Online Power Control for 5G Wireless Communications: A Deep Q-Network Approach

The popularity of smart mobile devices has resulted in the surged growth of mobile data traffic, which makes current cellular communication systems overloaded. To accommodate the data, the current wireless communication system is evolving to a 5G wireless communication system that employs multiple technologies to boost its system capacity. We notice that non-line-of-sight (NLOS) transmission is ubiquitous in wireless communication systems, and is even more common in 5G wireless communication systems due to using millimeter-Wave (mmWave) communications. Previous works employ beamforming techniques to enhance NLOS transmission performance but suffer from the high cost for controlling antennas. In this paper, we propose a dynamic transmission power control scheme for improving NLOS transmission performance. Particularly, we explore the control of UE association with MBS/SBSs and power allocation to maximize UEs' sum-rate under the constraints of transmission power and UEs' quality of service (QoS). To solve this maximization problem, we propose a deep Q- network (DQN) scheme, in which we apply a convolutional neural network (CNN) to estimate the Q-function offline and conduct a deep Q-learning online to find the control strategy. We offer simulation results to show the efficacy of the proposed scheme.