Impact of Channel Aging and Electromagnetic Interference on RIS-Assisted Cell-Free Massive MIMO Systems

TL;DR

This paper investigates how channel aging and electromagnetic interference impact RIS-assisted cell-free massive MIMO systems and proposes novel estimation and optimization methods to mitigate these effects, significantly improving spectral efficiency.

Contribution

It introduces a two-phase channel estimation scheme and a gradient-based RIS phase optimization to counteract channel aging and EMI effects in cell-free massive MIMO systems.

Findings

Nearly 10% spectral efficiency improvement with the proposed estimation scheme.

Additional 10-15% spectral efficiency gain using the gradient ascent optimization.

Enhanced system performance in environments with high mobility and electromagnetic interference.

Abstract

Cell-free massive multiple-input multiple-output (MIMO) and reconfigurable intelligent surfaces (RISs) are two potential sixth-generation (6G) technologies. However, channel aging due to user mobility and electromagnetic interference (EMI) impinging on RISs can negatively affect performance. Existing research on RIS-assisted cell-free massive MIMO systems often overlooks these issues. This work focuses on the impact and mitigation of channel aging and EMI on RIS-assisted cell-free massive MIMO systems over spatially correlated channels. To mitigate the degradation caused by these issues, we introduce a novel two-phase channel estimation scheme with large-scale fading coefficient-aided pilot assignment to enhance channel estimation accuracy compared to conventional minimum mean square error estimators. We then develop closed-form expressions for the downlink spectral efficiency (SE) performance and using these, optimize the sum downlink SE with respect to the RIS coefficient matrices. This optimization is accomplished by the projected gradient ascent (GA) algorithm. The results show that our proposed two-phase channel estimation scheme can achieve a nearly 10%-likely SE improvement compared to conventional channel estimation in environments affected by channel aging. A further 10%~15%-likely SE improvement is achieved using the proposed GA algorithm compared to random RIS phases, especially when the number of RISs increases.