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

• Published in: IEEE transactions on signal processing Vol. 64; no. 10; pp. 2541 - 2556
• Main Authors: Wen, Chao-Kai, Wang, Chang-Jen, Jin, Shi, Wong, Kai-Kit, Ting, Pangan
• Format: Journal Article
• Language: English
• Published: New York IEEE 15-05-2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bayes-optimal inference; Channel estimation; Electronic mail; Estimation; Gain control; joint channel-and-data estimation; low-precision ADC; massive MIMO; Mean square errors; MIMO; Quantization (signal); Receiving antennas; replica method
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2015.2508786

This paper considers a multiple-input multiple-output (MIMO) receiver with very low-precision analog-to-digital convertors (ADCs) with the goal of developing massive MIMO antenna systems that require minimal cost and power. Previous studies demonstrated that the training duration should be relatively long to obtain acceptable channel state information. To address this requirement, we adopt a joint channel-and-data (JCD) estimation method based on Bayes-optimal inference. This method yields minimal mean square errors with respect to the channels and payload data. We develop a Bayes-optimal JCD estimator using a recent technique based on approximate message passing. We then present an analytical framework to study the theoretical performance of the estimator in the large-system limit. Simulation results confirm our analytical results, which allow the efficient evaluation of the performance of quantized massive MIMO systems and provide insights into effective system design.