Q-Learning-based Setting of Cell Individual Offset for Handover of Flying Base Stations
Identifikátory výsledku
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>
Alternativní jazyky
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.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
20203 - Telecommunications
Návaznosti výsledku
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
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
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