Title: Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells,
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 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 signal to interference plus noise ratio due to neighboring cell
failure is sufficient for VoLTE power control purposes in practical cellular
networks. In our 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.