Exploring the Potential of Spiking Neural Networks in UWB NLOS Identification
Abstract
Ultra-Wideband (UWB) Non-Line-of-Sight (NLOS) identification is critical for accurate localization, yet prevailing deep learning solutions impose computational demands incompatible with low-cost edge devices. This letter explores the potential of Spiking Neural Networks (SNNs) for this task and develops a fully unsupervised SNN solution. Against supervised deep learning baselines, our approach achieves comparable accuracy (~82%) with only 4.7K synaptic operations per inference, representing a three-order-of-magnitude improvement in efficiency. These results establish that SNNs can serve as a viable neuromorphic alternative for edge UWB applications.
Fields of Study & Subjects
Ultra-Wideband Communications Technology
Wireless Signal Modulation Classification
Indoor and Outdoor Localization Technologies
Publication details
- Journal / source
- IEEE Internet of Things Journal
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 13
- Issue
- 14
- Pages
- 32451–32454
- Publication date
- 8 June 2026