Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks
- Authors
- Hwang, Sangwon; Kim, Hanjin; Lee, Hoon; Lee, Inkyu
- Issue Date
- 11월-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Resource management; Interference; Optimization; Uplink; Wireless communication; Downlink; Wireless sensor networks; Wireless powered communication networks; multi-agent deep reinforcement learning; actor-critic method
- Citation
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, v.69, no.11, pp.14055 - 14060
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
- Volume
- 69
- Number
- 11
- Start Page
- 14055
- End Page
- 14060
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/52026
- DOI
- 10.1109/TVT.2020.3029609
- ISSN
- 0018-9545
- Abstract
- This paper studies multi-agent deep reinforcement learning (MADRL) based resource allocation methods for multi-cell wireless powered communication networks (WPCNs) where multiple hybrid access points (H-APs) wirelessly charge energy-limited users to collect data from them. We design a distributed reinforcement learning strategy where H-APs individually determine time and power allocation variables. Unlike traditional centralized optimization algorithms which require global information collected at a central unit, the proposed MADRL technique models an H-AP as an agent producing its action based only on its own locally observable states. Numerical results verify that the proposed approach can achieve comparable performance of the centralized algorithms.
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