A Deep Reinforcement Learning-Based Technique for Optimal Power Allocation in Multiple Access Communications

Abstract

For many years, researchers have explored power allocation (PA) algorithms driven by models in wireless networks where multiple-user communications with interference are present. Nowadays, data-driven machine learning methods have become quite popular in analyzing wireless communication systems, which among them deep reinforcement learning (DRL) has a significant role in solving optimization issues under certain constraints. To this purpose, in this paper, we investigate the PA problem in a k-user multiple access channels (MAC), where k transmitters (e.g., mobile users) aim to send an independent message to a common receiver (e.g., base station) through wireless channels. To this end, we first train the deep Q network (DQN) with a deep Q learning (DQL) algorithm over the simulation environment, utilizing offline learning. Then, the DQN will be used with the real data in the online training method for the PA issue by maximizing the sumrate subjected to the source power. Finally, the simulation results indicate that our proposed DQN method provides better performance in terms of the sumrate compared with the available DQL training approaches such as fractional programming (FP) and weighted minimum mean squared error (WMMSE). Additionally, by considering different user densities, we show that our proposed DQN outperforms benchmark algorithms, thereby, a good generalization ability is verified over wireless multi-user communication systems.