Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs

TL;DR

This paper introduces a Bayes-optimal joint channel-and-data estimation method for massive MIMO systems with low-precision ADCs, enabling efficient and accurate system performance analysis and design.

Contribution

It develops a novel Bayes-optimal joint estimation technique using approximate message passing for quantized massive MIMO, with an analytical performance framework.

Findings

The proposed estimator achieves minimal mean square error in large systems.

Analytical results match simulation, validating the approach.

Insights into system design for low-precision ADC massive MIMO.

Abstract

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be {\em relatively long} to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.