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

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

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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).

本文言語English
ホスト出版物のタイトル2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ257-261
ページ数5
ISBN(電子版)9781728149851
DOI
出版ステータスPublished - 2020 2
イベント2nd International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 - Fukuoka, Japan
継続期間: 2020 2 192020 2 21

出版物シリーズ

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

Conference

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

ASJC Scopus subject areas

  • 情報システムおよび情報管理
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識
  • 情報システム
  • 信号処理

フィンガープリント

「Power Control Based on Multi-Agent Deep Q Network for D2D Communication」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル