Machine Learning Inspired Energy-Efficient Hybrid Precoding for MmWave Massive MIMO Systems

TL;DR

This paper introduces an energy-efficient hybrid precoding architecture for mmWave massive MIMO systems using switches and inverters, and proposes an adaptive machine learning-based optimization scheme to achieve near-optimal performance.

Contribution

It presents a novel low-energy hybrid precoding architecture and an adaptive cross-entropy optimization scheme inspired by machine learning for improved performance.

Findings

Achieves near-optimal sum-rate performance.

Significantly higher energy efficiency than traditional schemes.

Performance gap remains small and constant as the number of antennas increases.

Abstract

Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.