A Joint Optimization Framework for IRS-Assisted Energy Self-Sustainable IoT Networks

Abstract

Energy self-sustainability is critically important for future Internet of Things (IoT) networks to support an ever-growing massive number of wireless devices with low maintenance cost and high spectrum/energy efficiency. Power-splitting (PS)-based simultaneous wireless information and power transfer (PS-SWIPT) is a promising solution to realize it. However, the performance of PS-SWIPT is severely influenced by the channel attenuation caused by the detrimental radio propagation environment. Intelligent reflecting surface (IRS) is an emerging technology that can reconfigure the incident signal with considerable array gain so as to improve the PS-SWIPT performance. Thus, in this article, we investigate the weighted sumrate (WSR) maximization problem of the IRS-assisted multi-input–multioutput (MIMO) PS-SWIPT IoT network with multiple low-power IoT PS-based devices (PSDs). The formulated problem is nonconvex and arduous to tackle due to the presence of the intricately coupled variables and the mutually exclusive constraints. To the best of our knowledge, the problem is not addressed yet and cannot be solved by employing the existing methods directly. To cope with the problem, we develop a joint optimization framework that decomposes the original problem into several subproblems that can be solved alternately. Simulation results confirm the effectiveness of IRS to improve the WSR of the PS-SWIPT energy self-sustainable IoT networks and demonstrate that the proposed algorithm outperforms benchmark methods considerably.