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

arXiv: https://arxiv.org/abs/1707.03269 · HTML: https://mlsanity.com/paper/1707.03269/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2017.

## Built on by (most cited first, 3 of 3)

- [Transmit Power Control for Indoor Small Cells: A Method Based on Federated Reinforcement Learning](https://mlsanity.com/paper/2209.13536/) (2022)
- [A Machine Learning Based Algorithm for Joint Improvement of Power Control, link adaptation, and Capacity in Beyond 5G Communication systems](https://mlsanity.com/paper/2201.07090/) (2022)
- [Deep Reinforcement Learning Approach to QoSAware Load Balancing in 5G Cellular Networks under User Mobility and Observation Uncertainty](https://mlsanity.com/paper/2510.24869/) (2025)

## Abstract (arXiv)

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.