Multi-Agent Deep Reinforcement Learning for Distributed Resource Management in Wirelessly Powered Communication Networks

Sangwon Hwang, Hanjin Kim, Hoon Lee, Inkyu Lee

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

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.

Original languageEnglish
Article number9217951
Pages (from-to)14055-14060
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Volume69
Issue number11
DOIs
Publication statusPublished - 2020 Nov

Bibliographical note

Funding Information:
Manuscript received August 28, 2020; revised September 29, 2020; accepted September 30, 2020. Date of publication October 8, 2020; date of current version November 12, 2020. This work was supported in part by the National Research Foundation (NRF) through the Ministry of Science, ICT, and Future Planning (MSIP), Korea Government under Grant 2017R1A2B3012316 and in part by the NRF through the Ministry of Science, ICT (MSIT), Korea Government under Grant 2019R1F1A1060648. The review of this article was coordinated by Dr. Zubair Fadlullah. (Corresponding author: Inkyu Lee.) Sangwon Hwang and Inkyu Lee are with the School of Electrical Engineering, Korea University, Seoul 02841, Korea (e-mail: tkddnjs3510@korea.ac.kr; inkyu@korea.ac.kr).

Publisher Copyright:
© 1967-2012 IEEE.

Keywords

  • Wireless powered communication networks
  • actor-critic method
  • multi-agent deep reinforcement learning

ASJC Scopus subject areas

  • Automotive Engineering
  • Aerospace Engineering
  • Electrical and Electronic Engineering
  • Applied Mathematics

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