Energy-Efficient LEO Satellite-to-Terrestrial Communication via Pinching-Antenna Relay System

Energy-Efficient AFDM-IM-Based 6G LEO Satellite Communication Systems for Sustainable Wireless Connectivity

Robust Secure Precoding for Wireless Information and Power Transfer in RSMA-Based LEO Satellite Communications

Satellite-terrestrial integrated networks with simultaneous wireless information and power transfer (SWIPT) provide wide-area connectivity and sustainable service support, but they also face serious security challenges due to the broadcast nature of satellite links and the possibility that an energy receiver may act as potential eavesdropper. To address this issue, this paper proposes a secure precoding design for a high-altitude platform (HAP)-assisted rate-splitting multiple access (RSMA) architecture under a quasi-static transmission model. Specifically, a cooperative direct and relay transmission (CDRT) framework is developed, in which the HAP assists the satellite transmission to improve the physical layer security for multi-user SWIPT services. By assuming the energy receiver near the target user as potential eavesdropper, we formulate a sum secrecy rate maximization problem subject to energy harvesting and transmit power constraints. To transform the original nonconvex optimization problem into a tractable convex problem, we employ techniques such as first-order Taylor expansion approximation, rank-one constraint relaxation, successive convex approximation, and semidefinite relaxation. Numerical results demonstrate that the proposed CDRT-RSMA scheme significantly outperforms conventional non-orthogonal and time-division multiple access schemes in terms of security performance.

Integrated Sensing, Communication, and Power Transfer for Fluid-Antenna LEO Satellite Systems

Emerging technologies including wireless power transfer (WPT), integrated sensing and communication (ISAC), and fluid antennas (FAs), have significantly advanced the capabilities and performance of modern satellite communication systems. This paper investigates an FA-assisted integrated sensing, communication, and power transfer (ISCPT) framework for low Earth orbit (LEO) satellite networks, which operates in two phases: 1) an energy-transfer and target-sensing phase (Phase I), and 2) an information-transmission phase (Phase II). Specifically, in Phase I, a space solar power satellite (SSPS) transmits a dual-functional waveform to simultaneously charge multiple LEO satellites and illuminate a sensing target, while in Phase II, these LEO satellites coordinately serve multiple ground user equipments (UEs) leveraging the harvested energy. We formulate a sum-rate maximization problem subject to the SSPS’s transmit power constraint, LEO satellites’ energy harvesting and sensing requirements, UEs’ information rate demands, and the FAs’ movable regions. To tackle the highly-coupled and non-convex optimization problem, a three-stage alternating optimization (AO) algorithm is proposed, which decomposes it into resource allocation, SSPS-side FA placement, and LEO-side FA placement subproblems. In particular, the resource allocation subproblem is reformulated by adopting the Cauchy–Schwarz inequality and semidefinite relaxation (SDR), and is efficiently tackled via the successive convex approximation method. The two FA placement subproblems are addressed leveraging trust-region-based optimization. Simulation results validate the superior performance gains of the proposed algorithm over seven benchmarks and demonstrate that FAs can enhance multi-functional wireless services by adjusting inter-channel diversity according to service types. Notably, a non-trivial trade-off arises among FAs-enabled multi-functional services requiring distinct channel characteristics, as FAs cannot simultaneously provide optimal channel conditions for all services.

Multi-RIS-Assisted Satellite Compact Ultra-Massive Antenna Array for Massive Uplink Transmission

High-capacity satellite network is the cornerstone of future space-air-ground integrated networks. However, the satellite uplink transmissions still face critical challenges, including severe path loss, complex multi-user interference, and payload constraints. Recently, Reconfigurable Intelligent Surfaces (RIS) and Fluid Antenna Systems (FAS) have shown promise for satellite communications through their dynamic signal reconfiguration. This paper proposes a multi-RIS-assisted satellite Compact Ultra-Massive Antenna Array (CUMA) architecture for multi-user satellite uplink transmission. Specifically, we deploy multiple RISs on the terrestrial side to separate interfering Line-of-Sight (LoS) channels via optimized phase shifts, and adopt a CUMA receiver on the satellite to further mitigate interference through FAS port selection. To solve a sum-rate maximization problem, we alternately optimize FAS port selection using a Forward-Backward Greedy Selection (FBGS) algorithm and RIS phase shifts based on Fractional Programming (FP). To the best of our knowledge, this is the first work to jointly optimize multi-RIS and CUMA in a satellite uplink context, where strong LoS and extreme path loss fundamentally distinguish the design from terrestrial counterparts. Simulation results confirm the effectiveness of the proposed architecture across frequency bands. At 6 GHz, our scheme achieves 181% and 32% rate gains over fixed antennas and traditional CUMA schemes, respectively, while the gains also reach 138% and 27% at 26 GHz, illustrating superiority in both interference-limited and noise-limited regimes.

Demand-Aware Cooperative Transmission Design for Energy-Efficient LEO Satellite Networks

Numerical results indicate that the proposed framework outperforms benchmark schemes while accounting for traffic demands and EE, resulting in a mixed-integer nonlinear program (MINLP) for which finding a globally optimal solution is generally intractable.

Energy-Aware Scheduling and Beamforming for Simultaneous Wireless Information and Power Transfer in Low-Earth-Orbit Satellite and UAV Networks Using Lyapunov Optimization, Successive Convex Approximation, and WMMSE

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.