Energy-Efficient UAV-RIS-Assisted SWIPT in Integrated Ground–Aerial–Space Networks

Résumé fourni par la source

Reconfigurable intelligent surface (RIS) has emerged as a promising technology to enhance both achievable rate and energy efficiency in next-generation wireless networks. This article investigates a novel energy-efficient UAV-RIS-assisted architecture for simultaneous wireless information and power transfer (SWIPT) in integrated ground–aerial–space networks, where a satellite cooperates with multiple UAV-RISs to provide downlink wireless energy transfer (WET) and support uplink wireless information transmission (WIT) for energy-constrained IoT devices in remote environments. We formulate an alternating iterative joint optimization problem (AIJOP) that aims to maximize the system sum achievable rate while ensuring causality of device energy via jointly optimizing UAV-RIS trajectories, RIS phase shift matrices, and satellite power allocation and time allocation between WET and WIT. The problem is highly non-convex due to the strong coupling among variables. To address this challenge, we propose a trajectory–phase–power–time alternating optimization algorithm (TPPTAOA), which decomposes the original problem into four tractable subproblems and solves them iteratively. Specifically, the UAV-RIS trajectories is first optimized via using a device scheduling and TSP-based path planning approach to solve the first subproblem, followed by the proposition of a two-stage heuristic phase optimization algorithm under fixed parameters to solve the second subproblem. Subsequently, the satellite power allocation is solved using a Lagrangian dual method, while the time allocation is optimized through a two-stage strategy combining grid-based coarse search with gradient-based refinement. Simulation results under various system settings verify the fast convergence, robustness, and superior performance of the proposed TPPTAOA, showing significant improvements in both energy efficiency and uplink achievable rate compared with benchmark schemes.

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