Pith. sign in

REVIEW 3 major objections 2 minor 60 references

RSMA Enabled Hierarchical UAV Networks with Non Linear Energy Harvesting: Outage Probability Analysis and UAV Placement Optimization

T0 review · 3 major / 2 minor · reviewed 2026-06-27 · grok-4.3

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

Pith's one-line read A hierarchical UAV network using RSMA and non-linear energy harvesting from other UAVs achieves lower outage probability than benchmarks under hardware impairments and imperfect CSI.

desk verdict The paper's first-of-its-kind outage analysis for RSMA in hierarchical UAVs with non-linear EH from other UAVs is the real addition, but the donor energy balance is not closed. read the letter →

arxiv 2606.07284 v1 pith:DDJLBHAU submitted 2026-06-05 eess.SP

Faicel Khennoufa , Khelil Abdellatif , Metin Ozturk , Halim Yanikomeroglu , Safwan Alfattani This is my paper · ORCID
classification eess.SP
keywords UAVnetworksRSMAenergyharvestingoutageprobabilityNakagami-mfadinghardwareimpairmentsimperfectCSI6G
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

The paper proposes a hierarchical ad hoc UAV network in which UAVs harvest energy from surrounding UAVs via non-linear models and apply rate-splitting multiple access to serve ground IoT devices. It incorporates hardware impairments and imperfect channel state information, then derives exact and high-power approximate outage probability expressions over Nakagami-m fading for individual IoT devices, cluster-member UAVs, and the full system. Two optimization problems are formulated to place UAVs for better reliability. The resulting system is shown to outperform all considered benchmarks in outage probability.

What carries the argument

Outage probability expressions for ground IoT devices, each CMU, and the overall system, together with two UAV-placement optimization problems, all derived while accounting for non-linear EH, HWI, and ICSI.

What would settle it

A measurement campaign or simulation with real UAV channels showing that the derived outage probabilities are higher than those of a benchmark terrestrial-EH system under identical HWI and ICSI conditions.

Watch

Extended reading notes

Core claim

Closed-form outage probability expressions are obtained for the proposed RSMA-enabled hierarchical UAV network with non-linear EH from aerial sources, HWI, and ICSI over Nakagami-m channels; the expressions and the subsequent UAV-placement optimizations demonstrate strictly lower outage than benchmark schemes across the evaluated regimes.

Load-bearing premise

Energy harvesting from surrounding UAVs is more sustainable than from terrestrial sources and Nakagami-m fading accurately represents the channels in the hierarchical setup.

Editorial extensions

If this is right

  • The network supports longer continuous operation because aerial EH avoids terrestrial path-loss limitations.
  • RSMA remains effective for serving multiple IoT devices even when HWI and ICSI are present.
  • Approximate high-power outage formulas simplify performance evaluation without losing the ordering versus benchmarks.
  • Optimized UAV placement directly improves both individual and system-level reliability metrics.

Reading between the lines

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

  • The same derivation approach could be applied to other multiple-access schemes or to hybrid aerial-terrestrial energy sources.
  • Results suggest that scaling the number of harvesting UAVs may further reduce outage without increasing terrestrial infrastructure.
  • Placement optimization may interact with trajectory planning in mobile UAV scenarios not examined here.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 2 minor

Summary. The manuscript proposes a hierarchical ad hoc UAV network using rate-splitting multiple access (RSMA) with non-linear energy harvesting (EH) from neighboring UAVs, incorporating hardware impairments (HWI) and imperfect CSI. It derives outage probability expressions for ground IoT devices, cluster member UAVs (CMUs), and the overall system over Nakagami-m fading, provides high-SNR approximations, formulates two UAV placement optimization problems, and reports that the proposed system outperforms benchmarks in outage probability.

Significance. If the outage derivations hold and the non-linear EH model is sustainable, the work could provide useful closed-form expressions and placement insights for RSMA-enabled UAV networks in 6G scenarios with energy constraints. The explicit treatment of HWI and ICSI is a positive step, but the lack of energy-balance verification for donor UAVs limits the strength of the sustainability and outperformance claims.

major comments (3)
  1. [Section II] System model (Section II): The non-linear EH model applies the received RF power from neighboring UAVs directly to the EH function without verifying that donor UAVs can sustain the assumed transmit powers given their own flight energy draw, onboard battery limits, and mission duration; this unclosed energy loop makes the derived outage expressions and the superiority claim conditional on an unstated surplus assumption that is not tested in the numerical results.
  2. [Section III] Outage probability analysis (Section III): The expressions for IoT device and CMU outage probabilities incorporate HWI and ICSI but rely on the EH input power being independent of donor depletion; if donor transmit power cannot be maintained, the Nakagami-m based closed forms and high-SNR approximations lose their claimed practical relevance.
  3. [Section V] Numerical results and optimization (Section V): The reported outperformance over benchmarks is shown via outage curves, yet no sensitivity plots or constraints are provided for donor UAV energy budgets or EH parameter variations, leaving the placement optimization results vulnerable to the same sustainability gap.
minor comments (2)
  1. [Section II] Notation for the non-linear EH parameters (e.g., saturation thresholds) should be defined consistently between the system model and the outage derivations to avoid ambiguity in the high-SNR regime.
  2. [Introduction] The abstract states the work is the first to consider RSMA with non-linear EH, HWI, and ICSI in hierarchical UAVs; a brief related-work table or explicit citation gaps would strengthen this claim.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments and the opportunity to improve our manuscript. We address each of the major comments point by point below, providing clarifications on the system assumptions and indicating the revisions we will make.

read point-by-point responses

  1. Referee: [Section II] System model (Section II): The non-linear EH model applies the received RF power from neighboring UAVs directly to the EH function without verifying that donor UAVs can sustain the assumed transmit powers given their own flight energy draw, onboard battery limits, and mission duration; this unclosed energy loop makes the derived outage expressions and the superiority claim conditional on an unstated surplus assumption that is not tested in the numerical results.

    Authors: We appreciate the referee's point on the practical sustainability of the donor UAVs' transmit powers. Our analysis focuses on the RF energy harvesting for communication purposes, with the assumption that UAVs have separate energy management for flight and propulsion, as is standard in UAV communication literature. The non-linear EH model is applied to the received power at the harvesting nodes. To address this, we will revise Section II to explicitly state that the transmit powers of donor UAVs are assumed to be maintained by their primary energy sources, independent of the EH process at other nodes. We will also note that a full energy balance including flight energy draw is beyond the scope of this work but acknowledge that the results are conditional on sufficient energy reserves at donors. revision: partial

  2. Referee: [Section III] Outage probability analysis (Section III): The expressions for IoT device and CMU outage probabilities incorporate HWI and ICSI but rely on the EH input power being independent of donor depletion; if donor transmit power cannot be maintained, the Nakagami-m based closed forms and high-SNR approximations lose their claimed practical relevance.

    Authors: The outage expressions in Section III are derived under the system model assumptions detailed in Section II. We will update the text in Section III to cross-reference the clarified assumptions regarding donor energy sustainability. This will ensure that the practical relevance of the closed-form expressions and approximations is discussed with the appropriate caveats, without altering the mathematical derivations themselves. revision: partial

  3. Referee: [Section V] Numerical results and optimization (Section V): The reported outperformance over benchmarks is shown via outage curves, yet no sensitivity plots or constraints are provided for donor UAV energy budgets or EH parameter variations, leaving the placement optimization results vulnerable to the same sustainability gap.

    Authors: We agree that sensitivity analysis would provide additional insights. In the revised manuscript, we will expand Section V to include a discussion on the sensitivity of the results to EH parameters and explicitly state the dependence on the donor energy assumption. While incorporating full energy budget constraints would require a significantly extended model, we will add textual analysis explaining the conditions under which the placement optimization holds. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: derivations rest on standard channel models and explicit assumptions without reduction to fitted inputs or self-citations.

full rationale

The provided abstract and skeptic summary describe standard derivations of outage probability over Nakagami-m channels incorporating HWI, ICSI, and a non-linear EH function as an input model. No equations are shown that equate a derived quantity back to a fitted parameter by construction, and no self-citations are invoked as load-bearing uniqueness theorems. The optimization steps follow the derived expressions rather than presupposing their results. The energy-balance concern raised by the skeptic is an unverified modeling assumption (correctness risk) rather than a definitional loop. The derivation chain is therefore self-contained against external benchmarks.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

Abstract-only review; ledger populated from stated elements in abstract. Relies on standard wireless models (Nakagami-m) and practical constraints (HWI, ICSI, non-linear EH) without further detail.

assumptions (2)
  • domain assumption Nakagami-m fading channels accurately model the propagation environment
    Invoked for all outage derivations in the abstract.
  • domain assumption Non-linear EH from peer UAVs is feasible and more sustainable than terrestrial sources
    Central modeling choice stated in abstract as motivation and constraint.

how reviews work

0 comments
Cite this review

Pith. "Pith review of RSMA Enabled Hierarchical UAV Networks with Non Linear Energy Harvesting: Outage Probability Analysis and UAV Placement Optimization." pith.science (2026). https://pith.science/paper/DDJLBHAU

@misc{pith2026260607284,
  author       = {Pith},
  title        = {Pith review of: RSMA Enabled Hierarchical UAV Networks with Non Linear Energy Harvesting: Outage Probability Analysis and UAV Placement Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DDJLBHAU}},
  note         = {Machine review of arXiv:2606.07284}
}
abstract

Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil applications. Although multi-UAV systems offer greater efficiency and cost-effectiveness than single-UAV deployments, their implementation still faces several fundamental challenges that limit their reliability, sustainability, and scalability. The limited onboard energy restricts mission duration and communication continuity. Therefore, wireless energy harvesting (EH) emerges as a promising solution to overcome this limitation. However, terrestrial energy sources experience path loss, making EH from surrounding UAVs more sustainable. Moreover, rate-splitting multiple access (RSMA) remains insufficiently explored in hierarchical UAV networks under hardware impairments (HWI) and imperfect channel state information (ICSI). This paper proposes a hierarchical ad hoc UAV network with non-linear EH and RSMA to enhance both energy and cost efficiency, where UAVs harvest energy from surrounding UAVs. For a practical scenario, we consider the effect of HWI and ICSI in our proposed system. To the best of the authors knowledge, this study is the first to investigate such a scenario in the literature. The outage probability expressions for ground Internet of things (IoT) devices, each CMU, and the overall outage probability of the proposed system are derived over Nakagami-$m$ fading channels while considering practical constraints such as HWI, ICSI, and non-linear EH. Additionally, approximate outage probability expressions are derived for high transmit power regimes. Subsequently, we formulate two optimization problems to enhance reliability and performance. Our findings indicate that the proposed system outperforms all benchmarks in terms of outage probability.

Figures

Figures reproduced from arXiv: 2606.07284 by the authors.

Figure 1
Figure 1. Hierarchical ad hoc UAV network with EH system [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Block diagram of the PS protocol for a hierarchical [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Outage probability performance of two ground IoT devices with different threshold values under HWI and ICSI. and efficiency in harvested energy, leading to an increased outage probability. This again emphasizes the significance of mitigating HWI and ICSI to fully exploit the benefits of EH in the proposed system [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Comparison of the hierarchical ad hoc UAV network with non-linear EH using NOMA and RSMA with different threshold values under HWI and ICSI. wider range of input power levels. The proposed system consistently achieves higher harvested energy compared to the ground-base…
Figure 8
Figure 8. Figure 8: Overall outage probability for RSMA and NOMA with different numbers of CMU, L = {4, 6, 8, 10} under HWI and ICSI. 0 5 10 15 20 25 30 35 40 45 50 10-4 10-3 10-2 10-1 100 [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 7
Figure 7. Figure 7: Comparison of the harvested energy between the ground-based EH and the proposed system; (a) EEH w.r.t., Pth with different Pt values; (b) EEH w.r.t., dl with different Pt values. nates and the EH factor (ρ) due to its random nature. The RSEL algorithm, on the other han…
Figure 10
Figure 10. Figure 10: CMU outage probability w.r.t. Pt for various opti￾mization algorithms: RSEL, HGCD, CPPF, and JCPPM under HWI and ICSI. with gains of approximately 8 dB and 12 dB in NOMA and RSMA, respectively. The proposed algorithms (OPM￾JCIS, CPPF, and JCPPM) further deliver superi…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

60 extracted references · 1 canonical work pages

  1. [1]

    Role and evolution of non-terrestrial networks toward 6G systems,

    A. Guidotti, A. Vanelli-Coralli, M. E. Jaafari, N. Chuberre, J. Puttonen, V. Schena, G. Rinelli, and S. Cioni, “Role and evolution of non-terrestrial networks toward 6G systems,” IEEE Access, vol. 12, pp. 55 945–55 963, 2024

    2024

  2. [2]

    Integrating terrestrial and non-terrestrial networks for sus- tainable 6G operations: a latency-aware multi-tier cell-switching approach,

    M. Ozturk, M. Salamatmoghadasi, and H. Yanikomeroglu, “Integrating terrestrial and non-terrestrial networks for sus- tainable 6G operations: a latency-aware multi-tier cell-switching approach,” IEEE Network, vol. 40, no. 2, pp. 156–164, 2026. ACCEPTED IN IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 17

    2026

  3. [3]

    NOMA-assisted routing algorithm design for UA V ad hoc relay networks,

    X. Yang, B. Han, G. Zhang, P. Zheng, J. Bai, and D. Qin, “NOMA-assisted routing algorithm design for UA V ad hoc relay networks,” IEEE Sens. J, vol. 23, no. 3, pp. 3296–3312, 2023

    2023

  4. [4]

    Advanced nonlinear optimization techniques in SWIPT: a review,

    Q. M. Khalaf, A. Sali, A. Ismail, M. Y. Ahmad, Y. S. Hussein, and J. A. Shah, “Advanced nonlinear optimization techniques in SWIPT: a review,” IEEE Access, vol. 12, pp. 192 743–192 766, 2024

    2024

  5. [5]

    A multi-layer non- terrestrial networks architecture for 6G and beyond under real- istic conditions and with practical limitations,

    F. Khennoufa, K. Abdellatif, H. Yanikomeroglu, M. Ozturk, T. Elganimi, F. Kara, and K. Rabie, “A multi-layer non- terrestrial networks architecture for 6G and beyond under real- istic conditions and with practical limitations,” IEEE Internet of Things Mag., vol. 8, no. 5, pp. 136–143, 2025

    2025

  6. [6]

    Joint rransmission and resource optimization in NOMA-assisted IoVT with mobile edge computing,

    L. Qin, H. Lu, Y. Chen, B. Chong, and F. Guo, “Joint rransmission and resource optimization in NOMA-assisted IoVT with mobile edge computing,” IEEE Trans. Veh. Tech., vol. 73, no. 7, pp. 9984–9999, 2024

    2024

  7. [7]

    Performance optimization in RSMA-assisted uplink xURLLC IIoT networks with statistical QoS provisioning,

    Y. Chen, H. Lu, C. Wu, L. Qin, and X. Guo, “Performance optimization in RSMA-assisted uplink xURLLC IIoT networks with statistical QoS provisioning,” IEEE Trans. Wirel. Com- mun., vol. 24, no. 12, pp. 10 100–10 115, 2025

    2025

  8. [8]

    Rate-splitting multiple access: Fundamentals, survey, and future research trends,

    Y. Mao, O. Dizdar, B. Clerckx, R. Schober, P. Popovski, and H. V. Poor, “Rate-splitting multiple access: Fundamentals, survey, and future research trends,” IEEE Commun. Surv. Tutor., vol. 24, no. 4, pp. 2073–2126, 2022

Show all 60 references
  1. [9]

    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 of Things J., vol. 12, no. 24, pp. 51 951–51 975, 2025

    2025

  2. [10]

    Optimal trajectory and resource allocation for RSMA-UA V assisted IoT communi- cations,

    J. Feng, X. Liu, Z. Liu, and T. S. Durrani, “Optimal trajectory and resource allocation for RSMA-UA V assisted IoT communi- cations,” IEEE Trans. Veh. Tech., vol. 73, no. 6, pp. 8693–8704, 2024

    2024

  3. [11]

    On sum-rate maximization in downlink UA V-aided RSMA systems,

    D.-T. Hua, Q. T. Do, N.-N. Dao, and S. Cho, “On sum-rate maximization in downlink UA V-aided RSMA systems,” ICT Express, vol. 10, no. 1, pp. 15–21, 2024

    2024

  4. [12]

    On the downlink performance of RSMA- based UA V communications,

    W. Jaafar, S. Naser, S. Muhaidat, P. C. Sofotasios, and H. Yanikomeroglu, “On the downlink performance of RSMA- based UA V communications,” IEEE Trans. Veh. Tech., vol. 69, no. 12, pp. 16 258–16 263, 2020

    2020

  5. [13]

    Traffic-aware energy-efficient resource allocation for RSMA based UA V communications,

    M. Xiao, H. Cui, D. Huang, Z. Zhao, X. Cao, and D. O. Wu, “Traffic-aware energy-efficient resource allocation for RSMA based UA V communications,” IEEE Trans. Netw. Sci. Eng., vol. 11, no. 3, pp. 2537–2548, 2024

    2024

  6. [14]

    RSMA-enabled covert communications against multiple spatially random wardens,

    X. Pei, J. Liu, X. Luo, X. Wang, Y. Chen, M. Wen, and T. A. Tsiftsis, “RSMA-enabled covert communications against multiple spatially random wardens,” IEEE Trans. Veh. Technol., pp. 1–6, 2026

    2026

  7. [15]

    Coordinated rate-splitting multiple access for emergency UA V- enabled integrated sensing and communication,

    X. Wei, R. Li, Y. Chen, L. Xu, L. Wang, and Z. Han, “Coordinated rate-splitting multiple access for emergency UA V- enabled integrated sensing and communication,” IEEE Trans. Cogn. Commun. Netw., vol. 12, pp. 5999–6015, 2026

    2026

  8. [16]

    Autonomous driving with RSMA-enabled finite blocklength transmissions: ergodic performance analysis and optimization,

    Y. Wang, Y. Chen, L. Wang, D. Cai, X. Li, and P. Fan, “Autonomous driving with RSMA-enabled finite blocklength transmissions: ergodic performance analysis and optimization,” IEEE Trans. Wirel. Commun., vol. 25, pp. 2954–2969, 2026

    2026

  9. [17]

    Resource allocation and outage probability optimization method for multi-hop UA V relay network for servicing heterogeneous users,

    W. Pan, N. Lv, B. Hou, and Z. Ren, “Resource allocation and outage probability optimization method for multi-hop UA V relay network for servicing heterogeneous users,” IEEE Trans. Net. Sci. Eng., vol. 11, no. 3, pp. 2769–2781, 2024

    2024

  10. [18]

    Coordinated RSMA for integrated sensing and communication in emergency UA V systems,

    B. Yao, R. Li, Y. Chen, and L. Wang, “Coordinated RSMA for integrated sensing and communication in emergency UA V systems,” arXiv preprint arXiv:2406.19205, 2024

    2024

  11. [19]

    General outage probability model for UA V-to-UA V links in multi-UA V networks,

    W. J. Lau, J. M.-Y. Lim, C. Y. Chong, N. S. Ho, and T. W. M. Ooi, “General outage probability model for UA V-to-UA V links in multi-UA V networks,” Comput. Net., vol. 229, p. 109752, 2023

    2023

  12. [20]

    Multi-AA V energy- efficient detection coverage under jamming environment: a hierarchical collaborative learning approach,

    C. Fang, Y. Feng, X. Li, and Y. Yang, “Multi-AA V energy- efficient detection coverage under jamming environment: a hierarchical collaborative learning approach,” IEEE Trans. Veh. Tech., vol. 74, no. 5, pp. 7351–7363, 2025

    2025

  13. [21]

    Hierarchical network slicing for UA V-assisted wireless networks with de- ployment optimization,

    F. Wei, G. Feng, S. Qin, Y. Peng, and Y. Liu, “Hierarchical network slicing for UA V-assisted wireless networks with de- ployment optimization,” IEEE J. Sel. Areas Commun., vol. 42, no. 12, pp. 3705–3718, 2024

    2024

  14. [22]

    Vision-based for- mation control for an outdoor UA V swarm with hierarchical architecture,

    L. Ma, D. Meng, X. Huang, and S. Zhao, “Vision-based for- mation control for an outdoor UA V swarm with hierarchical architecture,” IEEE Access, vol. 11, pp. 75 134–75 151, 2023

    2023

  15. [23]

    Joint ADS-B in B5G for hierarchical UA V networks: performance analysis and MEC based optimization,

    C. Dong, Y. Liao, Z. Jia, Q. Wu, and L. Zhang, “Joint ADS-B in B5G for hierarchical UA V networks: performance analysis and MEC based optimization,” IEEE Internet of Things J., vol. 12, no. 12, pp. 22 211–22 223, 2025

    2025

  16. [24]

    Distribu- tionally robust chance-constrained optimization for hierarchical UA V-based MEC,

    C. Cui, Z. Jia, C. Dong, Z. Ling, J. You, and Q. Wu, “Distribu- tionally robust chance-constrained optimization for hierarchical UA V-based MEC,” in IEEE Conf. Com. Commun. Workshops, 2023, pp. 1–6

    2023

  17. [25]

    A novel real-time unmanned aerial vehicles-based disaster management frame- work,

    J. Lorincz, A. Tahirović, and B. R. Stojkoska, “A novel real-time unmanned aerial vehicles-based disaster management frame- work,” in IEEE 29th Telecommun. Forum, 2021, pp. 1–4

    2021

  18. [26]

    A survey on multiuser SWIPT communications for 5G+,

    S. Özyurt, A. F. Coşkun, S. Büyükçorak, G. Karabulut Kurt, and O. Kucur, “A survey on multiuser SWIPT communications for 5G+,” IEEE Access, vol. 10, pp. 109 814–109 849, 2022

    2022

  19. [27]

    Performance analysis and optimization for IoT mobile edge computing networks with RF energy harvesting and UA V relaying,

    A.-N. Nguyen, D.-B. Ha, V. N. Vo, V.-T. Truong, D.-T. Do, and C. So-In, “Performance analysis and optimization for IoT mobile edge computing networks with RF energy harvesting and UA V relaying,” IEEE Access, vol. 10, pp. 21 526–21 540, 2022

    2022

  20. [28]

    Max- imizing energy charging for UA V-assisted MEC systems with SWIPT,

    X. Hu, P. Wen, H. Xiao, W. Wang, and K.-K. Wong, “Max- imizing energy charging for UA V-assisted MEC systems with SWIPT,” IEEE Trans. Veh. Tech., vol. 74, no. 5, pp. 8442– 8447, 2025

    2025

  21. [29]

    Outage performance of UA V-assisted relaying MIMO-NOMA networks with SWIPT in Rician fading channels,

    H. Çağiran, O. Kucur, and S. Büyükçorak, “Outage performance of UA V-assisted relaying MIMO-NOMA networks with SWIPT in Rician fading channels,” AEÜ Int. J. Electron. Commun., vol. 193, p. 155713, 2025

    2025

  22. [30]

    Wireless powered NOMA assisted full-duplex cooperative U2U communications system with fluctuating two-ray channel,

    R. Bajpai, A. S. Shaikh, and N. Gupta, “Wireless powered NOMA assisted full-duplex cooperative U2U communications system with fluctuating two-ray channel,” IEEE Access, vol. 13, pp. 54 069–54 079, 2025

    2025

  23. [31]

    Energy efficiency maximization in UA V-assisted intelligent autonomous transport system for 6G networks with energy harvesting,

    J. Huang, T. Yu, X. Zhu, F. Yang, X. Lai, O. Alfarraj, and K. Yu, “Energy efficiency maximization in UA V-assisted intelligent autonomous transport system for 6G networks with energy harvesting,” IEEE Trans. Intell. Transp. Syst., vol. 26, no. 10, pp. 17 212–17 222, 2025

    2025

  24. [32]

    Hardware impairments effects on over the air system assisted by unmanned aerial vehicle,

    H. Alsmadi, E. Saleh, M. Alsmadi, and S. Ikki, “Hardware impairments effects on over the air system assisted by unmanned aerial vehicle,” IEEE Commun. Lett., vol. 28, no. 7, pp. 1609– 1613, 2024

    2024

  25. [33]

    Performance analysis of UA V-assisted DF relaying network with hardware impairments and energy harvesting,

    J. Chen, N. Chen, S. Cheng, G. Fan, L. Rao, X. Song, W. Lv, and D. Yang, “Performance analysis of UA V-assisted DF relaying network with hardware impairments and energy harvesting,” Wirel. Net., vol. 30, no. 5, pp. 3061–3073, 2024

    2024

  26. [34]

    Secrecy analysis and optimization of UA V-assisted relaying networks with hardware impairments,

    S. Cheng, J. Chen, X. Hu, N. Chen, G. Fan, L. Rao, X. Song, and D. Yang, “Secrecy analysis and optimization of UA V-assisted relaying networks with hardware impairments,” Electron. Lett., vol. 61, no. 1, p. e70248, 2025

    2025

  27. [35]

    UA V- empowered IoT network With hardware impairments and shad- owing,

    G. K. Pandey, D. S. Gurjar, S. Yadav, and S. Solanki, “UA V- empowered IoT network With hardware impairments and shad- owing,” IEEE Sens. Lett., vol. 7, no. 7, pp. 1–4, 2023

    2023

  28. [36]

    Performance prediction in UA V- terrestrial networks with hardware noise,

    B. V. Minh, A.-T. Le, C.-B. Le, S. Q. Nguyen, V.-D. Phan, T. N. Nguyen, and M. Voznak, “Performance prediction in UA V- terrestrial networks with hardware noise,” IEEE Access, vol. 11, pp. 117 562–117 575, 2023

    2023

  29. [37]

    Outage performance of multi-UA V relaying-based imperfect hardware hybrid satellite- terrestrial networks,

    P. K. Sharma and D. Gupta, “Outage performance of multi-UA V relaying-based imperfect hardware hybrid satellite- terrestrial networks,” IEEE Syst. J., vol. 16, no. 2, pp. 2311– 2314, 2022

    2022

  30. [38]

    Outage probability analysis of MISO-NOMA downlink communications in UA V- assisted Agri-IoT with SWIPT and TAS enhancement,

    Y. He, F. Huang, D. Wang, and R. Zhang, “Outage probability analysis of MISO-NOMA downlink communications in UA V- assisted Agri-IoT with SWIPT and TAS enhancement,” IEEE Trans. Net. Sci. Eng., vol. 12, no. 3, pp. 2151–2164, 2025

    2025

  31. [39]

    UA V-assisted AmBC network: performance analysis in nakagami- m fading channel,

    B. Ning, M. Yi, Y. Xu, J. Li, and W. Hao, “UA V-assisted AmBC network: performance analysis in nakagami- m fading channel,” IEEE Trans. Veh. Tech, vol. 74, no. 5, pp. 6966–6978, 2025

    2025

  32. [40]

    3-D position optimiza- tion of solar-powered hovering UA V relay in optical wireless backhaul,

    H. Liu, M. S. Bashir, and M.-S. Alouini, “3-D position optimiza- tion of solar-powered hovering UA V relay in optical wireless backhaul,” IEEE Trans. Aerosp. Electron. Syst., vol. 61, no. 3, pp. 5853–5870, 2025

    2025

  33. [41]

    Outage probability for downlink multi-antenna RSMA system with imperfect SIC,

    S. Li, J. Zhu, T. Feng, and M. Derakhshani, “Outage probability for downlink multi-antenna RSMA system with imperfect SIC,” IEEE Wirel. Commun. Lett., vol. 15, pp. 385–389, 2025

    2025

  34. [42]

    Outage prob- ability and throughput analysis of UA V-assisted rate-splitting multiple access,

    S. K. Singh, K. Agrawal, K. Singh, and C.-P. Li, “Outage prob- ability and throughput analysis of UA V-assisted rate-splitting multiple access,” IEEE Wirel. Commun. Lett., vol. 10, no. 11, pp. 2528–2532, 2021. ACCEPTED IN IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 18

    2021

  35. [43]

    BER analysis of dual-hop relaying with energy harvesting in nakagami- m fading channel,

    M. Babaei, Ü. Aygölü, and E. Basar, “BER analysis of dual-hop relaying with energy harvesting in nakagami- m fading channel,” IEEE Trans. Wirel. Commun., vol. 17, no. 7, pp. 4352–4361, 2018

    2018

  36. [44]

    A unified performance anal- ysis of cooperative NOMA with practical constraints: hardware impairment, imperfect SIC and CSI,

    S. Beddiaf, A. Khelil, F. Khennoufa, F. Kara, H. Kaya, X. Li, K. Rabie, and H. Yanikomeroglu, “A unified performance anal- ysis of cooperative NOMA with practical constraints: hardware impairment, imperfect SIC and CSI,” IEEE Access, vol. 10, pp. 132 931–132 948, 2022

    2022

  37. [45]

    Hardware and interference limited cooperative cr-noma net- works under imperfect sic and csi,

    S. Arzykulov, G. Nauryzbayev, A. Celik, and A. M. Eltawil, “Hardware and interference limited cooperative cr-noma net- works under imperfect sic and csi,” IEEE Open J. Commun. Soc., vol. 2, pp. 1473–1485, 2021

    2021

  38. [46]

    Outage and throughput analysis of power-beacon assisted nonlinear energy harvesting NOMA multi-user relay system over Nakagami-m fading channels,

    T. M. Hoang, B. C. Nguyen, T. T. Trung et al., “Outage and throughput analysis of power-beacon assisted nonlinear energy harvesting NOMA multi-user relay system over Nakagami-m fading channels,” Heliyon, vol. 6, no. 11, 2020

    2020

  39. [47]

    Wireless powered cooperative communication network for dual-hop uplink NOMA with IQI and SIC imperfections,

    F. Khennoufa, A. Khelil, S. Beddiaf, F. Kara, K. Rabie, H. Kaya, A. Emir, S. Ikki, and H. Yanikomeroglu, “Wireless powered cooperative communication network for dual-hop uplink NOMA with IQI and SIC imperfections,” IEEE Access, vol. 11, pp. 76 506–76 523, 2023

    2023

  40. [48]

    MIMO transmission with residual transmit-RF impairments,

    C. Studer, M. Wenk, and A. Burg, “MIMO transmission with residual transmit-RF impairments,” in Internat. ITG Workshop Smart Antennas, 2010, pp. 189–196

    2010

  41. [49]

    Impact of hardware impairment on the uplink SIMO Cooperative NOMA with selection relay under imperfect CSI,

    S. Beddiaf, A. Khelil, F. Khennoufa, F. Kara, K. Rabie, X. Li, H. Kaya, A. Emir, and H. Yanikomeroglu, “Impact of hardware impairment on the uplink SIMO Cooperative NOMA with selection relay under imperfect CSI,” IEEE Access, vol. 11, pp. 106 706–106 721, 2023

    2023

  42. [50]

    A vision and framework for the high altitude platform station (HAPS) networks of the future,

    G. K. Kurt, M. G. Khoshkholgh, S. Alfattani, A. Ibrahim, T. S. Darwish, M. S. Alam, H. Yanikomeroglu, and A. Yongacoglu, “A vision and framework for the high altitude platform station (HAPS) networks of the future,” IEEE Commun. Surv. Tutor., vol. 23, no. 2, pp. 729–779, 2021

    2021

  43. [51]

    L. A. Wolsey and G. L. Nemhauser, Integer and Combinatorial Optimization. John Wiley & Sons, 1999

    1999

  44. [52]

    Improved Big-M refor- mulation for generalized disjunctive programs,

    F. Trespalacios and I. E. Grossmann, “Improved Big-M refor- mulation for generalized disjunctive programs,” Comput. Chem. Eng., vol. 76, pp. 98–103, 2015

    2015

  45. [53]

    Pardo, Statistical Analysis of Empirical Data

    S. Pardo, Statistical Analysis of Empirical Data. Springer, 2020

    2020

  46. [54]

    Nocedal and S

    J. Nocedal and S. J. Wright, Numerical optimization. Springer, 2006

    2006

  47. [55]

    Introduction to algorithms third edition,

    C. Thomas H, E. Charles, R. Ronald L, S. Clifford et al., “Introduction to algorithms third edition,” 2009

    2009

  48. [56]

    Optimal LAP altitude for maximum coverage,

    A. Al-Hourani, S. Kandeepan, and S. Lardner, “Optimal LAP altitude for maximum coverage,” IEEE Wirel. Commun. Lett., vol. 3, no. 6, pp. 569–572, 2014

    2014

  49. [57]

    Drone small cells in the clouds: Design, deployment and performance analysis,

    M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Drone small cells in the clouds: Design, deployment and performance analysis,” in IEEE Global Commun. Conf., 2015, pp. 1–6

    2015

  50. [58]

    Error performance analysis of UA V-mounted RIS for NOMA systems with practical constraints,

    F. Khennoufa, K. Abdellatif, F. Kara, H. Yanikomeroglu, K. Rabie, T. Y. Elganimi, and S. Beddiaf, “Error performance analysis of UA V-mounted RIS for NOMA systems with practical constraints,” IEEE Commun. Lett., vol. 28, no. 4, pp. 887–891, 2024

    2024

  51. [59]

    Performance analysis of dual-hop AF relaying with non-linear/linear energy harvest- ing,

    M. Babaei, L. Durak-Ata, and Ü. Aygölü, “Performance analysis of dual-hop AF relaying with non-linear/linear energy harvest- ing,” Sensors, vol. 22, no. 16, p. 5987, 2022

    2022

  52. [60]

    The generalization of the binomial theorem,

    J. Morales and A. Flores-Riveros, “The generalization of the binomial theorem,” J. Math. Phys., vol. 30, no. 2, pp. 393–397, 1989. Faicel Khennoufa is an Assistant Professor of Telecommunications at the École Nationale Supérieure des Technologies A vancées (ENSTA), and a membe...

    1989

Pith tools

Reviewed June 27, 2026 · model on record in the stance chip above.