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

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

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

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728182988
DOIs
Publication statusPublished - 2020 Dec
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 2020 Dec 72020 Dec 11

Publication series

Name2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings

Conference

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

Keywords

  • Machine-type communications
  • distributed Q-learning
  • grant-free random access

ASJC Scopus subject areas

  • Media Technology
  • Modelling and Simulation
  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software
  • Safety, Risk, Reliability and Quality

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