Non-Cooperative Energy Efficient Power Allocation Game in D2D Communication: A Multi-Agent Deep Reinforcement Learning Approach

Khoi Khac Nguyen, Trung Q. Duong*, Ngo Anh Vien, Nhien An Le-Khac, Minh-Nghia Nguyen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)
101 Downloads (Pure)

Abstract

Recently, there is the widespread use of mobile devices and sensors, and rapid emergence of new wireless and networking technologies, such as wireless sensor network, device-to-device (D2D) communication, and vehicular ad hoc networks. These networks are expected to achieve a considerable increase in data rates, coverage, and the number of connected devices with a significant reduction in latency and energy consumption. Because there are energy resource constraints in user’s devices and sensors, the problem of wireless network resource allocation becomes much more challenging. This leads to the call for more advanced techniques in order to achieve a tradeoff between energy consumption and network performance. In this paper, we propose to use reinforcement learning, an efficient simulation-based optimization framework, to tackle this problem so that user experience is maximized. Our main contribution is to propose a novel non-cooperative and real-time approach based on deep reinforcement learning to deal with the energy-efficient power allocation problem while still satisfying the quality of service constraints in D2D communication.

Original languageEnglish
Pages (from-to)100480-100490
Number of pages11
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 19 Jul 2019

Bibliographical note

Funding Information:
This work was supported in part by the Newton Prize 2017 and the Newton Fund Institutional Link through the Fly-by Flood Monitoring Project under Grant ID 428328486, which is delivered by the British Council.

Publisher Copyright:
© 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

Keywords

  • D2D communication
  • Deep reinforcement learning
  • Energy efficient wireless communication
  • Multi-agent reinforcement learning
  • Power allocation

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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