Throughput Maximization Design for RIS-Assisted WPCN-NOMA-Based ISAC Systems

• Published in: IEEE transactions on communications Vol. 73; no. 8; pp. 6943 - 6957
• Main Authors: He, Silei, Tang, Kun, Zheng, Beixiong, Xiu, Xin, Feng, Wenjie, Che, Wenquan, Xue, Quan
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.2025
• Subjects: Array signal processing; Integrated sensing and communication; Integrated sensing and communication (ISAC); joint optimization; NOMA; non-orthogonal multiple access (NOMA); Optimization; Performance evaluation; Radar; reconfigurable intelligent surface (RIS); Reconfigurable intelligent surfaces; Sensors; Throughput; Wireless communication; wireless-powered communication network (WPCN)
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2025.3543242

In this paper, a reconfigurable intelligent surface (RIS) is utilized to assist wireless-powered communication network (WPCN) for achieving integrated sensing and communication (ISAC). In this system, the ISAC base station (BS) first performs radar sensing for multiple targets and wireless power transfer (WPT) for multiple energy-constrained devices with assistance of RIS during the downlink (DL), then all devices transmit their data to the ISAC BS in the uplink (UL) by employing non-orthogonal multiple access (NOMA) with the use of harvested energy. To maximize the sum achievable throughput subject to the quality of service (QoS) constraints of communication devices and sensing quality constraint of targets, we jointly optimize the energy beamforming matrix, radar beamforming matrix, phase shift matrix of RIS, and transmission timeslot allocation in the DL and UL durations. To tackle this non-convexity problem, a two-stage solution is proposed. In the first stage, the energy and radar beamforming matrices, and phase shift matrix of RIS in the DL can be derived according to the penalty-based successive convex approximation (SCA) algorithm and Riemannian conjugate gradient (RCG) algorithm, respectively. In the second stage, the transmission timeslot allocation and phase shift matrix of RIS in the UL are obtained via the polar point method and penalty-based RCG algorithm, respectively. The final optimal results of each stage are yielded based on alternating optimization (AO) algorithm. Numerical results verify the significant improvement of deploying RIS for WPT and data transmission, and the superiority of the proposed optimization solution in improving the sensing performance and achievable throughput compared to other schemes.