Power Allocation in Multi-Cell Networks Using Deep Reinforcement Learning

In this paper, multi-cell power allocation approach is researched. Different from the traditional optimization decomposition method, Deep Reinforcement Learning (DRL) method is employed to solve the power allocation issue which is an NP-hard problem. The objective of our work is to maximize the overall capacity of the entire network in the scenario where the base stations are randomly and densely distributed. We propose a wireless resource mapping method and a deep neural network for multi-cell power allocation named as Deep-Q-Full-Connected-Network (DQFCNet). Compared with the water-filling power allocation and Q-learning method, DQFCNet can achieve a higher overall capacity. Furthermore, the simulation results show that DQFCNet has significant improvement in convergence speed and stability.