Ergodic Rate Analysis of Reconfigurable Intelligent Surface-Aided Massive MIMO Systems with ZF Detectors

TL;DR

This paper analyzes the ergodic achievable rate of RIS-aided massive MIMO systems with ZF detectors, deriving closed-form expressions and demonstrating significant performance gains over RIS-free and MRC-based systems.

Contribution

It introduces a two-timescale design for RIS-assisted massive MIMO, deriving ergodic rate expressions and optimizing RIS passive beamforming using long-term statistical CSI.

Findings

Ergodic rate scales with ig(\log_2(MN)ig)

RIS-aided systems outperform RIS-free and MRC-based systems

Closed-form ergodic rate expression derived

Abstract

This letter investigates the reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems with a two-timescale design. First, the zero-forcing (ZF) detector is applied at the base station (BS) based on instantaneous aggregated CSI, which is the superposition of the direct channel and the cascaded user-RIS-BS channel. Then, by leveraging the channel statistical property, we derive the closed-form ergodic achievable rate expression. Using a gradient ascent method, we design the RIS passive beamforming only relying on the long-term statistical CSI. We prove that the ergodic rate can reap the gains on the order of $\mathcal{O}\left(\log_{2}\left(MN\right)\right)$, where $M$ and $N$ denote the number of BS antennas and RIS elements, respectively. We also prove the striking superiority of the considered RIS-aided system with ZF detectors over the RIS-free systems and RIS-aided systems with maximum-ratio combining (MRC).