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 -

Final Paper (Archive) - Final Paper (Local) - Poster (PowerPoint) - Poster (PDF) - Software Release


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
  1. use RL to solve performance tuning problems in an indoor cellular network for voice bearers and
  2. 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.