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

TL;DR

The paper tackles downlink interference in cell-free MIMO under imperfect CSIT. It develops robust MMSE precoding with a loading term to mitigate residual interference and derives a structured solution $\mathbf{P}^{(\text{r})}=f_{\tau}\bar{\mathbf{P}}$, where $\bar{\mathbf{P}}=(\hat{\mathbf{G}}^{*}\hat{\mathbf{G}}^{\mathrm{T}}+(1+f^2)\mathbb{E}[\tilde{\mathbf{G}}^{*}\tilde{\mathbf{G}}^{\mathrm{T}}]+\lambda f_{\tau}^{2}\mathbf{I})^{-1}\hat{\mathbf{G}}^{*}$. To enable scalability, it introduces AP/user clustering and AP selection, yielding sparse and cluster-based variants (MMSE-RB-SP, MMSE-RB-RD, MMSE-RB-RD) with reduced signaling and complexity. Ergodic sum-rate analysis and extensive simulations demonstrate that the proposed robust schemes outperform conventional MMSE precoding across CSIT accuracy levels, with a clear tradeoff between performance and complexity. The work provides practical, scalable precoding solutions for CF-MIMO deployments facing CSIT imperfections.

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.

Figures (2)

• Figure 1: Sum-rate performance of the proposed robust precoders. Here, $M=128$, $K=16$.
• Figure 2: Sum-rate performance vs channel quality of the proposed robust precoders. Here, $M=128$, $K=16$.

Theorems & Definitions (3)

• Proposition 1
• Proposition 2
• Proposition 3