A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols in Mobile Networks

TL;DR

This paper introduces a deep reinforcement learning-based adaptive handover protocol that improves data rates and reduces failures in mobile networks by learning from environment interactions, outperforming standard 5G protocols.

Contribution

It presents a novel reinforcement learning approach for handover optimization that adapts to user mobility and outperforms existing 3GPP standards.

Findings

Outperforms 3GPP handover protocol in data rate and failure reduction

Highly flexible to different user speeds

Accurate environment design for fair comparison

Abstract

Due to an ever-increasing number of participants and new areas of application, the demands on mobile communications systems are continually increasing. In order to deliver higher data rates, enable mobility and guarantee QoS requirements of subscribers, these systems and the protocols used are becoming more complex. By using higher frequency spectrums, cells become smaller and more base stations have to be deployed. This leads to an increased number of handovers of user equipments between base stations in order to enable mobility, resulting in potentially more frequent radio link failures and rate reduction. The persistent switching between the same base stations, commonly referred to as "ping-pong", leads to a consistent reduction of data rates. In this work, we propose a method for handover optimization by using proximal policy optimization in mobile communications to learn an adaptive handover protocol. The resulting agent is highly flexible regarding different travelling speeds of user equipments, while outperforming the standard 5G NR handover protocol by 3GPP in terms of average data rate and number of radio link failures. Furthermore, the design of the proposed environment demonstrates remarkable accuracy, ensuring a fair comparison with the standard 3GPP protocol.