RHS-UAV-Based ISAC System With RIS Enhancing

• Published in: IEEE communications letters Vol. 29; no. 7; pp. 1614 - 1618
• Main Authors: Zhang, Shunwai, Ding, Xu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; alternating optimization; Array signal processing; Autonomous aerial vehicles; Base stations; Beamforming; Convexity; Holography; Integrated sensing and communication; Linear programming; Metamaterials; Noise threshold; Optimization; Quadratic programming; reconfigurable holographic surface; reconfigurable intelligent surface; Reconfigurable intelligent surfaces; Signal to noise ratio; Telecommunications; unmanned aerial vehicle; Unmanned aerial vehicles
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2025.3569544

To address the challenge of spectrum congestion in the sixth generation (6G) networks, in this letter, we propose a novel reconfigurable holographic surface (RHS)-unmanned aerial vehicle (UAV) based integrated sensing and communication (ISAC) system with reconfigurable intelligent surface (RIS) enhancing. To maximize the achievable communication rate while satisfying the minimum sensing signal-to-noise ratio (SNR) threshold, we jointly optimize the digital beamforming of base station, the holographic beamforming of RHS and the passive beamforming of RIS. To deal with the complicated non-convex optimization problem, an alternating optimization (AO)-based algorithm is adopted to decouple it into three subproblems which are further solved individually. Particularly, penalty concave-convex procedure (PCCP) method is utilized to deal with the rank-one constraint of passive beamforming subproblem and successive convex approximation (SCA) is utilized to simplify the complicated quadratically constrained quadratic programming (QCQP) of holographic beamforming. Simulation results demonstrate the superiorities of the proposed system as compared with its counterparts, and also show that the AO-based algorithm outperforms the benchmark algorithms obviously.