  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.
