Q-Learning Algorithm for VoLTE Closed-Loop Power Control in Indoor Small Cells

TL;DR

This paper introduces a reinforcement learning-based closed-loop power control algorithm for indoor VoLTE small cells, significantly enhancing voice quality and reliability by maintaining optimal signal-to-interference ratios amidst network faults.

Contribution

It presents a novel RL approach for VoLTE power control in indoor small cells and demonstrates its effectiveness through simulation results.

Findings

Improved voice retainability in simulations.

Enhanced mean opinion score compared to industry standards.

Maintained effective SIR despite network faults.

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.