Joint Transceiver and Large Intelligent Surface Design for Massive MIMO mmWave Systems

• Published in: IEEE transactions on wireless communications Vol. 20; no. 2; pp. 1052 - 1064
• Main Authors: Wang, Peilan, Fang, Jun, Dai, Linglong, Li, Hongbin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Antenna arrays; Array signal processing; Complexity; Data transmission; Downlink; Downlinking; Energy consumption; joint transceiver-LIS design; large intelligent surface (LIS); Millimeter waves; MIMO (control systems); MIMO communication; mmWave communications; Optimization; Propagation; Radio frequency; Transceivers; Wireless communication
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2020.3030570

Large intelligent surface (LIS) has recently emerged as a potential low-cost solution to reshape the wireless propagation environment for improving the spectral efficiency. In this article, we consider a downlink millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) system, where an LIS is deployed to assist the downlink data transmission from a base station (BS) to a user equipment (UE). Both the BS and the UE are equipped with a large number of antennas, and a hybrid analog/digital precoding/combining structure is used to reduce the hardware cost and energy consumption. We aim to maximize the spectral efficiency by jointly optimizing the LIS's reflection coefficients and the hybrid precoder (combiner) at the BS (UE). To tackle this non-convex problem, we reformulate the complex optimization problem into a much more friendly optimization problem by exploiting the inherent structure of the effective (cascade) mmWave channel. A manifold optimization (MO)-based algorithm is then developed. Simulation results show that by carefully devising LIS's reflection coefficients, our proposed method can help realize a favorable propagation environment with a small channel matrix condition number. Besides, it can achieve a performance comparable to those of state-of-the-art algorithms, while at a much lower computational complexity.