Intelligent Reflecting Surfaces: Sum-Rate Optimization Based on Statistical CSI

Abstract

In this paper, we consider a multi-user multiple-input multiple-output (MIMO) system aided by multiple intelligent reflecting surfaces (IRSs) that are deployed to increase the coverage and, possibly, the rank of the channel. We propose an optimization algorithm to configure the IRSs, which is aimed at maximizing the network sum-rate by exploiting only the statistical characterization of the environment, such as the distribution of the locations of the users and the distribution of the multipath channels. As a consequence, the proposed approach does not require the estimation of the instantaneous channel state information (CSI) for system optimization, thus significantly relaxing (or even avoiding) the need of frequently reconfiguring the IRSs, which constitutes one of the most critical issues in IRS-assisted systems. Numerical results confirm the validity of the proposed approach. It is shown, in particular, that IRS-assisted wireless systems that are optimized based on statistical CSI still provide large performance gains as compared to the baseline scenarios in which no IRSs are deployed.

submitted for journal publication

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• Two-Timescale Design for Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with Imperfect CSI
• Fairness-Oriented Multiple RISs-Aided MmWave Transmission: Stochastic Optimization Methods
• Multi-RIS Discrete-Phase Encoding for Interpath-Interference-Free Channel Estimation