Exploring the Potential of Spiking Neural Networks in UWB Channel Estimation

• URL: http://arxiv.org/abs/2512.23975v1
• Date: Tue, 30 Dec 2025 04:10:18 GMT
• Title: Exploring the Potential of Spiking Neural Networks in UWB Channel Estimation
• Authors: Youdong Zhang, Xu He, Xiaolin Meng,
• Abstract summary: This letter explores the potential of Spiking Neural Networks (SNNs) for channel estimation.<n>We develop a fully unsupervised SNN solution that attains 80% test accuracy.<n>Compared with complex deep learning methods, our SNN implementation is inherently suited to neuromorphic deployment.
• Score: 7.52520480528178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although existing deep learning-based Ultra-Wide Band (UWB) channel estimation methods achieve high accuracy, their computational intensity clashes sharply with the resource constraints of low-cost edge devices. Motivated by this, this letter explores the potential of Spiking Neural Networks (SNNs) for this task and develops a fully unsupervised SNN solution. To enable a comprehensive performance analysis, we devise an extensive set of comparative strategies and evaluate them on a compelling public benchmark. Experimental results show that our unsupervised approach still attains 80% test accuracy, on par with several supervised deep learning-based strategies. Moreover, compared with complex deep learning methods, our SNN implementation is inherently suited to neuromorphic deployment and offers a drastic reduction in model complexity, bringing significant advantages for future neuromorphic practice.

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