Sum-Rate Maximization in IRS-Assisted Wireless-Powered Multiuser MIMO Networks With Practical Phase Shift

Abstract

The newly emerging intelligent reflecting surface (IRS) with large-scale passive reflecting elements has great potentials to enhance the performance of wireless-powered Internet of Things (IoT) networks, by manipulating the wireless channel. However, most of the existing works considered the ideal reflection of IRS elements with independent amplitude and phase shift. In this article, an IRS-assisted wireless-powered multiuser multi-input-multi-output network is considered, taking into account the practical coupling effect between the reflecting amplitude and the phase shift. Then, an uplink sum-rate maximization problem is investigated by jointly designing the active beamforming of multiple antennas, the passive beamforming of the IRS, and the time allocation ratio. Due to the tightly coupled optimization variables, the formulated problem is nonconvex. To effectively solve this problem, we decompose it into three subproblems, i.e., the active beamforming, the downlink passive beamforming, and the uplink passive beamforming. For the active beamforming design, access point’s optimal downlink energy beamforming matrix is proved to be rank-one, and IoT users’ optimal uplink information covariance matrices are derived in semi-closed forms. For the downlink passive beamforming design, a low-complexity algorithm based on the successive convex approximation and the penalty function method is proposed. For the uplink passive beamforming design, the multiuser problem is equivalently transformed into a virtual single-user problem, which is solved via an iterative algorithm. Numerical results show that, in comparison with algorithms without IRS, our proposed algorithm can significantly improve the uplink sum rate up to 50% when the number of passive elements is 100.