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.

中文翻译：

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蜂窝M2M通信Q-learning随机接入方案中基于优先级的学习自动机

本文应用学习自动机来提高蜂窝机器对机器 (M2M) 通信系统中基于 Q 学习的随机接入信道 (QL-RACH) 方案的性能。提出了一种优先学习自动机QL-RACH（PLA-QL-RACH）访问方案。该方案采用优先学习自动机技术，通过最小化 M2M 设备与共享蜂窝系统 RACH 的人对人设备的交互和冲突级别来提高吞吐量性能。此外，该方案通过控制惩罚的管理，消除了M2M设备遭受的过度惩罚。仿真结果表明，与 QL-RACH 相比，所提出的 PLA-QL-RACH 方案将 RACH 吞吐量提高了大约 82%，并将访问延迟降低了 79%，并且学习收敛速度更快。