Reinforcement learning-based MTC random access and heterogeneous network resource allocation

With the continuous advancement of technology, ITS (Intelligent Transportation Systems) require various technologies for effective management to further improve transportation efficiency. In this regard, the random access mechanism for communication serves as an effective solution. However, challenges arise when employing RAM (Random Access Mechanisms) in cellular wireless communication due to factors such as heterogeneous networks, complex transmission environments, and uncertain user behavior. To address these issues, this paper proposes a reinforcement learning-based joint resource allocation method.