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,
as well as January and February 2021, among all articles published in the IEEE Transactions on Communications
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)
Department of Electrical and Computer Engineering,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
School of Electrical, Computer and Energy Engineering,
Arizona State University,
Tempe, AZ 85287 USA
Published on IEEE Explore
Paper draft on arXiv
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
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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