# Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings

arXiv: https://arxiv.org/abs/2210.15498 · HTML: https://mlsanity.com/paper/2210.15498/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 13 Oct 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015)
- [A Deep Q-Learning Method for Downlink Power Allocation in Multi-Cell Networks](https://mlsanity.com/paper/1904.13032/) (2019)
- [An Overview of Ultra-WideBand (UWB) Standards and Organizations (IEEE 802.15.4, FiRa, Apple): Interoperability Aspects and Future Research Directions](https://mlsanity.com/paper/2202.02190/) (2022)
- [Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework](https://mlsanity.com/paper/1802.06402/) (2018)
- [A Novel Sample-efficient Deep Reinforcement Learning with Episodic Policy Transfer for PID-Based Control in Cardiac Catheterization Robots](https://mlsanity.com/paper/2110.14941/) (2021)

## Abstract (arXiv)

Ultra-wideband technology has become increasingly popular for indoor localization and location-based services. This has led recent advances to be focused on reducing the ranging errors, whilst research focusing on enabling more reliable and energy efficient communication has been largely unexplored. The IEEE 802.15.4 UWB physical layer allows for several settings to be selected that influence the energy consumption, range, and reliability. Combined with the available link state diagnostics reported by UWB devices, there is an opportunity to dynamically select PHY settings based on the environment. To address this, we propose a deep Q-learning approach for enabling reliable UWB communication, maximizing packet reception rate (PRR) and minimizing energy consumption. Deep Q-learning is a good fit for this problem, as it is an inherently adaptive algorithm that responds to the environment. Validation in a realistic office environment showed that the algorithm outperforms traditional Q-learning, linear search and using a fixed PHY layer. We found that deep Q-learning achieves a higher average PRR and reduces the ranging error while using only 14% of the energy compared to a fixed PHY setting in a dynamic office environment.