Improved Differential Evolution for Enhancing the Aggregated Channel Estimation of RIS-Aided Cell-Free Massive MIMO

TL;DR

This paper proposes an improved differential evolution algorithm to optimize RIS phase shifts, significantly enhancing channel estimation accuracy in RIS-aided cell-free massive MIMO systems with spatial correlation.

Contribution

It introduces a novel augmented differential evolution algorithm tailored for nonconvex optimization in RIS phase shift design, improving channel estimation in cell-free massive MIMO.

Findings

Enhanced channel estimation accuracy compared to benchmarks

Significant NMSE reduction achieved by the proposed algorithm

Effective avoidance of local minima in nonconvex optimization

Abstract

Cell-Free Massive multiple-input multiple-output (MIMO) systems are investigated with the support of a reconfigurable intelligent surface (RIS). The RIS phase shifts are designed for improved channel estimation in the presence of spatial correlation. Specifically, we formulate the channel estimate and estimation error expressions using linear minimum mean square error (LMMSE) estimation for the aggregated channels. An optimization problem is then formulated to minimize the average normalized mean square error (NMSE) subject to practical phase shift constraints. To circumvent the problem of inherent nonconvexity, we then conceive an enhanced version of the differential evolution algorithm that is capable of avoiding local minima by introducing an augmentation operator applied to some high-performing Diffential Evolution (DE) individuals. Numerical results indicate that our proposed algorithm can significantly improve the channel estimation quality of the state-of-the-art benchmarks.