Q-Learning-based Setting of Cell Individual Offset for Handover of Flying Base Stations

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00359437" target="_blank" >RIV/68407700:21230/22:00359437 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/VTC2022-Spring54318.2022.9860721" target="_blank" >https://doi.org/10.1109/VTC2022-Spring54318.2022.9860721</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/VTC2022-Spring54318.2022.9860721" target="_blank" >10.1109/VTC2022-Spring54318.2022.9860721</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Q-Learning-based Setting of Cell Individual Offset for Handover of Flying Base Stations

Popis výsledku v původním jazyce

Flying base stations (FlyBSs) are widely used to improve coverage and/or quality of service for users in mobile networks. To ensure a seamless mobility of the FlyBSs among the static base stations (SBSs), an efficient handover mechanism is required. We focus on the handover of FlyBSs among SBSs and we dynamically adjust the cell individual offset (CIO) of the SBSs based on their load to increase the sum capacity of the users served by the FlyBSs while considering also a handover cost. Due to complexity of the defined problem and limited knowledge of other parameters required for conventional optimization methods, we adopt Q-learning to solve the problem. For Q-learning, we define a reward function reflecting the tradeoff between the capacity of users and the cost of performed handovers. The proposed Q-learning based approach converges promptly and increases the sum capacity of the users served by the FlyBSs by up to 23% for eight deployed FlyBSs comparing to state-of-the-art algorithms. At the same time, the number of handovers performed by the FlyBSs is notably reduced (up to 25%) by the proposal.

Název v anglickém jazyce

Q-Learning-based Setting of Cell Individual Offset for Handover of Flying Base Stations

Popis výsledku anglicky

Flying base stations (FlyBSs) are widely used to improve coverage and/or quality of service for users in mobile networks. To ensure a seamless mobility of the FlyBSs among the static base stations (SBSs), an efficient handover mechanism is required. We focus on the handover of FlyBSs among SBSs and we dynamically adjust the cell individual offset (CIO) of the SBSs based on their load to increase the sum capacity of the users served by the FlyBSs while considering also a handover cost. Due to complexity of the defined problem and limited knowledge of other parameters required for conventional optimization methods, we adopt Q-learning to solve the problem. For Q-learning, we define a reward function reflecting the tradeoff between the capacity of users and the cost of performed handovers. The proposed Q-learning based approach converges promptly and increases the sum capacity of the users served by the FlyBSs by up to 23% for eight deployed FlyBSs comparing to state-of-the-art algorithms. At the same time, the number of handovers performed by the FlyBSs is notably reduced (up to 25%) by the proposal.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20203 - Telecommunications

Projekt

<a href="/cs/project/LTT20004" target="_blank" >LTT20004: Spolupráce s mezinárodním výzkumným centrem v oblasti digitálních komunikačních systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

ISBN

978-1-6654-8243-1

ISSN

—

e-ISSN

2577-2465

Počet stran výsledku

7

Strana od-do

—

Název nakladatele

IEEE Conference Publications

Místo vydání

Piscataway

Místo konání akce

Helsinki

Datum konání akce

19. 6. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000861825801151