Machine Learning Based Hybrid Precoding for MmWave MIMO-OFDM with Dynamic Subarray

TL;DR

This paper introduces a machine learning-based hybrid precoding method for mmWave MIMO-OFDM systems with dynamic subarrays, enhancing spectral and energy efficiency over traditional approaches.

Contribution

It develops a PCA-based hybrid precoding scheme combined with a clustering algorithm for dynamic subarrays, improving spectral and energy efficiency in mmWave MIMO systems.

Findings

Outperforms conventional schemes in spectral efficiency.

Achieves higher energy efficiency with dynamic subarrays.

Effective clustering improves precoding performance.

Abstract

Hybrid precoding design can be challenging for broadband millimeter-wave (mmWave) massive MIMO due to the frequency-flat analog precoder in radio frequency (RF). Prior broadband hybrid precoding work usually focuses on fully-connected array (FCA), while seldom considers the energy-efficient partially-connected subarray (PCS) including the fixed subarray (FS) and dynamic subarray (DS). Against this background, this paper proposes a machine learning based broadband hybrid precoding for mmWave massive MIMO with DS. Specifically, we first propose an optimal hybrid precoder based on principal component analysis (PCA) for the FS, whereby the frequency-flat RF precoder for each subarray is extracted from the principle component of the optimal frequency-selective precoders for fully-digital MIMO. Moreover, we extend the PCA-based hybrid precoding to DS, where a shared agglomerative hierarchical clustering (AHC) algorithm developed from machine learning is proposed to group the DS for improved spectral efficiency (SE). Finally, we investigate the energy efficiency (EE) of the proposed scheme for both passive and active antennas. Simulations have confirmed that the proposed scheme outperforms conventional schemes in both SE and EE.