IEEE Transactions on Communications,
vol. 67, no. 10, Oct. 2019, pp. 7152-7167, DOI 10.1109/TCOMM.2019.2926715.
A Framework for Automated Cellular Network Tuning with Reinforcement Learning
Faris B. Mismar,
Jinseok Choi and
Brian L. Evans
Department of Electrical and Computer Engineering,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
Self-Organizing Network Fault Management -
Voice Over LTE Downlink Closed Loop Power Control
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:
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.
- closed loop power control (PC) for downlink voice over LTE (VoLTE) and
- self-organizing network (SON) fault management.
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Last Updated 11/02/19.