Deep Learning based Multi-User Power Allocation and Hybrid Precoding in Massive MIMO Systems

TL;DR

This paper introduces a deep learning-based power allocation method for multi-user massive MIMO systems that significantly reduces computational time while maintaining near-optimal performance, enabling real-time applications.

Contribution

It presents a novel deep neural network approach for power allocation in MU-mMIMO, combining hybrid precoding with offline supervised learning for fast online predictions.

Findings

DL-PA reduces runtime by over 98% compared to PSO-PA.

DL-PA achieves near-optimal sum-rate capacity.

The hybrid precoding technique decreases RF chains and channel estimation overhead.

Abstract

This paper proposes a deep learning based power allocation (DL-PA) and hybrid precoding technique for multiuser massive multiple-input multiple-output (MU-mMIMO) systems. We first utilize an angular-based hybrid precoding technique for reducing the number of RF chains and channel estimation overhead. Then, we develop the DL-PA algorithm via a fully-connected deep neural network (DNN). DL-PA has two phases: (i) offline supervised learning with the optimal allocated powers obtained by particle swarm optimization based PA (PSO-PA) algorithm, (ii) online power prediction by the trained DNN. In comparison to the computationally expensive PSO-PA, it is shown that DL-PA greatly reduces the runtime by 98.6%-99.9%, while closely achieving the optimal sum-rate capacity. It makes DL-PA a promising algorithm for the real-time online applications in MU-mMIMO systems.