用脉冲神经网络实现低功耗超宽带信道估计
Exploring the Potential of Spiking Neural Networks in UWB Channel Estimation
- 提出无监督脉冲神经网络方法解决资源受限设备的信道估计问题
- 测试准确率达80%，媲美多种有监督深度学习模型
- 模型复杂度大幅降低，适合神经形态芯片部署
尽管现有的基于深度学习的超宽带（UWB）信道估计方法精度很高，但其计算开销与低成本边缘设备的资源限制严重冲突。为此，本文探索脉冲神经网络（SNN）在该任务中的潜力，并提出一种完全无监督的SNN解决方案。为实现全面性能评估，设计了多组对比策略，并在公开基准上进行测试。实验结果表明，该无监督方法仍能达到80%的测试准确率，与若干有监督深度学习方法相当。此外，相比复杂深度学习模型，SNN实现更天然适配神经形态硬件部署，显著降低模型复杂度，对未来的神经形态应用具有重要意义。
原文摘要 · Abstract (English)
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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