

    2025

  161. [169]

    Aerospace integrated networks innovation for empowering 6G: A survey and future challenges,

    D. Zhou, M. Sheng, J. Li, and Z. Han, “Aerospace integrated networks innovation for empowering 6G: A survey and future challenges,”IEEE Commun. Surv. Tutor., vol. 25, pp. 975–1019, Secondquarter 2023

    2023

Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.