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

TL;DR

This paper introduces a deep reinforcement learning method to dynamically adapt UWB radio settings for improved reliability and energy efficiency in indoor environments, outperforming traditional methods.

Contribution

The paper presents a novel deep Q-learning approach for real-time PHY setting adaptation in UWB radios, enhancing communication reliability and energy efficiency.

Findings

Deep Q-learning outperforms traditional Q-learning and fixed settings.

Achieves higher packet reception rate (PRR) and lower ranging error.

Reduces energy consumption to 14% of fixed PHY in dynamic environments.

Abstract

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.