Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells
This addresses performance tuning for voice bearers in indoor cellular networks, but it is incremental as it applies an existing RL method to a specific domain.
The paper tackled power control for VoLTE in indoor small cells by proposing a reinforcement learning algorithm, which significantly improved voice retainability and mean opinion score compared to industry standards in simulations.
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.