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학술지 Multi-agent Q-learning based cell breathing considering SBS collaboration for maximizing energy efficiency in B5G heterogeneous networks
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저자
이호원, 김은진, 김형섭, 나지현, 최현호
발행일
202212
출처
ICT Express, v.8 no.4, pp.525-529
ISSN
2405-9595
출판사
한국통신학회 (KICS), Elsevier
DOI
https://dx.doi.org/10.1016/j.icte.2021.09.006
협약과제
20HH7700, 5G 스몰셀을 위한 인공지능 기반 자율구성 네트워크(SON) 기술 개발, 나지현
초록
In B5G heterogeneous cellular networks, a rapid increase in the number of small cell base stations (SBSs) to support a massive number of devices tends to waste a considerable amount of energy. Therefore, intelligent management of SBSs?? power consumption is one of the most important research issues. We herein propose quasi-distributed Q-learning-based cell breathing (QD-QCB) considering full and partial SBS collaborations for maximizing network energy efficiency. Also, the concept of an aggregated active SBS set based on regional user distributions is proposed for computing- and energy-efficient operation. Through intensive simulations, we show that the proposed QD-QCB algorithm can achieve optimal energy efficiency, and improve the network energy efficiency significantly compared with conventional algorithms such as no transmit power control, random cell breathing, and greedy cell breathing algorithms.
KSP 제안 키워드
Distributed Q-learning, Energy-efficient operation, Heterogeneous cellular networks, Learning-based, Power Consumption, Power control(PC), Quasi-distributed, Research Issues, Small cells, Transmit power control, base station(BS)
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저작자 표시 - 비영리 - 변경금지 (CC BY NC ND)