Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems

TL;DR

This paper introduces variational Bayes based joint channel estimation and data detection techniques for low-resolution ADC massive MIMO systems, addressing non-linearity and interference challenges.

Contribution

It develops novel VB inference methods for joint estimation and detection in low-resolution ADC MIMO systems, including noise variance modeling for improved robustness.

Findings

Proposed VB methods outperform existing detection techniques.

The methods are robust to noise and interference.

Numerical results validate the effectiveness of the approaches.

Abstract

Massive multiple-input multiple-output (MIMO) communications using low-resolution analog-to-digital converters (ADCs) is a promising technology for providing high spectral and energy efficiency with affordable hardware cost and power consumption. However, the use of low-resolution ADCs requires special signal processing methods for channel estimation and data detection since the resulting system is severely non-linear. This paper proposes joint channel estimation and data detection methods for massive MIMO systems with low-resolution ADCs based on the variational Bayes (VB) inference framework. We first derive matched-filter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Then we extend these methods to the joint channel estimation and data detection (JED) problem and propose two methods we refer to as MF-QVB-JED and LMMSE-QVB-JED. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to float the noise variance/covariance matrix as an unknown random variable that is used to account for both the noise and the residual inter-user interference. We also present practical aspects of the QVB framework to improve its implementation stability. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods.