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

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.