상세정보
학술지
Multi-agent Q-learning based cell breathing considering SBS collaboration for maximizing energy efficiency in B5G heterogeneous networks
- 발행일
- 202212
- 출처
- ICT Express, v.8 no.4, pp.525-529
- ISSN
- 2405-9595
- 출판사
- 한국통신학회 (KICS), Elsevier
- 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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