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

TL;DR

This paper proposes a joint design of transceivers and large intelligent surfaces for mmWave massive MIMO systems, enhancing spectral efficiency with a low-complexity manifold optimization approach.

Contribution

It introduces a novel joint optimization framework for LIS reflection coefficients and hybrid precoding in mmWave MIMO, leveraging channel structure for improved efficiency.

Findings

Achieves spectral efficiency comparable to state-of-the-art methods.

Reduces computational complexity significantly.

Creates favorable propagation environments with optimized LIS reflection.

Abstract

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 paper, 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.