期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 64, 期 10, 页码 2541-2556出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2015.2508786
关键词
Bayes-optimal inference; joint channel-and-data estimation; low-precision ADC; massive MIMO; replica method
资金
- Ministry of Science and Technology of Taiwan [MOST 103-2221-E-110-029-MY3, MOST 104-3115-E-110-001]
- ITRI in Hsinchu, Taiwan [E352J33120]
- National Natural Science Foundation of China [61531011, 61222102]
- International Science and Technology Cooperation Program of China [2014DFT10300]
- EPSRC [EP/M016005/1]
- EPSRC [EP/M016005/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/M016005/1] Funding Source: researchfish
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.
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