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

被引:98
|
作者
Wang, Peilan [1 ]
Fang, Jun [1 ]
Dai, Linglong [2 ]
Li, Hongbin [3 ]
机构
[1] Univ Elect Sci & Technol China, Natl Key Lab Sci & Technol Commun, Chengdu 611731, Peoples R China
[2] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China
[3] Stevens Inst Technol, Dept Elect & Comp Engn, Hoboken, NJ 07030 USA
基金
美国国家科学基金会;
关键词
Radio frequency; MIMO communication; Optimization; Downlink; Antenna arrays; Array signal processing; Wireless communication; mmWave communications; large intelligent surface (LIS); joint transceiver-LIS design; WIRELESS COMMUNICATION; CHANNEL ESTIMATION; OPTIMIZATION; MODELS;
D O I
10.1109/TWC.2020.3030570
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:1052 / 1064
页数:13
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