pith. machine review for the scientific record. sign in

arxiv: 2604.09015 · v1 · submitted 2026-04-10 · 💻 cs.NI · cs.IT· math.IT

Recognition: unknown

Generative AI Agent Empowered Power Allocation for HAP Propulsion and Communication Systems

Xiaoyu Xing , Dingyi Lu , Peng Yang , Zehui Xiong , Xianbin Cao , Tony Q. S. Quek
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:56 UTC · model grok-4.3

classification 💻 cs.NI cs.ITmath.IT
keywords high altitude platformsgenerative AIpropulsion power modelbeamformingenergy efficiencyQoSpower allocation6G
0
0 comments X

The pith

Generative AI agent builds accurate propulsion power model for HAPs by capturing hull-propeller interference, allowing better energy split with communication systems.

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

The paper shows that an interactive generative AI agent can derive a propulsion power model for high-altitude platforms that includes aerodynamic interference between the hull and propeller, which earlier studies often ignored. With a reliable model of how much energy propulsion actually consumes, the remaining power budget for communications can be allocated more precisely to meet user quality-of-service targets while improving overall energy efficiency. The authors then formulate a beamforming optimization problem that balances these goals and solve it with their proposed QoS-enhanced energy-efficient algorithm, validated through simulations.

Core claim

By interacting with the generative AI agent, an accurate propulsion power consumption model is developed that takes into account the aerodynamic interference between the HAP's hull and the propeller. Assisted by the AI agent, a HAP beamforming problem is formulated to improve user QoS and enhance the energy efficiency of the HAP communication system, solved by the proposed QoS-enhanced energy-efficient (Q3E) beamforming algorithm.

What carries the argument

Interactive generative AI agent that derives the propulsion model accounting for hull-propeller interference and assists in formulating the beamforming problem.

If this is right

  • The accurate propulsion model prevents over-allocation of power to flight and frees more energy for communications.
  • The Q3E beamforming algorithm improves both user quality of service and system energy efficiency compared with standard approaches.
  • Better power budgeting reduces the chance of degraded beamforming performance caused by misestimated propulsion demands.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same AI-assisted modeling approach could extend to other energy-constrained aerial systems such as drones operating in urban environments.
  • Real-time updates to the propulsion model during flight could further adapt beamforming to changing wind or payload conditions.
  • Integration with satellite or terrestrial networks might use the shared power model to coordinate energy use across multiple platforms.

Load-bearing premise

The generative AI agent reliably captures the coupled interactions among aerodynamics, propulsion efficiency, and communication QoS without overfitting to limited simulation scenarios.

What would settle it

Flight tests of a physical HAP that measure actual propulsion power under different speeds and loads, then compare those measurements directly against the AI-derived model predictions.

Figures

Figures reproduced from arXiv: 2604.09015 by Dingyi Lu, Peng Yang, Tony Q. S. Quek, Xianbin Cao, Xiaoyu Xing, Zehui Xiong.

Figure 1
Figure 1. Figure 1: The conceptual architecture of LLM-based generative AI Agent: [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Generative AI Agent-based framework for HAP power consumption [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of power consumption obtained by the model in [22] and [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Deviation ratio of the aerodynamic drag model in [43] versus CFD [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of propeller efficiency obtained by the model in [22] and [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of velocity and pressure contours under different propeller [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The ANN architecture that has been contemplated provides a [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Propulsion power and deviation ratios for the derived model. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The QoS satisfaction ratio versus communication power constraints [PITH_FULL_IMAGE:figures/full_fig_p012_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The EE performance versus communication power constraints for [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
read the original abstract

High altitude platforms (HAPs) are emerging as a key enabler for 6G coverage, yet limited energy must be split between propulsion and communications. Most prior HAP studies ignore propulsion power or rely on surrogates that miss hull-propeller interference, leading to misestimated communication power budgets and degraded beamforming. More importantly, HAP power allocation is intrinsically a multi-system, multidisciplinary problem in which aerodynamics, propulsion-system efficiency, and communication-system performance (quality of service (QoS) and energy efficiency (EE)) are tightly coupled.To address these challenges, this paper designs an interactive generative artificial intelligence (AI)-empowered HAP power allocation agent.By interacting with the AI agent, we develop an accurate propulsion power consumption model that takes into account the aerodynamic interference between the HAP's hull and the propeller. Assisted by the AI agent, we further formulate a HAP beamforming problem to improve user QoS and enhance the EE of the HAP communication system.This paper also proposes a QoS-enhanced energy-efficient (Q3E) beamforming algorithm to solve the formulated problem.Simulation results demonstrate the accuracy of the propulsion-power model and the effectiveness of the Q3E algorithm.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that an interactive generative AI agent can be used to derive an accurate propulsion power consumption model for high-altitude platforms (HAPs) that incorporates aerodynamic interference between the hull and propeller. This model enables formulation of a beamforming optimization problem balancing user QoS and system energy efficiency (EE); the authors propose a QoS-enhanced energy-efficient (Q3E) beamforming algorithm to solve the problem and report that simulations confirm both the model's accuracy and the algorithm's effectiveness.

Significance. If the AI-derived propulsion model is shown to be physically grounded and the Q3E algorithm delivers measurable EE gains while meeting QoS targets, the work would advance integrated multidisciplinary optimization for energy-limited HAPs in 6G networks, addressing a common simplification in prior studies that either neglect propulsion power or use overly coarse surrogates.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'simulation results demonstrate the accuracy of the propulsion-power model' is load-bearing for the paper's contribution on multidisciplinary power allocation, yet the abstract supplies no quantitative metrics (e.g., RMSE, R², or percentage error), baselines, error bars, or comparisons to independent references such as blade-element momentum theory with interference corrections or CFD data for HAP geometries.
  2. [Simulation results] Simulation results (as described): The validation of the hull-propeller interference term appears confined to internal, self-consistent scenarios without reported external physical benchmarks; this directly affects the credibility of the claimed tight coupling between aerodynamics, propulsion efficiency, and communication QoS/EE that justifies the Q3E algorithm.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by including at least one key quantitative result (e.g., EE improvement percentage or model error) to allow readers to gauge the magnitude of the claimed gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and insightful comments on our manuscript. We agree that strengthening the quantitative validation of the propulsion power model is important for supporting the paper's claims on multidisciplinary HAP power allocation. We have revised the abstract and simulation sections to address these points directly while preserving the core contribution of the generative AI agent and the Q3E algorithm.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'simulation results demonstrate the accuracy of the propulsion-power model' is load-bearing for the paper's contribution on multidisciplinary power allocation, yet the abstract supplies no quantitative metrics (e.g., RMSE, R², or percentage error), baselines, error bars, or comparisons to independent references such as blade-element momentum theory with interference corrections or CFD data for HAP geometries.

    Authors: We agree that the abstract should include quantitative support for the accuracy claim. In the revised manuscript, we have updated the abstract to report the root-mean-square error (RMSE) and R² values obtained from the AI-derived model against blade-element momentum theory with interference corrections. Error bars have been added to the corresponding simulation figures, and the abstract now explicitly references these metrics along with the percentage error relative to the theoretical baseline. revision: yes

  2. Referee: [Simulation results] Simulation results (as described): The validation of the hull-propeller interference term appears confined to internal, self-consistent scenarios without reported external physical benchmarks; this directly affects the credibility of the claimed tight coupling between aerodynamics, propulsion efficiency, and communication QoS/EE that justifies the Q3E algorithm.

    Authors: We acknowledge the importance of external benchmarks for credibility. The revised simulation section now includes explicit comparisons of the interference term to independent literature references on HAP aerodynamics, reporting percentage errors and consistency with blade-element momentum theory. While original CFD simulations for the exact HAP geometry were not feasible within the scope of this work due to resource limitations, the generative AI agent was prompted to incorporate established interference corrections from the literature; we have added a limitations paragraph clarifying this and strengthened the discussion of how the model enables the observed QoS-EE trade-off in the Q3E algorithm. revision: partial

Circularity Check

0 steps flagged

No circularity detected; derivation self-contained

full rationale

The abstract and available text describe developing a propulsion model via AI-agent interaction and a Q3E beamforming algorithm, with accuracy shown via simulations. No equations, fitted parameters, or derivations are provided that reduce by construction to self-defined inputs. No self-citations, uniqueness theorems, or ansatzes are quoted as load-bearing. Per rules, internal simulation validation alone does not trigger circularity without explicit reduction (e.g., Eq. X = input by definition). The chain remains independent of the target claims.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the AI agent and Q3E algorithm are described conceptually without mathematical details or new postulated components.

pith-pipeline@v0.9.0 · 5524 in / 1179 out tokens · 35291 ms · 2026-05-10T16:56:41.472404+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

57 extracted references · 6 canonical work pages · 2 internal anchors

  1. [1]

    Enhancing next-generation urban connectivity: Is the integrated HAPS-terrestrial network a solution?

    A. A. Shamsabadi, A. Yadav, and H. Yanikomeroglu, “Enhancing next-generation urban connectivity: Is the integrated HAPS-terrestrial network a solution?”IEEE Commun. Lett., vol. 28, no. 5, pp. 1112– 1116, May 2024

    2024

  2. [2]

    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, no. 12, pp. 3327–3354, Dec. 2024

    2024

  3. [3]

    Survey on near-space information networks: Channel modeling, networking, and transmission perspectives,

    X. Cao, X. Su, P. Yang, Y . Gao, D. O. Wu, and T. Q. S. Quek, “Survey on near-space information networks: Channel modeling, networking, and transmission perspectives,”IEEE Commun. Surveys Tuts., early access, Mar 10, 2025, DOI:10.1109/COMST.2025.3549523

    work page doi:10.1109/comst.2025.3549523 2025

  4. [4]

    Toward RIS-enhanced inte- grated terrestrial/non-terrestrial connectivity in 6G,

    P. Ramezani, B. Lyu, and A. Jamalipour, “Toward RIS-enhanced inte- grated terrestrial/non-terrestrial connectivity in 6G,”IEEE Netw., vol. 37, no. 3, pp. 178–185, May 2023

    2023

  5. [5]

    Near-space communications: The last piece of 6G space–air–ground–sea integrated network puzzle,

    H. Liu, T. Qin, null, T. Mao, K. Ying, Z. Wan, L. Qiao, R. Na, Z. Li, C. Hu, Y . Mei, T. Li, G. Wen, L. Chen, Z. Wu, R. Liu, G. Chen, S. Wang, and D. Zheng, “Near-space communications: The last piece of 6G space–air–ground–sea integrated network puzzle,”Space Sci. Technol., vol. 4, p. 0176, Aug. 2024

    2024

  6. [6]

    Survey on HA2G and A2A channel measurement and modeling for airborne maneuvering networks,

    P. An, S. Yang, J. Sun, J. Li, P. Yang, and X. Cao, “Survey on HA2G and A2A channel measurement and modeling for airborne maneuvering networks,”Chin. J. Aeronaut., p. 104036, early access, Dec. 24, 2025, DOI:10.1016/j.cja.2025.104036

    work page doi:10.1016/j.cja.2025.104036 2025

  7. [7]

    Deep joint semantic coding and beamforming for near-space airship- borne massive MIMO network,

    M. Wu, Z. Gao, Z. Wang, D. Niyato, G. K. Karagiannidis, and S. Chen, “Deep joint semantic coding and beamforming for near-space airship- borne massive MIMO network,”IEEE J. Sel. Areas Commun., vol. 43, no. 1, pp. 260–278, Jan. 2025

    2025

  8. [8]

    Reliability and delay analysis of 3-dimensional networks with multi-connectivity: Satellite, HAPs, and cellular communications,

    F. Salehi, M. Ozger, and C. Cavdar, “Reliability and delay analysis of 3-dimensional networks with multi-connectivity: Satellite, HAPs, and cellular communications,”IEEE Trans. Netw. Serv. Manage., vol. 21, no. 1, pp. 437–450, Feb. 2024

    2024

  9. [9]

    Joint optimization of caching, computing, and trajectory planning in aerial mobile edge computing networks: An MADDPG approach,

    H. Sun, Y . Zhou, H. Zhang, L. Ale, H. Dai, and N. Zhang, “Joint optimization of caching, computing, and trajectory planning in aerial mobile edge computing networks: An MADDPG approach,”IEEE Internet Things J., vol. 11, no. 24, pp. 40 996–41 007, Dec. 2024

    2024

  10. [10]

    Robust multiuser beamforming for IRS-enhanced near-space downlink communications coexisting with satellite system,

    S. Xu, J. Liu, T. K. Rodrigues, and N. Kato, “Robust multiuser beamforming for IRS-enhanced near-space downlink communications coexisting with satellite system,”IEEE Internet Things J., vol. 9, pp. 14 900–14 912, Aug. 2022

    2022

  11. [11]

    Joint user association and beam- forming in integrated satellite-HAPS-ground networks,

    S. Liu, H. Dahrouj, and M.-S. Alouini, “Joint user association and beam- forming in integrated satellite-HAPS-ground networks,”IEEE Trans. Veh. Technol., vol. 73, no. 4, pp. 5162–5178, Apr. 2024

    2024

  12. [12]

    Uplink multiple access with semi-grant-free transmission in inte- grated satellite-aerial-terrestrial networks,

    H. Kong, M. Lin, L. Han, W.-P. Zhu, Z. Ding, and M.-S. Alouini, “Uplink multiple access with semi-grant-free transmission in inte- grated satellite-aerial-terrestrial networks,”IEEE J. Sel. Areas Commun., vol. 41, pp. 1723–1736, Jan. 2023

    2023

  13. [13]

    Energy efficiency maximization in RIS-assisted SWIPT networks with RSMA: A PPO-based approach,

    R. Zhang, K. Xiong, Y . Lu, P. Fan, D. W. K. Ng, and K. B. Letaief, “Energy efficiency maximization in RIS-assisted SWIPT networks with RSMA: A PPO-based approach,”IEEE J. Sel. Areas Commun., vol. 41, no. 5, pp. 1413–1430, May 2023

    2023

  14. [14]

    Power allocation and performance evaluation for NOMA-aided in- tegrated satellite-HAP-terrestrial networks under practical limitations,

    K. Guo, H. Shuai, K. An, F. Zhou, T. A. Tsiftsis, X. Li, and M. Wu, “Power allocation and performance evaluation for NOMA-aided in- tegrated satellite-HAP-terrestrial networks under practical limitations,” IEEE Internet Things J., vol. 11, no. 7, pp. 13 002–13 017, Apr. 2024

    2024

  15. [15]

    Generative AI-powered aerial access networks: Recent studies and future outlook,

    T.-H. Nguyen, T. K. Nguyen, V . N. Quoc Bao, H. Park, and L. Park, “Generative AI-powered aerial access networks: Recent studies and future outlook,” in2024 15th International Conference on Information and Communication Technology Convergence (ICTC), Oct. 2024, pp. 529–533

    2024

  16. [16]

    AI-based mobility-aware energy efficient resource allocation and trajectory design for NFV enabled aerial networks,

    M. Pourghasemian, M. R. Abedi, S. S. Hosseini, N. Mokari, M. R. Javan, and E. A. Jorswieck, “AI-based mobility-aware energy efficient resource allocation and trajectory design for NFV enabled aerial networks,”IEEE Trans. Green Commun. Networking, vol. 7, no. 1, pp. 281–297, Mar. 2023

    2023

  17. [17]

    Spatial path analysis for high-altitude solar-powered airship with maximum net energy,

    W. Zhu, B. Zhang, L. Zhang, Y . Xu, and K. Yang, “Spatial path analysis for high-altitude solar-powered airship with maximum net energy,” Aerosp. Sci. Technol., vol. 117, p. 106922, Oct. 2021

    2021

  18. [18]

    Multidisciplinary design of high altitude airship based on solar energy optimization,

    L. Zhang, W. Zhu, H. Du, and M. Lv, “Multidisciplinary design of high altitude airship based on solar energy optimization,”Aerosp. Sci. Technol., vol. 110, p. 106440, Mar. 2021

    2021

  19. [19]

    High-altitude airship propulsion system optimal design and experiment based on energy balance,

    W. Dongchen, S. Bifeng, J. Jun, and W. Haifeng, “High-altitude airship propulsion system optimal design and experiment based on energy balance,”Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng., vol. 237, pp. 3 – 16, Jan. 2023

    2023

  20. [20]

    A novel multi- fidelity optimization framework for high-altitude propellers,

    N. Mourousias, B. G. Marinus, and M. C. Runacres, “A novel multi- fidelity optimization framework for high-altitude propellers,”Aerosp. Sci. Technol., vol. 153, p. 109407, Oct. 2024

    2024

  21. [21]

    Uncertainty quantifica- tion study of the aerodynamic performance of high-altitude propellers,

    N. Mourousias, A. Garc ´ıa-Guti´errez, A. Malim, D. Dom ´ınguez Fern´andez, B. G. Marinus, and M. C. Runacres, “Uncertainty quantifica- tion study of the aerodynamic performance of high-altitude propellers,” Aerosp. Sci. Technol., vol. 133, p. 108108, Feb. 2023

    2023

  22. [22]

    A rule-based online energy management strategy for long-endurance stratospheric airships,

    K. Song, Z. Li, Y . Zhang, X. Wang, G. Xu, and H. Zhang, “A rule-based online energy management strategy for long-endurance stratospheric airships,”Aerosp. Sci. Technol., vol. 151, p. 109266, May 2024

    2024

  23. [23]

    Least-to-Most Prompting Enables Complex Reasoning in Large Language Models

    D. Zhou, N. Sch ¨arli, L. Hou, J. Wei, N. Scales, X. Wang, D. Schu- urmans, C. Cui, O. Bousquet, Q. Le, and E. Chi, “Least-to-most prompting enables complex reasoning in large language models,” 2023, arXiv:2205.10625

    work page internal anchor Pith review arXiv 2023

  24. [24]

    Empowering biomedical discov- ery with AI agents,

    S. Gao, A. Fang, Y . Huang, V . Giunchiglia, A. Noori, J. R. Schwarz, Y . Ektefaie, J. Kondic, and M. Zitnik, “Empowering biomedical discov- ery with AI agents,”Cell, vol. 187, no. 22, pp. 6125–6151, Oct. 2024

    2024

  25. [25]

    Making network configuration human-friendly,

    C. Wang, M. Scazzariello, A. Farshin, D. Kostic, and M. Chiesa, “Mak- ing network configuration human friendly,” 2023,arXiv:2309.06342

    work page arXiv 2023

  26. [26]

    Autohma-llm: Efficient task coordination and execution in heterogeneous multi-agent systems using hybrid large language models,

    T. Yang, P. Feng, Q. Guo, J. Zhang, X. Zhang, J. Ning, X. Wang, and Z. Mao, “Autohma-llm: Efficient task coordination and execution in heterogeneous multi-agent systems using hybrid large language models,” IEEE Trans. Cognit. Commun. Networking, vol. 11, no. 2, pp. 987–998, Apr. 2025

    2025

  27. [27]

    Managing linux servers with LLM-based AI agents: An empirical evaluation with GPT4,

    C. Cao, F. Wang, L. Lindley, and Z. Wang, “Managing linux servers with LLM-based AI agents: An empirical evaluation with GPT4,”Mach. Learn. Appl., vol. 17, p. 100570, Sep. 2024

    2024

  28. [28]

    Interactive AI with retrieval-augmented generation for next generation networking,

    R. Zhang, H. Du, Y . Liu, D. Niyato, J. Kang, S. Sun, X. Shen, and H. V . Poor, “Interactive AI with retrieval-augmented generation for next generation networking,”IEEE Netw., pp. 1–1, Nov. 2024. 14

    2024

  29. [29]

    The Rise and Potential of Large Language Model Based Agents: A Survey

    Z. Xi, W. Chen, X. Guo, W. He, Y . Ding, B. Hong, M. Zhang, J. Wang, S. Jin, E. Zhou, R. Zheng, X. Fan, X. Wang, L. Xiong, Y . Zhou, W. Wang, C. Jiang, Y . Zou, X. Liu, Z. Yin, S. Dou, R. Weng, W. Cheng, Q. Zhang, W. Qin, Y . Zheng, X. Qiu, X. Huang, and T. Gui, “The rise and potential of large language model based agents: A survey,” 2023, arXiv:2309.07864

    work page internal anchor Pith review arXiv 2023

  30. [30]

    A figure of merit based computational heat transfer method for aerothermal analysis of airborne electronics system lru,

    M. H. Noor, A. Sarosh, U. Riaz, M. Adnan, and O. A. A. Awan, “A figure of merit based computational heat transfer method for aerothermal analysis of airborne electronics system lru,” in2022 19th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Aug. 2022, pp. 70–75

    2022

  31. [31]

    A non-stationary 3-D wideband channel model for intelligent reflecting surface-assisted HAP-MIMO communication systems,

    Z. Lian, Y . Su, Y . Wang, and L. Jiang, “A non-stationary 3-D wideband channel model for intelligent reflecting surface-assisted HAP-MIMO communication systems,”IEEE Trans. Veh. Technol., vol. 71, no. 2, pp. 1109–1123, Feb. 2022

    2022

  32. [32]

    MIMO- NOMA enabled sectorized cylindrical massive antenna array for HAPS with spatially correlated channels,

    R. Shafie, M. J. Omidi, O. Abbasi, and H. Yanikomeroglu, “MIMO- NOMA enabled sectorized cylindrical massive antenna array for HAPS with spatially correlated channels,”IEEE Trans. Wireless Commun., vol. 23, no. 10, pp. 15 155–15 168, Oct. 2024

    2024

  33. [33]

    Integration of los MIMO RF and optical links via HAPS for high-capacity satellite feeder links,

    S. H. Hariq and R. Iyer Seshadri, “Integration of los MIMO RF and optical links via HAPS for high-capacity satellite feeder links,” inProc. IEEE 100th Veh. Technol. Conf. (VTC2024-Fall), Oct. 2024, pp. 1–5

    2024

  34. [34]

    Data-driven deep learning based hybrid beamforming for aerial massive MIMO-OFDM systems with implicit CSI,

    Z. Gao, M. Wu, C. Hu, F. Gao, G. Wen, D. Zheng, and J. Zhang, “Data-driven deep learning based hybrid beamforming for aerial massive MIMO-OFDM systems with implicit CSI,”IEEE J. Sel. Areas Commun., vol. 40, no. 10, pp. 2894–2913, Oct. 2022

    2022

  35. [35]

    QoS-aware precoding in downlink massive MIMO LEO satellite communications,

    Y . Huang, L. You, C. G. Tsinos, W. Wang, and X. Gao, “QoS-aware precoding in downlink massive MIMO LEO satellite communications,” IEEE Commun. Lett., vol. 27, pp. 1560–1564, Jan. 2023

    2023

  36. [36]

    Beam squint-aware integrated sensing and communications for hybrid massive MIMO LEO satellite systems,

    L. You, X. Qiang, C. G. Tsinos, F. Liu, W. Wang, X. Gao, and B. Ot- tersten, “Beam squint-aware integrated sensing and communications for hybrid massive MIMO LEO satellite systems,”IEEE J. Sel. Areas Commun., vol. 40, no. 10, pp. 2994–3009, Oct. 2022

    2022

  37. [37]

    Uxnb-enabled cell-free massive MIMO with HAPS-assisted sub-THz backhauling,

    O. Abbasi and H. Yanikomeroglu, “Uxnb-enabled cell-free massive MIMO with HAPS-assisted sub-THz backhauling,”IEEE Trans. Veh. Technol., vol. 73, no. 5, pp. 6937–6953, May 2024

    2024

  38. [38]

    A survey of recent advances in optimization methods for wireless communications,

    Y .-F. Liu, T.-H. Chang, M. Hong, Z. Wu, A. Man-Cho So, E. A. Jorswieck, and W. Yu, “A survey of recent advances in optimization methods for wireless communications,”IEEE J. Sel. Areas Commun., vol. 42, no. 11, pp. 2992–3031, Nov. 2024

    2024

  39. [39]

    Beamforming design using UE positions and 3D terrain-building in HAPS system,

    Z. Li, S. Yang, Y . Yin, M. Bouazizi, and T. Ohtsuki, “Beamforming design using UE positions and 3D terrain-building in HAPS system,” in Proc. IEEE 100th Veh. Technol. Conf. (VTC2024-Fall), Oct. 2024, pp. 1–7

    2024

  40. [40]

    Hemispherical mas- sive MIMO architecture for high-altitude platform station (HAPS),

    O. Abbasi, H. Yanikomeroglu, and G. Kaddoum, “Hemispherical mas- sive MIMO architecture for high-altitude platform station (HAPS),” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Apr. 2024, pp. 1–6

    2024

  41. [41]

    Energy sustainability in dense radio access networks via high altitude platform stations,

    M. Salamatmoghadasi, A. Mehrabian, and H. Yanikomeroglus, “Energy sustainability in dense radio access networks via high altitude platform stations,”IEEE Networking Lett., vol. 6, no. 1, pp. 21–25, Mar. 2024

    2024

  42. [42]

    Stratospheric wind field feature extraction and energy management for hybrid electric solar airship with deep reinforcement learning,

    Y . Liu, K. Sun, and M. Lv, “Stratospheric wind field feature extraction and energy management for hybrid electric solar airship with deep reinforcement learning,”Sustainable Energy Technol. Assess., vol. 71, p. 103993, Nov. 2024

    2024

  43. [43]

    High-altitude airship variable-pitch propeller performance analysis at multiple altitudes,

    W. Dongchen, S. Bifeng, J. Jun, and X. Dong, “High-altitude airship variable-pitch propeller performance analysis at multiple altitudes,” Proc. Inst. Mech. Eng., Part G: J. Aerosp. Eng., vol. 237, pp. 2961– 2973, Apr. 2023

    2023

  44. [44]

    Station-keeping HAPS mission through optimal sprint and drift trajectories,

    A. Delgado, D. Dom ´ınguez, J. Gonzalo, and A. Escapa, “Station-keeping HAPS mission through optimal sprint and drift trajectories,”Aerosp. Sci. Technol., vol. 152, p. 109365, Sep. 2024

    2024

  45. [45]

    Stratospheric airship trajectory planning in wind field using deep reinforcement learning,

    L. Qi, X. Yang, F. Bai, X. Deng, and Y . Pan, “Stratospheric airship trajectory planning in wind field using deep reinforcement learning,” Adv. Space Res., vol. 75, no. 1, pp. 620–634, Jan. 2025

    2025

  46. [46]

    Conceptual design and feasibility study of winged hybrid airship,

    A. Gangadhar, M. Manikandan, D. Rajaram, and D. Mavris, “Conceptual design and feasibility study of winged hybrid airship,”Aerospace, vol. 9, no. 1, Dec. 2022

    2022

  47. [47]

    S. F. Hoerner,Fluid-Dynamic Drag: Practical Information on Aerody- namic Drag and Hydrodynamic Resistance. Brick Town, New Jersey: Hoerner Fluid Dynamics, 1965

    1965

  48. [48]

    Estimation of volumetric drag coefficient of two-dimensional body of revolution,

    M. I. Alam and R. S. Pant, “Estimation of volumetric drag coefficient of two-dimensional body of revolution,”Journal of Aircraft, vol. 56, no. 5, pp. 2080–2082, 2019

    2080

  49. [49]

    A comparative study of conventional and tri-lobed stratospheric airships,

    M. Manikandan and R. S. Pant, “A comparative study of conventional and tri-lobed stratospheric airships,”The Aeronautical Journal, vol. 125, no. 1290, pp. 1434–1466, Aug. 2021

    2021

  50. [50]

    Assessment of the effectiveness of stall delay and tip loss corrections for the simulation of small propeller performance with virtual blade model,

    L. S. Diehl and J. G. Coelho, “Assessment of the effectiveness of stall delay and tip loss corrections for the simulation of small propeller performance with virtual blade model,”Eur. J. Mech. B. Fluids, vol. 105, pp. 34–43, May 2024

    2024

  51. [51]

    A function improving tip loss correction of blade-element momentum theory for wind turbines,

    W. Z. S. Wei Zhong, Tongguang Wang and W. J. Zhu, “A function improving tip loss correction of blade-element momentum theory for wind turbines,”Int. J. Sustainable Energy, vol. 43, no. 1, p. 2352794, Dec. 2024

    2024

  52. [52]

    Lexicographic multi-objective mpc for constrained nonlinear systems with changing objective prioritization,

    D. He, H. Li, and H. Du, “Lexicographic multi-objective mpc for constrained nonlinear systems with changing objective prioritization,” Automatica, vol. 125, p. 109433, Mar. 2021

    2021

  53. [53]

    Adaptive subarray segmentation: A new paradigm of spatial non- stationary near-field channel estimation for xl-mimo systems,

    S. Yang, P. An, P. Yang, X. Cao, D. Oliver Wu, and T. Q. S. Quek, “Adaptive subarray segmentation: A new paradigm of spatial non- stationary near-field channel estimation for xl-mimo systems,”IEEE Trans. Commun., vol. 74, pp. 2220–2234, early access, Dec. 15, 2026, DOI:10.1109/TCOMM.2025.3644475

    work page doi:10.1109/tcomm.2025.3644475 2026

  54. [54]

    HAPS-based interference suppression through null broadening with directivity control in space-air-ground integrated net- works,

    Y . Kawamoto, A. Matsushita, S. Verma, N. Kato, K. Kaneko, A. Sata, and M. Hangai, “HAPS-based interference suppression through null broadening with directivity control in space-air-ground integrated net- works,”IEEE Trans. Veh. Technol., vol. 72, no. 12, pp. 16 098–16 107, Dec. 2023

    2023

  55. [55]

    Movable cell- free massive mimo for high-speed train communications: A ppo-based antenna position optimization,

    J. Dai, Y . Liu, J. Zheng, R. Zhang, J. Zhang, and B. Ai, “Movable cell- free massive mimo for high-speed train communications: A ppo-based antenna position optimization,” in2025 IEEE International Conference on Communications Workshops (ICC Workshops), Jun. 2025, pp. 38–43

    2025

  56. [56]

    Multi-UA V-aided power-up and data collection: Multi-agent DQL with genetic algorithm approach,

    V . That, S. Muy, and J.-R. Lee, “Multi-UA V-aided power-up and data collection: Multi-agent DQL with genetic algorithm approach,”Expert Syst. Appl., vol. 297, p. 129401, Feb. 2026

    2026

  57. [57]

    Cellular network from the sky: Toward people-centered smart communities,

    B. E. Y . Belmekki, A. J. Aljohani, S. A. Althubaity, A. A. Harthi, K. Bean, A. Aijaz, and M.-S. Alouini, “Cellular network from the sky: Toward people-centered smart communities,”IEEE Open J. Commun. Soc., vol. 5, pp. 1916–1936, Mar. 2024

    1916