Stochastic Hybrid Combining Design for Quantized Massive MIMO Systems

• Published in: IEEE transactions on vehicular technology Vol. 69; no. 12; pp. 16224 - 16229
• Main Authors: Wang, Yalin, Chen, Xihan, Cai, Yunlong, Hanzo, Lajos
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analog to digital conversion; Analog to digital converters; Analog-digital conversion; Antennas; Channel state information; Interference; MIMO (control systems); Optimization; Performance degradation; Quantization (signal); Quantized massive MIMO; Radio frequency; relaxed stochastic successive convex approximation; stochastic hybrid combining; Uplink
• ISSN: 0018-9545; 1939-9359
• DOI: 10.1109/TVT.2020.3036523

Both the power-dissipation and cost of massive multiple-input multiple-output (mMIMO) systems may be substantially reduced by using low-resolution analog-to-digital converters (LADCs) at the receivers. However, both the coarse quantization of LADCs and the inaccurate instantaneous channel state information (ICSI) degrade the performance of quantized mMIMO systems. To overcome these challenges, we propose a novel stochastic hybrid analog-digital combiner (SHC) scheme for adapting the hybrid combiner to the long-term statistics of the channel state information (SCSI). We seek to minimize the transmit power by jointly optimizing the SHC subject to average rate constraints. For the sake of solving the resultant nonconvex stochastic optimization problem, we develop a relaxed stochastic successive convex approximation (RSSCA) algorithm. Simulations are carried out to confirm the benefits of our proposed scheme over the benchmarkers.