Multi-IRS Assisted Wireless-Powered Mobile Edge Computing for Internet of Things

This paper proposes a multiple intelligent reflecting surfaces (IRSs) assisted wireless-powered mobile edge computing (MEC) system, where the IRSs are deployed to assist both the downlink wireless power transfer (WPT) from the multi-antenna hybrid access point (HAP) to the wireless devices (WDs) and the uplink computation offloading from the WDs to the MEC server. To further improve the system performance, the energy beamforming and multiple-user detection (MUD) technologies are exploited. We consider both partial and binary offloading schemes and formulate the sum computation rate (SCR) maximization problems for them, respectively. To tackle the non-convexity of each problem, we propose an efficient alternating optimization (AO) method. Specifically, the Lagrange duality method is used to optimize the energy beamforming vector and the MUD matrix at the HAP, and the CPU frequencies and transmit power of the WDs. Then, we optimize the discrete phase shifts via the successive convex appropriation (SCA) method, the rank-one equivalents, and rounding method. Finally, the optimal time scheduling can be obtained via the one-dimensional search method. In addition, we propose a greedy algorithm with low complexity to optimize the computing modes for the binary offloading scheme. Numerical results show that our proposed schemes outperform the benchmarks.