Power Control Based on Multi-Agent Deep Q Network for D2D Communication

Shi Gengtian, Takashi Koshimizu, Megumi Saito, Pan Zhenni, Liu Jiang, Shigeru Shimamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In device-to-device (D2D) communication under a cell with resource sharing mode the spectrum resource utilization of the system will be improved. However, if the interference generated by the D2D user is not controlled, the performance of the entire system and the quality of service (QOS) of the cellular user may be degraded. Power control is important because it helps to reduce interference in the system. In this paper, we propose a reinforcement learning algorithm for adaptive power control that helps reduce interference to increase system throughput. Simulation results show the proposed algorithm has better performance than traditional algorithm in LTE (Long Term Evolution).

Original languageEnglish
Title of host publication2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages257-261
Number of pages5
ISBN (Electronic)9781728149851
DOIs
Publication statusPublished - 2020 Feb
Event2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan
Duration: 2020 Feb 192020 Feb 21

Publication series

Name2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020

Conference

Conference2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
CountryJapan
CityFukuoka
Period20/2/1920/2/21

Keywords

  • D2D
  • Deep Q Network
  • Power Control
  • Reinforcement Learning
  • System throughput

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Signal Processing

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