Uplink Multiuser Massive MIMO Systems with One-Bit ADCs: A Coding-Theoretic Viewpoint

TL;DR

This paper introduces a coding-theoretic detection framework for uplink multiuser massive MIMO systems with one-bit ADCs, transforming the detection problem into a channel coding problem and demonstrating near-ideal performance.

Contribution

It proposes a novel coding-theoretic MIMO detection framework that models the system as a non-linear code and develops practical decoding methods without channel state information.

Findings

The framework converts MIMO detection into a channel coding problem.

The minimum distance of the code relates to the system's diversity order.

Simulations show near-ideal performance with training-based implementation.

Abstract

This paper investigates an uplink multiuser massive multiple-input multiple-output (MIMO) system with one-bit analog-to-digital converters (ADCs), in which $K$ users with a single-antenna communicate with one base station (BS) with $n_r$ antennas. In this system, we propose a novel MIMO detection framework, which is inspired by coding theory. The key idea of the proposed framework is to create a non-linear code $\Cc$ of length $n_r$ and rate $K/n_r$ using the encoding function that is completely characterized by a non-linear MIMO channel matrix. From this, a multiuser MIMO detection problem is converted into an equivalent channel coding problem, in which a codeword of the $\Cc$ is sent over $n_r$ parallel binary symmetric channels, each with different crossover probabilities. Levereging this framework, we develop a maximum likelihood decoding method, and show that the minimum distance of the $\Cc$ is strongly related to a diversity order. Furthermore, we propose a practical implementation method of the proposed framework when the channel state information is not known to the BS. The proposed method is to estimate the code $\Cc$ at the BS using a training sequence. Then, the proposed {\em weighted} minimum distance decoding is applied. Simulations results show that the proposed method almost achieves an ideal performance with a reasonable training overhead.