Energy Efficiency Maximization for Intelligent Surfaces-Aided Massive MIMO With Zero Forcing

Abstract

In this work, we address the energy efficiency (EE) maximization problem in a\ndownlink communication system utilizing reconfigurable intelligent surface\n(RIS) in a multi-user massive multiple-input multiple-output (mMIMO) setup with\nzero-forcing (ZF) precoding. The channel between the base station (BS) and RIS\noperates under a Rician fading with Rician factor K1. Since systematically\noptimizing the RIS phase shifts in each channel coherence time interval is\nchallenging and burdensome, we employ the statistical channel state information\n(CSI)-based optimization strategy to alleviate this overhead. By treating the\nRIS phase shifts matrix as a constant over multiple channel coherence time\nintervals, we can reduce the computational complexity while maintaining an\ninteresting performance. Based on an ergodic rate (ER) lower bound closed-form,\nthe EE optimization problem is formulated. Such a problem is non-convex and\nchallenging to tackle due to the coupled variables. To circumvent such an\nobstacle, we explore the sequential optimization approach where the power\nallocation vector p, the number of antennas M, and the RIS phase shifts v are\nseparated and sequentially solved iteratively until convergence. With the help\nof the Lagrangian dual method, fractional programming (FP) techniques, and\nLemma 1, insightful compact closed-form expressions for each of the three\noptimization variables are derived. Simulation results validate the\neffectiveness of the proposed method across different generalized channel\nscenarios, including non-line-of-sight (NLoS) and partially line-of-sight (LoS)\nconditions. This underscores its potential to significantly reduce power\nconsumption, decrease the number of active antennas at the base station, and\neffectively incorporate RIS structure in mMIMO communication setup with just\nstatistical CSI knowledge.\n