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Priority-based learning automata in Q-learning random access scheme for cellular M2M communications

  • Shinkafi, Nasir A. (Department of Electrical Engineering, Bayero University) ;
  • Bello, Lawal M. (Department of Electrical Engineering, Bayero University) ;
  • Shu'aibu, Dahiru S. (Department of Electrical Engineering, Bayero University) ;
  • Mitchell, Paul D. (Department of Electronic Engineering, University of York)
  • Received : 2020.03.12
  • Accepted : 2020.10.15
  • Published : 2021.10.01
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Abstract

This paper applies learning automata to improve the performance of a Q-learning based random access channel (QL-RACH) scheme in a cellular machine-to-machine (M2M) communication system. A prioritized learning automata QL-RACH (PLA-QL-RACH) access scheme is proposed. The scheme employs a prioritized learning automata technique to improve the throughput performance by minimizing the level of interaction and collision of M2M devices with human-to-human devices sharing the RACH of a cellular system. In addition, this scheme eliminates the excessive punishment suffered by the M2M devices by controlling the administration of a penalty. Simulation results show that the proposed PLA-QL-RACH scheme improves the RACH throughput by approximately 82% and reduces access delay by 79% with faster learning convergence when compared with QL-RACH.

Keywords

Acknowledgement

The authors acknowledge the support and facilities available at their respective institutions which has allowed this research to be fulfilled.

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