A Variational Bayesian Perspective on MIMO Detection with Low-Resolution ADCs

This paper proposes data detection methods for massive multiple-input multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs) based on the variational Bayes (VB) inference framework. We 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. 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. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods.