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http://dx.doi.org/10.4218/etrij.2020-0091

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)
Publication Information
ETRI Journal / v.43, no.5, 2021 , pp. 787-798 More about this Journal
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
learning automata; LTE network; machine to machine; Q-learning; RACH congestion;
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