Robust Beamforming for IRS-aided SWIPT in Cognitive Satellite and Terrestrial Networks

• Published in: IEEE communications letters Vol. 27; no. 9; p. 1
• Main Authors: Wang, Zining, Lin, Min, Huang, Shupei, Zhu, Wei-Ping, Han, Lve, De Cola, Tomaso
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Beamforming; Cognitive satellite and terrestrial networks; Earth stations; Energy harvesting; Energy requirements; intelligent reflecting surface; Multicasting; Optimization; Power transfer; robust beamforming; Robustness; Satellite antennas; Satellite broadcasting; Satellite networks; Satellites; Signal to noise ratio; Simultaneous wireless information and power transfer; Wireless communication
• ISSN: 1089-7798; 1558-2558
• DOI: 10.1109/LCOMM.2023.3288483

This paper proposes a robust beamforming (BF) scheme for intelligent reflecting surface (IRS)-aided simultaneous wireless information and power transfer (SWIPT) in a cognitive satellite and terrestrial network (CSTN). The satellite network serves multiple earth stations through the multicast transmission, while the terrestrial network operating at the same spectrum implements the SWIPT through IRS-aided multicast technology. Assuming that the imperfect channel state information (CSI) is available, we formulate an optimization problem to maximize the minimum achievable rate of the information receivers (IRs), subject to the transmit power budget, achievable rate and harvesting energy requirements. To address this nonconvex problem, we propose a tight bound robust BF algorithm based on Lagrange duality and alternating optimization (AO) to jointly optimize the active and passive beamformers for the base station, satellite and IRS, respectively. Simulation results confirm the robustness and superiority of our proposed BF scheme over other related works.