  In this paper, we address inter-beam inter-cell interference mitigation in 5G
networks that employ millimeter-wave (mmWave), beamforming and non-orthogonal
multiple access (NOMA) techniques. Those techniques play a key role in
improving network capacity and spectral efficiency by multiplexing users on
both spatial and power domains. In addition, the coverage area of multiple
beams from different cells can intersect, allowing more flexibility in
user-cell association. However, the intersection of coverage areas also implies
increased inter-beam inter-cell interference, i.e. interference among beams
formed by nearby cells. Therefore, joint user-cell association and inter-beam
power allocation stand as a promising solution to mitigate inter-beam,
inter-cell interference. In this paper, we consider a 5G mmWave network and
propose a reinforcement learning algorithm to perform joint user-cell
association and inter-beam power allocation to maximize the sum rate of the
network. The proposed algorithm is compared to a uniform power allocation that
equally divides power among beams per cell. Simulation results present a
performance enhancement of 13-30% in network's sum-rate corresponding to the
lowest and highest traffic loads, respectively.
