DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection

Ultra Wide Band (UWB) has been emerged as a technology to provide reliable, accurate, and energy-efficient indoor navigation/localization systems. There are, however, several key challenges ahead for its efficient implementation including complexity of the identification/mitigation of Non Line of Sight (NLoS) links, and the limited battery life of UWB beacons, which is especially problematic in practical circumstances with certain beacons located in strategic positions. To address these challenges, we introduce an efficient node selection framework to enhance the location accuracy, without using complex NLoS mitigation methods, while maintaining a balance between the remaining battery life of UWB beacons. Referred to as the Deep Q-Learning Energy-optimized LoS/NLoS (DQLEL) UWB node selection framework, the mobile user is autonomously trained to determine the optimal set of UWB beacons to be localized based on the 2-D Time Difference of Arrival (TDoA) framework. The effectiveness of the proposed DQLEL framework is evaluated in terms of the link condition, the deviation of the remaining battery life of UWB beacons, location error, and cumulative rewards. Based on the simulation results, the proposed DQLEL framework significantly outperformed its counterparts across the aforementioned aspects.