IEEE Transactions on Communications, submitted June 28, 2019, and resubmitted Sept. 22, 2019.

Deep Reinforcement Learning for 5G Networks: Joint Beamforming, Power Control, and Interference Coordination

Faris B. Mismar (1), Brian L. Evans (1), and Ahmed Alkhateeb (2)

(1) Department of Electrical and Computer Engineering, Wireless Networking and Communications Group, The University of Texas at Austin, Austin, TX 78712 USA -

(2) School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287 USA

Paper Draft (on arXiv)

Software Release


The fifth generation of wireless communications (5G) promises massive increases in traffic volume and data rates, as well as improved reliability in voice calls. Jointly optimizing beamforming, power control, and interference coordination in a 5G wireless network to enhance the communication performance to end users poses a significant challenge. In this paper, we formulate the joint design of beamforming, power control, and interference coordination as a non-convex optimization problem to maximize the signal to interference plus noise ratio (SINR) and solve this problem using deep reinforcement learning. By using the greedy nature of deep Q-learning to estimate future rewards of actions and using the reported coordinates of the users served by the network, we propose an algorithm for voice bearers and data bearers in sub-6 GHz and millimeter wave (mmWave) frequency bands, respectively. The algorithm improves the performance measured by SINR and sum-rate capacity. The proposed algorithm does not require the knowledge of the channel state information. In realistic cellular environments, the simulation results show that our algorithm outperforms the link adaptation industry standards for sub-6 GHz voice bearers. For data bearers over a mmWave band, our algorithm approaches the maximum sum rate capacity, but yet scales linearly in runtime complexity in the number of antennas and base stations.

COPYRIGHT NOTICE: All the documents on this server have been submitted by their authors to scholarly journals or conferences as indicated, for the purpose of non-commercial dissemination of scientific work. The manuscripts are put on-line to facilitate this purpose. These manuscripts are copyrighted by the authors or the journals in which they were published. You may copy a manuscript for scholarly, non-commercial purposes, such as research or instruction, provided that you agree to respect these copyrights.

Last Updated 09/22/19.