標題: Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO With Low-Precision ADCs
作者: Wen, Chao-Kai
Wang, Chang-Jen
Jin, Shi
Wong, Kai-Kit
Ting, Pangan
電控工程研究所
Institute of Electrical and Control Engineering
關鍵字: Bayes-optimal inference;joint channel-and-data estimation;low-precision ADC;massive MIMO;replica method
公開日期: 15-May-2016
摘要: This paper considers a multiple-input multipleoutput (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.
URI: http://dx.doi.org/10.1109/TSP.2015.2508786
http://hdl.handle.net/11536/133774
ISSN: 1053-587X
DOI: 10.1109/TSP.2015.2508786
期刊: IEEE TRANSACTIONS ON SIGNAL PROCESSING
Volume: 64
Issue: 10
起始頁: 2541
結束頁: 2556
Appears in Collections:Articles