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

arXiv: https://arxiv.org/abs/2108.13157 · HTML: https://mlsanity.com/paper/2108.13157/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 44 references that are in the map.
- First posted on arXiv 24 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge](https://mlsanity.com/paper/2011.14684/) (2020)
- [Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization](https://mlsanity.com/paper/2004.04618/) (2020)

## Built on by (most cited first, 6 of 6)

- [Constructing Indoor Region-based Radio Map without Location Labels](https://mlsanity.com/paper/2308.16759/) (2023)
- [Multi-Timescale Ensemble Q-learning for Markov Decision Process Policy Optimization](https://mlsanity.com/paper/2402.05476/) (2024)
- [Leveraging Digital Cousins for Ensemble Q-Learning in Large-Scale Wireless Networks](https://mlsanity.com/paper/2402.08022/) (2024)
- [Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization](https://mlsanity.com/paper/2109.00126/) (2021)
- [JUNO: Jump-Start Reinforcement Learning-based Node Selection for UWB Indoor Localization](https://mlsanity.com/paper/2205.08422/) (2022)
- [Provable Performance Bounds for Digital Twin-driven Deep Reinforcement Learning in Wireless Networks: A Novel Digital-Twin Bisimulation Metric](https://mlsanity.com/paper/2502.17983/) (2025)

## Abstract (arXiv)

Recent advancements in Internet of Things (IoTs) have brought about a surge of interest in indoor positioning for the purpose of providing reliable, accurate, and energy-efficient indoor navigation/localization systems. Ultra Wide Band (UWB) technology has been emerged as a potential candidate to satisfy the aforementioned requirements. Although UWB technology can enhance the accuracy of indoor positioning due to the use of a wide-frequency spectrum, there are key challenges ahead for its efficient implementation. On the one hand, achieving high precision in positioning relies on the identification/mitigation Non Line of Sight (NLoS) links, leading to a significant increase in the complexity of the localization framework. On the other hand, UWB beacons have a limited battery life, 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.