We consider sum–rate maximization of a wireless-powered network with an uncrewed aerial vehicle-mounted hybrid access point for off-grid and energy-autonomous deployment. Non-orthogonal multiple access (NOMA) over orthogonal time–frequency space (OTFS) maps symbols to the delay–Doppler domain for doubly selective channels. Each OTFS frame includes uplink (UL) pilots, downlink (DL) wireless power transfer (WPT), and UL OTFS–NOMA data with pilot-based, grid-quantized delay–Doppler alignment and ideal successive interference cancellation. We jointly optimize the DL WPT covariance, UL beamforming, the DL/UL time split, and per-sensor UL energies via alternating optimization (AO) and a single successive convex approximation (SCA) step. Simulation results show that the proposed AO/SCA scheme achieves an average sum–rate gain of 85.4% over the max-energy beamforming with single-pass UL/time optimization benchmark. Despite its higher complexity, the proposed AO/SCA offers a good performance–complexity tradeoff.
The integration of low-Earth-orbit (LEO) satellites with unmanned aerial vehicles (UAVs) promises high-throughput and flexible wireless connectivity, yet it faces critical challenges in simultaneously guaranteeing data rates and long-term energy harvesting under mobility and imperfect channel state information (CSI). Additionally, the rate–energy trade-off imposed by simultaneous wireless information and power transfer (SWIPT) further complicates per-slot resource allocation. In this paper, we propose a Lyapunov-based scheduling framework that stabilizes UAV data and virtual energy queues while maximizing weighted throughput. The framework employs a custom inner solver combining successive convex approximation (SCA) and weighted minimum mean-square error (WMMSE) optimization to efficiently compute per-slot beamformers and power-splitting ratios. Our approach explicitly accounts for UAV mobility, Rician fading channels with Doppler, and circuit nonlinearities in energy harvesting, ensuring feasible and energy-aware SWIPT operation. A LEO satellite–UAV integrated communication system is considered, where multiple satellites provide wireless connectivity to energy-constrained UAVs operating in a dynamic three-dimensional environment. The satellites employ multi-antenna transmission, while the UAVs rely on energy harvesting mechanisms to sustain their operation. The communication links are characterized by dominant line-of-sight propagation conditions, and UAV trajectories are adaptively optimized to improve network performance and energy efficiency. Simulation results demonstrate that the proposed Lyapunov-based SCA-WMMSE framework significantly outperforms a fixed baseline approach, providing substantial improvements in signal quality, achievable data rates, and harvested energy. Moreover, the proposed method maintains stable energy management behavior and guarantees long-term energy sustainability for the UAVs.
E. Spyrou, V. Kappatos, C. Angelis et al.· Telecom· 0 citations
An unmanned aerial vehicle (UAV)-enabled ISAC system employing rate-splitting multiple access (RSMA) and a joint beamforming and trajectory optimization framework is investigated and results demonstrate that the proposed algorithm significantly improves the achievable system downlink rate.
Shunxuan Wang, Qi Zhu· Italian National Conference...· 0 citations
Sixth-generation (6G) wireless networks require massive connectivity, high spectral efficiency (SE), and energy efficiency (EE). Although conventional non-orthogonal multiple access (NOMA) improves spectrum utilization by allowing multiple users to share the same subchannel through power-domain multiplexing, applying NOMA to large user groups significantly increases successive interference cancellation (SIC) complexity and intra-group interference. To address this limitation, clustered NOMA (C-NOMA) groups users into small subclusters where SIC is performed over fewer users, thereby improving scalability while reducing decoding complexity. Combining C-NOMA with hybrid beamforming (HB) further enhances SE and lowers power consumption by serving each subcluster through a dedicated analog beam with fewer radio-frequency chains. In this paper, we develop a cooperative resource-allocation framework for C-NOMA-enabled multi-uncrewed aerial vehicle (UAV) millimeter-wave (mmWave) networks with two-dimensional (2D) HB. UAVs equipped with 2D uniform planar array antennas serve as aerial base stations to enhance line-of-sight (LoS) connectivity. We formulate an EE-maximization problem that jointly considers user subclustering, power allocation (PA), and subchannel assignment (SA) under transmit-power and SIC constraints. User subclustering is first performed through head selection and channel-disparity-based pairing to enable efficient C-NOMA transmission. Given the resulting structure, the 2D HB and joint PA/SA optimization problem is solved via a two-stage approach: a heuristic 2D HB construction followed by a multi-agent deep deterministic policy gradient (MADDPG) algorithm for cooperative PA and SA optimization under centralized training and decentralized execution (CTDE). Simulation results demonstrate that the proposed framework consistently improves EE over representative benchmarks across different network configurations.
Muhammet Hevesli, A. M. Seid, Mohamed M. Abdallah et al.· IEEE Open Journal of the Com...· 0 citations
Simulation results demonstrate that the proposed method significantly reduces the time-averaged CRB by over 10%, compared with the ISAC system without UAV assistance, and also achieves a higher sensing accuracy than both the fixed-UAV-trajectory and the maximum-ratio-transmission-based beamforming benchmarks.
This paper experimentally validates a near-far-resilient asynchronous uplink multiple access (MA) scheme for underwater optical wireless vehicle networks (UOWVNs) based on code division multiple access (CDMA). In UOWVNs, asynchronous packet arrivals and motion-induced delay fluctuations cause chip misalignment, degrade the spreading-code orthogonality, and generate dynamic multiuser interference (MUI) under received-power imbalance (near-far) conditions. Because a 1-Gchip/s chip rate is desirable for 4K video uplinks, continuous sub-nanosecond delay estimation and decorrelator regeneration under vehicle motion can impose real-time implementation complexity. To address this challenge, we propose a novel 1-Gchip/s asynchronous uplink MA scheme for UOWVNs that utilizes a length-16 Hadamard code pair with low aperiodic cross-correlation, packet-embedded pilots for per-user chip alignment, and an adaptive interference cancellation (AIC) receiver. The AIC updates despreading weights once per packet to suppress dynamic MUI without sub-nanosecond relative delay estimation. The proposed scheme was experimentally validated using two underwater optical wireless links, achieving an effective bit rate of 59.9 Mb/s per user. Under near-far conditions, the AIC with three-symbol stacking reached the forward error correction limit at signal-to-interference ratios (SIRs) of approximately -9.5 dB for the desired user 1 case and -8.1 dB for the desired user 2 case, and remained below the limit for relative delays of up to 3 ns at SIRs of -7 dB and -6 dB, respectively. Additionally, user scalability was evaluated by simulation, indicating feasibility for at least five active users, and operation-count analysis revealed linear receiver-complexity scaling.
E. Khansalee, Yasuhiro Okamura, Masanori Hanawa· IEEE Access· 0 citations
The pervasive integration of unmanned aerial vehicles (UAVs) into demanding industrial scenarios, including power line inspection and mine hoisting, poses critical challenges for next-generation wireless networks in ensuring robust connectivity and high spectral efficiency under rapidly time-varying channel conditions. Although superimposed pilot schemes offer a promising solution to improve spectral efficiency by sharing time-frequency resources, these methods inevitably introduce severe pilot-data mutual interference. This interference degrades channel estimation accuracy and symbol detection reliability, thereby threatening mission-critical UAV operations. To tackle this issue, a sensing information-assisted superimposed pilot channel estimation method is proposed in UAV orthogonal frequency division multiplexing systems. In the proposed method, high-precision kinematic parameters, including position and velocity, acquired from onboard UAV sensing receivers are exploited to derive deterministic, enhanced prior bounds in the delay and Doppler domains. Based on the sensed prior information, a two-stage delay-Doppler denoising scheme is designed to truncate data symbol interference via adaptive thresholding. Subsequently, a sensing-assisted iterative decision-directed mechanism is employed to refine the estimation accuracy. Simulation results demonstrate that the proposed method eliminates the severe error floors of conventional superimposed pilot-based channel estimation methods. Furthermore, it achieves rapid convergence and optimal symbol detection performance at a low pilot power ratio, effectively improving spectral efficiency in dynamic UAV communication scenarios.
