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 interferenceplus-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. The tuning of network performance aims at providing the end user with excellent quality of experience (QoE). With over 1.5 billion smartphones used globally, demand patterns have The authors are with the Wireless Networking and Communications Group, Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA email: {faris.mismar, This paper is an expanded journal version of [1] and [2]. 2 Demands have shifted towards reliable packetized voice and applications with higher data rates and lower latencies [4].