Intelligent Reflecting Surface Aided Green Communication with Deployment Optimization

This paper investigates an intelligent reflecting surface (IRS) aided green multiple-user downlink communication system. In contrast to the existing works that deploy the IRS in a fixed location, the location of the IRS is taken as an optimization variable to minimize the total transmit power by jointly optimizing the location of the IRS, transmit beamformers at the base station (BS), and IRS phase shifts. We point out a critical conclusion that before and after IRS deployment, the channel state information (CSI) of all the communication terminals is different, so an offline-online hybrid-CSI optimization framework is proposed to solve the problem. In the offline stage, we optimize the IRS location with only the statistical CSI (S-CSI) so the ergodic quality of service (QoS) constraints have to be considered, and universal lower bounds associated only with the location variable are derived to decouple all variables. In the online stage, all the instantaneous-CSI (I-CSI) are available. To solve this non-convex problem, an alternating optimization framework is developed. We propose a Riemannian Manifold (RM) algorithm to optimize the IRS phase shifts. Simulation results validate that the proposed algorithm is convergent and effective, and show that the location deployment of IRS is crucial for green communication.