Asilomar Conference on Signals, Systems and Computers,
Pacific Grove, California USA, Oct. 28-31, 2018
Q-Learning Algorithm for VoLTE Closed Loop Power Control in Indoor Small Cells
Faris B. Mismar 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
faris.mismar@utexas.edu -
bevans@ece.utexas.edu
Final Paper (Archive) -
Final Paper (Local) -
Poster (PowerPoint) -
Poster (PDF) -
Software Release
Abstract
We propose a reinforcement learning (RL) based closed loop power control
algorithm for the downlink of the voice over LTE (VoLTE) radio bearer
for an indoor environment served by small cells.
The main contributions of our paper are to
- use RL to solve performance tuning problems in an indoor cellular
network for voice bearers and
- show that our derived lower bound loss in effective SINR is sufficient
for VoLTE power control purposes in practical cellular networks.
In simulation, the proposed RL-based power control algorithm significantly
improves both voice retainability and mean opinion score compared to current
industry standards.
The improvement is due to maintaining an effective downlink signal to
interference plus noise ratio against adverse network operational issues
and faults.
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Last Updated 11/07/18.