Study of Clustered Robust Linear Precoding for Cell-Free MU-MIMO Networks

TL;DR

This paper introduces novel robust precoding techniques for cell-free MU-MIMO networks that effectively mitigate residual interference caused by imperfect channel state information, improving system performance.

Contribution

The paper proposes new robust precoding methods that incorporate user and access point clustering to reduce computational complexity and signaling load.

Findings

Robust MMSE precoders outperform conventional MMSE in various CSIT accuracy scenarios.

Clustering reduces computational cost and signaling load.

Proposed techniques effectively mitigate residual interference.

Abstract

Precoding techniques are key to dealing with multiuser interference in the downlink of cell-free (CF) multiple-input multiple-output systems. However, these techniques rely on accurate estimates of the channel state information at the transmitter (CSIT), which is not possible to obtain in practical systems. As a result, precoders cannot handle interference as expected and the residual interference substantially degrades the performance of the system. To address this problem, CF systems require precoders that are robust to CSIT imperfections. In this paper, we propose novel robust precoding techniques to mitigate the effects of residual multiuser interference. To this end, we include a loading term that minimizes the effects of the imperfect CSIT in the optimization objective. We further derive robust precoders that employ clusters of users and access points to reduce the computational cost and the signaling load. Numerical experiments show that the proposed robust minimum mean-square error (MMSE) precoding techniques outperform the conventional MMSE precoder for various accuracy levels of CSIT estimates.