Access Delay Optimization of Double-Contention Random Access Scheme in Machine-to-Machine Communications

Machine learning-based random access schemes have gained significant attention in recent years. However, optimizing the access delay of such schemes remains a challenge. In this letter, we focus on analyzing and optimizing the mean access delay of the deep neural network-based double-contention random access (DCRA) scheme, proposed in our earlier work. Specifically, by leveraging the characterization of the state transition of each access request, the mean sojourn time of each state can be derived explicitly, based on which the mean access delay of the DCRA scheme is further obtained. To optimize the mean access delay, we propose optimizing the backoff parameters of every MTDs. Simulation results demonstrate that the mean access delay of the DCRA scheme can be significantly reduced compared to existing schemes, by appropriately selecting the backoff parameters of every MTDs.