A Multi-cell MMSE Precoder for Massive MIMO Systems and New Large System Analysis

TL;DR

This paper introduces a multi-cell MMSE precoder for massive MIMO systems that leverages all estimated channel directions to suppress interference, providing significant spectral efficiency gains and stable performance compared to existing precoders.

Contribution

The paper proposes a novel multi-cell MMSE precoder that utilizes all local channel estimates for interference suppression, with a large-system analysis and practical power control considerations.

Findings

Achieves significant spectral efficiency gains over classical precoders.

Provides a tight large-scale approximation of downlink SINR.

Ensures stable performance even with large numbers of users.

Abstract

In this paper, a new multi-cell MMSE precoder is proposed for massive MIMO systems. We consider a multi-cell network where each cell has $K$ users and $B$ orthogonal pilot sequences are available, with $B = \beta K$ and $\beta \ge 1$ being the pilot reuse factor over the network. In comparison with conventional single-cell precoding which only uses the $K$ intra-cell channel estimates, the proposed multi-cell MMSE precoder utilizes all $B$ channel directions that can be estimated locally at a base station, so that the transmission is designed spatially to suppress both parts of the inter-cell and intra-cell interference. To evaluate the performance, a large-scale approximation of the downlink SINR for the proposed multi-cell MMSE precoder is derived and the approximation is tight in the large-system limit. Power control for the pilot and payload, imperfect channel estimation and arbitrary pilot allocation are accounted for in our precoder. Numerical results show that the proposed multi-cell MMSE precoder achieves a significant sum spectral efficiency gain over the classical single-cell MMSE precoder and the gain increases as $K$ or $\beta$ grows. Compared with the recent M-ZF precoder, whose performance degrades drastically for a large $K$, our M-MMSE can always guarantee a high and stable performance. Moreover, the large-scale approximation is easy to compute and shown to be accurate even for small system dimensions.