Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 20, Issue 2, Pages 1052-1064Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2020.3030570
Keywords
Radio frequency; MIMO communication; Optimization; Downlink; Antenna arrays; Array signal processing; Wireless communication; mmWave communications; large intelligent surface (LIS); joint transceiver-LIS design
Funding
- National Science Foundation of China [61829103, 61871091]
- Natural Science Foundation of China for Outstanding Young Scholars [61722109]
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This article investigates the use of a large intelligent surface in a downlink millimeter-wave multiple-input-multiple-output system to optimize spectral efficiency. By jointly optimizing the reflection coefficients of the LIS and the hybrid precoder/combiner at the base station/user equipment, the optimization goal can be achieved. Simulation results show that the proposed method achieves a favorable propagation environment with reduced computational complexity compared to state-of-the-art algorithms.
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.
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