{"ID":2823900,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.23975","arxiv_id":"2512.23975","title":"Exploring the Potential of Spiking Neural Networks in UWB Channel Estimation","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.","short_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 de...","url_abs":"https://arxiv.org/abs/2512.23975","url_pdf":"https://arxiv.org/pdf/2512.23975v1","authors":"[\"Youdong Zhang\",\"Xu He\",\"Xiaolin Meng\"]","published":"2025-12-30T04:10:18Z","proceeding":"cs.ET","tasks":"[\"cs.ET\",\"cs.LG\"]","methods":"[]","has_code":false}