Energy-Efficient Resource Allocation and Antenna Selection for IRS-Assisted Multicell Downlink Networks
Atefeh Rezaei, Ata Khalili, Jalal Jalali, Hossein Shafiei, Qingqing Wu
Abstract
This letter considers a network-assisted intelligent reflecting surface (IRS) technology. We aim to adopt an energy-efficient strategy via an antenna selection (AS) framework that determines which base station (BS) antennas transmit the data to the user equipment. In particular, we select the best set of antennas to increase energy efficiency (EE) while reducing power consumption. Also, the network takes advantage of the IRS system to increase the coverage and overall throughput of the network. We first propose an efficient algorithm for the considered scenario based on the successive convex approximation (SCA). Then we employ the Dinkelbach method that jointly selects the best set of antennas and optimizes their beamforming. Second, by introducing the slack variable and SCA method, we propose a tight approximation to solve the passive beamforming at the IRS. Simulation results unveil the performance of the proposed method and its influence on the power consumption at each antenna’s RF chain.