Linear and Deep Neural Network-based Receivers for Massive MIMO Systems with One-Bit ADCs

TL;DR

This paper introduces a two-stage detection approach for massive MIMO systems with one-bit ADCs, combining linear Bussgang-based receivers and a deep neural network to improve detection accuracy while reducing complexity.

Contribution

It proposes a novel two-stage detection method that integrates Bussgang-based linear receivers and a model-driven DNN, enhancing performance and reducing computational complexity in one-bit ADC massive MIMO systems.

Findings

Significant performance gain over existing linear receivers.

Comparable performance of DNN-based receiver to SVM-based methods.

Reduced search complexity with a limited candidate set.

Abstract

The use of one-bit analog-to-digital converters (ADCs) is a practical solution for reducing cost and power consumption in massive Multiple-Input-Multiple-Output (MIMO) systems. However, the distortion caused by one-bit ADCs makes the data detection task much more challenging. In this paper, we propose a two-stage detection method for massive MIMO systems with one-bit ADCs. In the first stage, we propose several linear receivers based on the Bussgang decomposition, that show significant performance gain over existing linear receivers. Next, we reformulate the maximum-likelihood (ML) detection problem to address its non-robustness. Based on the reformulated ML detection problem, we propose a model-driven deep neural network-based (DNN-based) receiver, whose performance is comparable with an existing support vector machine-based receiver, albeit with a much lower computational complexity. A nearest-neighbor search method is then proposed for the second stage to refine the first stage solution. Unlike existing search methods that typically perform the search over a large candidate set, the proposed search method generates a limited number of most likely candidates and thus limits the search complexity. Numerical results confirm the low complexity, efficiency, and robustness of the proposed two-stage detection method.