Channel Estimation and Performance Analysis of One-Bit Massive MIMO Systems

TL;DR

This paper proposes a novel channel estimation method for one-bit massive MIMO systems using Bussgang decomposition, providing analytical insights into system performance and design optimization.

Contribution

It introduces a Bussgang-based channel estimator for one-bit MIMO, outperforming previous methods and deriving closed-form achievable rate expressions for system analysis.

Findings

The proposed estimator outperforms existing methods across all SNRs.

Closed-form achievable rate expressions are derived for flat fading channels.

Insights into optimal resource allocation and system design are obtained.

Abstract

This paper considers channel estimation and system performance for the uplink of a single-cell massive multiple-input multiple-output (MIMO) system. Each receive antenna of the base station (BS) is assumed to be equipped with a pair of one-bit analog-to-digital converters (ADCs) to quantize the real and imaginary part of the received signal. We first propose an approach for channel estimation that is applicable for both flat and frequency-selective fading, based on the Bussgang decomposition that reformulates the nonlinear quantizer as a linear functionwith identical first- and second-order statistics. The resulting channel estimator outperforms previously proposed approaches across all SNRs. We then derive closed-form expressions for the achievable rate in flat fading channels assuming low SNR and a large number of users for the maximal ratio and zero forcing receivers that takes channel estimation error due to both noise and one-bit quantization into account. The closed-form expressions in turn allow us to obtain insight into important system design issues such as optimal resource allocation, maximal sum spectral efficiency, overall energy efficiency, and number of antennas. Numerical results are presented to verify our analytical results and demonstrate the benefit of optimizing system performance accordingly.