Statistical CSI-based Design for Reconfigurable Intelligent Surface-aided Massive MIMO Systems with Direct Links

TL;DR

This paper proposes a statistical CSI-based design for RIS-assisted massive MIMO systems with direct links, deriving a closed-form data rate expression and optimizing phase shifts using genetic algorithms to enhance system performance.

Contribution

It introduces a novel statistical CSI-based phase shift design and demonstrates significant performance gains in RIS-assisted massive MIMO systems with low complexity.

Findings

RIS improves system performance significantly.

Genetic algorithm effectively maximizes sum data rate.

Low-overhead statistical CSI scheme is practical.

Abstract

This paper investigates the performance of reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with direct links, and the phase shifts of the RIS are designed based on the statistical channel state information (CSI). We first derive the closed-form expression of the uplink ergodic data rate. Then, based on the derived expression, we use the genetic algorithm (GA) to solve the sum data rate maximization problem. With low-complexity maximal-ratio combination (MRC) and low-overhead statistical CSI-based scheme, we validate that the RIS can still bring significant performance gains to traditional massive MIMO systems.