Energy-Efficient Resource Allocation for Multi-IRS-Aided Green Networks

Intelligent reflecting surface (IRS) is intended to be a game-changing innovation in wireless communications by enabling a transition from channel adaptation to a smart wireless environment. In this paper, we propose a multi-intelligent reflecting surface (IRS) assisted millimeter wave (mm-wave) system in which IRS elements are switched on and off, independently. We formulate the resource allocation problem as an optimization to maximize energy efficiency under individual quality of service (QoS) constraints. We propose a new algorithm where the access point (AP) adjust the transmit beamforming and IRSs set the phaseshifts, and the on/off status of the IRSs until convergence is reached. In the first stage, we modify and apply successive convex approximation (SCA) and fractional programming (FP) approaches to achieve a solution for the optimization subproblems of the phase-shift coefficients and element on/off status of IRSs. Then, for the beamforming subproblem, we propose a modified nested FP approach that determines an optimal solution for the beamforming vectors of the access point. Our performance analysis of a practical scenario with a specified number of users and IRS elements shows that the proposed approach improves energy efficiency by up to 16.60% compared to the case where the on/off status of IRS elements and phaseshifts are selected randomly.