A Framework for Automated Cellular Network Tuning with Reinforcement Learning

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 signal to interference-plus-noise ratio (SINR) for indoor and outdoor environments. By leveraging the ability of Q-learning to estimate future SINR improvement rewards, we propose two algorithms: (1) voice over LTE (VoLTE) downlink closed loop power control (PC) 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 effective SINR meets the target SINR. The SON fault management algorithm uses RL to improve the performance of an outdoor 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 SINR optimization problem using RL. Simulation results show that our proposed RL based algorithms outperform the industry standards today in realistic cellular communication environments.