Optimization of Retransmission for Short Packet in MTC Devices

The growth of the Internet of Things has given rise to various innovative services such as self-driving vehicles, remote operations, immersive technologies, tactile Internet and automated factories. Machine Type Communication (MTC) data, primarily transmitted through short packets, is the backbone of these applications. However, short packets have limited error correction capabilities, thus it is necessitating the use of retransmission techniques to increase the reliability of Short Packet Communication (SPC). While retransmissions exploit time diversity to lower the bit error rate, excessive retransmissions can degrade system performance. Consequently, refining retransmission policies is a crucial research area for SPC systems. In this paper, an SPC system model with Incremental Redundancy Hybrid Automatic Repeat reQuest (IR-HARQ) is studied to optimize the number of retransmissions under various conditions by determining an ideal block-length selection policy. A Probabilistic Q-learning (PQL) algorithm based on the two-factor theory is proposed to determine the optimal block length selection by striving to optimize the retransmission. Simulation results show that the proposed algorithm can effectively improve the performance of retransmission system.