Data-Aided Regularization of Direct-Estimate Combiner in Distributed MIMO Systems

TL;DR

This paper introduces a data-driven regularization method for the direct-estimate combiner in distributed MIMO uplink systems, improving symbol error rate performance when pilot symbols are scarce.

Contribution

It proposes an iterative regularization technique based on a shrinkage coefficient optimized via the mean squared error of hard decisions, enhancing combiner robustness.

Findings

Significant SER improvement with limited pilot symbols

Effective regularization reduces covariance matrix deviation

Iterative optimization aligns with system performance metrics

Abstract

This paper explores the data-aided regularization of the direct-estimate combiner in the uplink of a distributed multiple-input multiple-output system. The network-wide combiner can be computed directly from the pilot signal received at each access point, eliminating the need for explicit channel estimation. However, the sample covariance matrix of the received pilot signal that is used in its computation may significantly deviate from the actual covariance matrix when the number of pilot symbols is limited. To address this, we apply a regularization to the sample covariance matrix using a shrinkage coefficient based on the received data signal. Initially, the shrinkage coefficient is determined by minimizing the difference between the sample covariance matrices obtained from the received pilot and data signals. Given the limitations of this approach in interference-limited scenarios, the shrinkage coefficient is iteratively optimized using the sample mean squared error of the hard-decision symbols, which is more closely related to the actual system's performance, e.g., the symbol error rate (SER). Numerical results demonstrate that the proposed regularization of the direct-estimate combiner significantly enhances the SER, particularly when the number of pilot symbols is limited.