  Tuning cellular network performance against always occurring wireless
impairments can dramatically improve reliability to end users. In this paper,
we formulate cellular network performance tuning as a reinforcement learning
(RL) problem and provide a solution to improve the performance for indoor and
outdoor environments. By leveraging the ability of Q-learning to estimate
future performance improvement rewards, we propose two algorithms: (1) closed
loop power control (PC) for downlink voice over LTE (VoLTE) and (2)
self-organizing network (SON) fault management. The VoLTE PC algorithm uses RL
to adjust the indoor base station transmit power so that the signal to
interference plus noise ratio (SINR) of a user equipment (UE) meets the target
SINR. It does so without the UE having to send power control requests. The SON
fault management algorithm uses RL to improve the performance of an outdoor
base station cluster by resolving faults in the network through configuration
management. Both algorithms exploit measurements from the connected users,
wireless impairments, and relevant configuration parameters to solve a
non-convex performance optimization problem using RL. Simulation results show
that our proposed RL based algorithms outperform the industry standards today
in realistic cellular communication environments.
