Coordinated Machine Learning for Handover in Mobile Networks with Transparent Relaying UAVs

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00382099" target="_blank" >RIV/68407700:21230/24:00382099 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ICCWORKSHOPS59551.2024.10615707" target="_blank" >https://doi.org/10.1109/ICCWORKSHOPS59551.2024.10615707</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICCWORKSHOPS59551.2024.10615707" target="_blank" >10.1109/ICCWORKSHOPS59551.2024.10615707</a>

Result language

angličtina

Original language name

Coordinated Machine Learning for Handover in Mobile Networks with Transparent Relaying UAVs

Original language description

The Unmanned Aerial Vehicles (UAVs) acting as relays in the mobile networks are usually energy constrained. To improve the energy efficiency of such networks, UAVs should operate in a transparent relaying mode. In such mode, however, the channel quality between users and UAVs cannot be measured, since the transparent relays do not transmit own signaling. A lack of information on the quality of channel between users and UAVs limits practical implementation and is serious restraint for mobility management. To overcome this limitation, we develop a novel concept of coordinated machine learning for handover of users and UAVs in the mobile networks with transparent relaying UAVs. First, we predict the channel quality from other known information in the network via deep neural network (DNN). Such predicted channel quality is then fed into deep reinforcement learning (DRL) for an adjustment of handover parameter - cell individual offset (CIO). Unfortunately, a simple concatenation of the DNN and the DRL leads to a notable performance degradation. Hence, we propose a coordination of the DNN for channel quality prediction and the DRL for CIO setting. The coordination consists in a mutual exchange of performance-related information and an update of DNN according to a reward of DRL. The proposal increases the sum capacity by up to 12.7% while reducing the number of user and UAV handovers by up to 12.9% and 16.4%, respectively, compared to related works.

Czech name

—

Czech description

—

Type

D - Article in proceedings

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

2024

Confidentiality

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

Article name in the collection

2024 IEEE International Conference on Communications Workshops

ISBN

979-8-3503-0406-0

ISSN

2164-7038

e-ISSN

—

Number of pages

6

Pages from-to

1761-1766

Publisher name

Institute of Electrical and Electronic Engineers

Place of publication

Piscataway

Event location

Denver

Event date

Jun 9, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001296276700292