Distributed Q-Learning-Assisted Grant-Free NORA for Massive Machine-Type Communications

Zhenjiang Shi, Wei Gao, Jiajia Liu, Nei Kato, Yanning Zhang

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Large-scale connectivity support is a critical challenge in the massive machine-type communications scenario. Grant-free random access (RA) is a promising solution because it can reduce severe signaling overhead in contention-based RA procedure. However, there will still be collisions due to the random selection of spectrum resources by the devices. Therefore, we propose a distributed Q-learning-assisted grant-free RA scheme to alleviate the collisions between devices. Considering the characteristic of the machine-type communications devices with bursty traffic, the random packet arrival model is adopted in this paper. In order to cope with the difficulties brought by the random transmission of devices to Q-learning, an action reward based on the active probabilities of devices is designed. In addition, we introduce the power domain nor-orthogonal multiple access to further enhance the number of accessible devices. Numerical results demonstrate the advantages of the proposed scheme from the devices' successful access probability.

本文言語English
ホスト出版物のタイトル2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728182988
DOI
出版ステータスPublished - 2020 12月
イベント2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
継続期間: 2020 12月 72020 12月 11

出版物シリーズ

名前2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

Conference

Conference2020 IEEE Global Communications Conference, GLOBECOM 2020
国/地域Taiwan, Province of China
CityVirtual, Taipei
Period20/12/720/12/11

ASJC Scopus subject areas

  • メディア記述
  • モデリングとシミュレーション
  • 器械工学
  • 人工知能
  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ
  • ソフトウェア
  • 安全性、リスク、信頼性、品質管理

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