IEEE Transactions on Communications, vol. 68, no. 3, Mar. 2020, pp. 1581-1592, DOI 10.1109/TCOMM.2019.2961332.

One of the top 50 most accessed articles
in January, February, March, April, May, June, July, August, September, October, November, and December of 2020,
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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
faris.mismar@utexas.edu - bevans@ece.utexas.edu

(2) School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ 85287 USA
alkhateeb@asu.edu

Published on IEEE Explore
Paper draft on arXiv

Software Release

Abstract

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. 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 in the mmWave frequency band, our algorithm approaches the maximum sum rate capacity, but with less than 4% of the required run time.


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Last Updated 03/21/21.