Deep Reinforcement Learning Based Dynamic Multichannel Access in HetNets

This paper deals with the problem of the dynamic multichannel access (MCA) based on the LTE-WLAN aggregation in dynamic heterogeneous networks. To ensure the users' personalized requirements, the minimization after satisfied (MAS) criterion is firstly proposed. Then, a prediction-based deep deterministic policy gradient (P-DDPG) algorithm is presented, achieving continuous processing. Finally, a new reward function is designed according to the MAS criteria. Meanwhile, the virtual user approach and the base station-centric strategy are proposed to design the action spaces so that the number of actions is independent of the number of users. Simulation results demonstrate the effectiveness of the P-DDPG algorithm for solving the dynamic MCA problem and corroborate that the proposed MAS criterion is superior to the existing criteria.