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Journal Article
Multi-agent Q-learning based cell breathing considering SBS collaboration for maximizing energy efficiency in B5G heterogeneous networks
- Authors
- Howon Lee, Eunjin Kim, Hyungsub Kim, JeeHyeon Na, Hyun-Ho Choi
- Issue Date
- 2022-12
- Citation
- ICT Express, v.8, no.4, pp.525-529
- ISSN
- 2405-9595
- Publisher
- 한국통신학회 (KICS), Elsevier
- Language
- English
- Type
- Journal Article
- Abstract
- 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 Keywords
- 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)
This work is distributed under the term of Creative Commons License (CCL)
(CC BY NC ND)
(CC BY NC ND)
