Coordinated Learning for Handover Management in 6G Networks With Transparent UAV Relays

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00386818" target="_blank" >RIV/68407700:21230/25:00386818 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/TCOMM.2025.3555857" target="_blank" >https://doi.org/10.1109/TCOMM.2025.3555857</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TCOMM.2025.3555857" target="_blank" >10.1109/TCOMM.2025.3555857</a>

Result language

angličtina

Original language name

Coordinated Learning for Handover Management in 6G Networks With Transparent UAV Relays

Original language description

We focus on handover management in networks integrating traditional terrestrial ground base stations (GBSs) and non-terrestrial unmanned aerial vehicles (UAVs) serving users. In such scenario, we propose a joint management of handover of users equipment (UEs) between UAVs and GBSs as well as handover of UAVs between GBSs. Our goal is to maximize the sum capacity for UEs while avoiding redundant handovers. As an impact of handover on the future network performance is not explicit, we adopt deep deterministic policy gradient (DDPG). Furthermore, since the UAVs are usually energy constrained, we consider an energy efficient transparent relaying mode for the UAVs. However, in the transparent relaying mode, the access channel quality between the UAV relay and the UE is unknown even if such information is essential for handover. Thus, we further employ deep neural network (DNN) to predict the access channel quality. An incorporation of DDPG for handover management together with DNN for channel quality prediction can impair the sum capacity of the UEs due to an accumulation of inherent small prediction errors of DNN and DDPG. Hence, we also introduce a coordination between DNN and DDPG to suppress the accumulation of the prediction errors. Simulations demonstrate that the proposed handover management and the coordination of DDPG and DNN increase the sum capacity by up to 63% while notably reducing the number of handovers and handover failure ratio compared to the state-of-the-art works.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

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

Name of the periodical

IEEE Transactions on Communications

ISSN

0090-6778

e-ISSN

1558-0857

Volume of the periodical

73

Issue of the periodical within the volume

10

Country of publishing house

US - UNITED STATES

Number of pages

16

Pages from-to

9553-9568

UT code for WoS article

001605128000048

EID of the result in the Scopus database

2-s2.0-105001524191