Enhancing User Throughput in Multi-panel mmWave Radio Access Networks for Beam-based MU-MIMO Using a DRL Method

TL;DR

This paper presents a deep reinforcement learning approach to optimize beam management in multi-panel mmWave MU-MIMO networks, significantly improving throughput and reducing latency in practical scenarios.

Contribution

It introduces a novel DRL-based adaptive beam management framework that models beam selection as an MDP, leveraging spatial correlations and real-time data for enhanced performance.

Findings

Up to 16% increase in user throughput.

Latency reduced by factors of 3 to 7.

Effective dynamic beam adjustment in practical networks.

Abstract

Millimeter-wave (mmWave) communication systems, particularly those leveraging multi-user multiple-input and multiple-output (MU-MIMO) with hybrid beamforming, face challenges in optimizing user throughput and minimizing latency due to the high complexity of dynamic beam selection and management. This paper introduces a deep reinforcement learning (DRL) approach for enhancing user throughput in multi-panel mmWave radio access networks in a practical network setup. Our DRL-based formulation utilizes an adaptive beam management strategy that models the interaction between the communication agent and its environment as a Markov decision process (MDP), optimizing beam selection based on real-time observations. The proposed framework exploits spatial domain (SD) characteristics by incorporating the cross-correlation between the beams in different antenna panels, the measured reference signal received power (RSRP), and the beam usage statistics to dynamically adjust beamforming decisions. As a result, the spectral efficiency is improved and end-to-end latency is reduced. The numerical results demonstrate an increase in throughput of up to 16% and a reduction in latency by factors 3-7x compared to baseline (legacy beam management).