Energy-Efficient Transmission Strategy for UAV-RIS 2.0 Assisted Communications Using Rate Splitting Multiple Access

This study explores the optimization of transmission strategies focusing on energy efficiency within a network comprising ground-based beyond-diagonal reconfigurable intelligent surfaces (BD-RIS), a.k.a RIS 2.0, and multiple unmanned aerial vehicles (UAVs). The motivation behind this work stems from...

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Vydáno v:IEEE transactions on wireless communications s. 1
Hlavní autoři: Huroon, Aamer Mohamed, Huang, Yu-Chih, Wang, Li-Chun
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 2025
Témata:
Autonomous aerial vehicles
beyond diagonal reconfigurable intelligent surface
Couplings
Energy efficiency
Interference
NOMA
Optimization
Rate splitting multiple access
Reconfigurable intelligent surfaces
Resource management
Rotors
unmanned aerial vehicle
Vectors
ISSN:1536-1276, 1558-2248
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Shrnutí:This study explores the optimization of transmission strategies focusing on energy efficiency within a network comprising ground-based beyond-diagonal reconfigurable intelligent surfaces (BD-RIS), a.k.a RIS 2.0, and multiple unmanned aerial vehicles (UAVs). The motivation behind this work stems from the critical need to enhance energy efficiency in next-generation wireless networks, where the integration of UAVs and RIS technologies presents both opportunities and challenges. Specifically, while UAVs offer flexible deployment and improved coverage, their limited battery life and the complex interference environment in multi-user networks necessitate innovative solutions for sustainable operation. Each UAV is designed to serve its corresponding user group, with each group utilizing unique subcarriers to maintain orthogonality and employing a rate-splitting multiple access (RSMA) strategy within each group. The primary objectives of this work are to optimize: 1) the allocation of BD-RIS elements to groups, 2) the phase rotations of BD-RIS, 3) the common rate allocation in RSMA, 4) UAV trajectories, and 5) the design of precoders. To achieve these objectives, we formulate an optimization problem under the framework of mixed-integer nonlinear programming (MINLP), with a focus on maximizing energy efficiency. Our proposed solution combines generalized Benders decomposition (GBD), a manifold-based algorithm, and successive convex approximation (SCA). GBD decomposes the MINLP into primal and master sub-problems, which are iteratively solved. To efficiently address variable coupling in the primal problem, we adopt a block coordinate descent (BCD) method and employ the Riemannian conjugate gradient (RCG) technique for phase rotation. SCA addresses the remaining challenges in the primal problem, while a two-stage approach simplifies the optimization process. Simulations confirm the significant energy efficiency improvements achieved by the proposed method.
ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2025.3617169