Deep Reinforcement Learning-based Prediction of User Speed for Handover Optimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00390773" target="_blank" >RIV/68407700:21230/25:00390773 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/VTC2025-Fall65116.2025.11309977" target="_blank" >https://doi.org/10.1109/VTC2025-Fall65116.2025.11309977</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/VTC2025-Fall65116.2025.11309977" target="_blank" >10.1109/VTC2025-Fall65116.2025.11309977</a>

Result language

angličtina

Original language name

Deep Reinforcement Learning-based Prediction of User Speed for Handover Optimization

Original language description

Seamless connectivity and efficient mobility management in mobile networks can be facilitated via estimation of the speed and mobility state of user equipment (UE). In this paper, we propose a deep reinforcement learning (DRL) framework for prediction of the UE speed based on the number of performed handovers and density of all base stations (BSs), including both macro base stations (MBSs) and small-cell base stations (SBSs). We leverage the capability of DRL to learn and adapt to complex mobility patterns, enhancing the accuracy of the speed prediction in highly dynamic environments. To demonstrate benefits of the DRL-based UE speed prediction, we further exploit the predicted speed to determine the UEs’ mobility states. The mobility state serves as an input for handover optimization via fuzzy logic. By incorporating the speed prediction, the system proactively anticipates changes in the UEs’ mobility and enables more precise and timely optimization of handover parameters. The simulation with realistic UE mobility traces shows that the proposed DRL improves the accuracy of the speed prediction by up to 35% and reduces the prediction root mean square error by up to 59% compared to the state-of-the-art algorithms. Moreover, using the proposed DRL-based speed prediction for fuzzy logic-based handover optimization improves the UEs’ sum capacity by up to 43% and reduces the number of handovers by up to 39% compared to state-of-the-art works.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

20203 - Telecommunications

Project

<a href="/en/project/GA23-05646S" target="_blank" >GA23-05646S: Intelligent Radio Resource and Mobility Management based on Federated Learning</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

IEEE 102nd Vehicular Technology Conference (VTC2025-Fall)

ISBN

979-8-3315-0320-8

ISSN

2577-2465

e-ISSN

2577-2465

Number of pages

7

Pages from-to

1-7

Publisher name

IEEE

Place of publication

New York

Event location

Chengdu

Event date

Oct 19, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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