Optimization for IRS-Assisted MIMO-OFDM SWIPT System With Nonlinear EH Model

• Published in: IEEE internet of things journal Vol. 9; no. 24; pp. 25253 - 25268
• Main Authors: Peng, Xingxiang, Wu, Peiran, Tan, Hongzhou, Xia, Minghua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Complexity; Energy harvesting; Heuristic methods; Intelligent reflecting surface (IRS); Internet of Things; MIMO communication; multiple-input and multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM); nonlinear energy harvesting (EH); OFDM; Optimization; Orthogonal Frequency Division Multiplexing; power splitting (PS); Power transfer; Precoding; Simultaneous wireless information and power transfer (SWIPT); Wireless communication
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2022.3195927

Simultaneous wireless information and power transfer (SWIPT) has emerged as an appealing solution to prolonging the lifetime of low-power Internet of Things (IoT) networks. Meanwhile, an intelligent reflecting surface (IRS) can reconstruct a favorable wireless propagation environment for IoT terminals to achieve high spectrum and energy efficiencies. To take full advantage of these two technologies, this article studies the optimization of an IRS-assisted multiple-input and multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) SWIPT system with nonlinear energy harvesting (EH) model. In particular, we aim to maximize the achievable data rate by jointly designing the transmit precoding matrices, the IRS matrix, as well as the power splitting (PS) ratio subject to the transmit power and harvested power constraints. Since the formulated problem is highly nonconvex, we develop an alternating optimization (AO)-based algorithm to find a high-quality suboptimal solution. Moreover, we further design a heuristic algorithm based on a two-stage optimization strategy to reduce the computational complexity. Simulation results verify that the proposed AO-based algorithm can significantly improve the achievable data rate compared to conventional benchmarks, and the proposed heuristic low-complexity algorithm can achieve comparable performance to the AO-based algorithm.