Joint DDPG and Unsupervised Learning for Channel Allocation and Power Control in Centralized Wireless Cellular Networks

Abstract

In order to solve the resource allocation problem in scenarios of centralized wireless cellular communication with multiple cells, users and channels, a novel resource allocation algorithm based on joint Deep Deterministic Policy Gradient (DDPG) reinforcement learning and unsupervised learning is proposed. Firstly, the proposed algorithm constructs a channel allocation deep neural network based on DDPG to provide an optimized channel allocation scheme. Secondly, the proposed algorithm constructs a power control deep neural network based on unsupervised learning to provide an optimized power control scheme. In order to make the unsupervised learning have perceptions on dynamic wireless environments, the double experience replay is executed to train the channel allocation deep neural network with the DDPG reinforcement learning and the power control deep neural network with the unsupervised learning, respectively. Since the proposed joint algorithm combines the dynamic perception ability of the DDPG reinforcement learning and the continuous optimization ability of unsupervised learning, the energy efficiency can be effectively maximized. Simulation results show that the proposed algorithm outperforms other algorithms in terms of energy efficiency and transmit rate in time-varying dynamic environments. Furthermore, we discuss the implications of our results and possible future research directions. Our work contributes to the advancement of resource allocation techniques in multi-cell cellular networks to meet the increasing demands of modern wireless communication systems.

Keywords

• Deep reinforcement learning
• DDPG
• unsupervised learning
• double experience replay
• channel allocation
• power control