The use of higher frequencies in mobile communication systems leads to smaller cell sizes, resulting in the deployment of more base stations and an increase in handovers to support user mobility. This can lead to frequent radio link failures and reduced data rates. In this work, we propose a handover optimization method using proximal policy optimization (PPO) to develop an adaptive handover protocol. Our PPO-based agent, implemented in the base stations, is highly adaptive to varying user equipment speeds and outperforms the 3GPP-standardized 5G NR handover procedure in terms of average data rate and radio link failure rate. Additionally, our simulation environment is carefully designed to ensure high accuracy, realistic user movements, and fair benchmarking against the 3GPP handover method.
@article{arxiv.2503.21601,
title = {A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols},
author = {Johannes Voigt and Peter Jiacheng Gu and Peter Rost},
journal= {arXiv preprint arXiv:2503.21601},
year = {2025}
}