详细信息
Performance Analysis and Transceiver Design of Few-Bit Quantized MIMO Systems ( SCI-EXPANDED收录 EI收录)
文献类型:期刊文献
英文题名:Performance Analysis and Transceiver Design of Few-Bit Quantized MIMO Systems
作者:Ling, Xiaofeng[1];Wang, Rui[2]
机构:[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Tongji Univ, Dept Informat & Commun, Shanghai 201804, Peoples R China
年份:2019
卷号:7
起止页码:9935
外文期刊名:IEEE ACCESS
收录:;EI(收录号:20190706488506);WOS:【SCI-EXPANDED(收录号:WOS:000458002400001)】;
基金:This work was supported in part by the National Science Foundation China under Grant 61771345, in part by the Fundamental Research Funds for the Central Universities, in part by the Shanghai Science and Technology Committee under Grant 18142200800, and in part by the Shanghai Automotive Industry Science and Technology Development Fund under Grant 1835.
语种:英文
外文关键词:Multi-input multi-output (MIMO); low resolution analog-to-digital convertors; achievable rate
摘要:Utilizing low-resolution analog-to-digital converters (ADCs) was proven to be efficient in releasing the burden of power consumption for future wireless systems. In this paper, we analyze and optimize the achievable rate of the multi-input multi-output channel with low-resolution ADCs at the receiver by assuming that the channel state information is known at both the transmitter and the receiver. Toward this end, we first derive the approximate achievable rate of the considered channel model by exploiting the Bussgang theorem. According to the derived achievable rate expression, we propose two approaches, namely, the singular value decomposition-based approach and the gradient-based approach, to jointly optimize the transmit signal covariance and the receive analog combiner. Moreover, an upper bound of achievable rate is derived from the information theory point of view to evaluate the optimality of the derived achievable rate. Extensive simulation results are provided to assess the performance of the proposed designs. We show that the proposed designs can reach the upper bound at low signal-to-noise ratio (SNR), which implies that our optimized achievable rate approaches the capacity at low SNR.
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