Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO with Low-Precision ADCs

摘要

This paper considers a multiple-input multiple-output (MIMO) receiver withvery low-precision analog-to-digital convertors (ADCs) with the goal ofdeveloping massive MIMO antenna systems that require minimal cost and power.Previous studies demonstrated that the training duration should be {\emrelatively long} to obtain acceptable channel state information. To addressthis requirement, we adopt a joint channel-and-data (JCD) estimation methodbased on Bayes-optimal inference. This method yields minimal mean square errorswith respect to the channels and payload data. We develop a Bayes-optimal JCDestimator using a recent technique based on approximate message passing. Wethen present an analytical framework to study the theoretical performance ofthe estimator in the large-system limit. Simulation results confirm ouranalytical results, which allow the efficient evaluation of the performance ofquantized massive MIMO systems and provide insights into effective systemdesign.