Double Attention LSTM

RAN Slicing (RS) is a 6G Radio Access Network technology for resource allocation that meets the diverse needs of users. The current surge in traffic across vertical services requires a paradigm capable of handling unpredictability in a stochastic environment. This paper follows a sequential approach to address the differing needs in RS. We propose an extended application powered by a Double Deep Q Network (DDQN) to instantiate an Apprentice Agent (AA) that serves users based on on-demand requests.