Optimization of Dynamic Frame Length for Random Access in Machine-Type Communication Systems

With the rapid development of the Internet of Things and 5G communication technologies, the demand for the random access of a massive number of user equipment in burst scenarios has significantly increased. Traditional fixed-frame-length mechanisms, due to their inability to dynamically adapt to fluctuations in access traffic, are prone to exacerbating channel resource competition, increasing the probability of preamble collisions, and significantly elevating access delays, thereby constraining the system performance of large-scale machine-type communications. To address these issues, this paper proposes a dynamic frame length optimization algorithm based on Q-learning. By leveraging reinforcement learning algorithms to autonomously perceive access traffic characteristics, this algorithm can dynamically adjust frame length parameters without relying on estimates of the number of user equipment. It optimizes the frame length to improve random access performance, reduces collisions among user equipment competing for preambles, and enhances the utilization ratio of preamble resources.