ISAC enables pervasive monitoring, but modern sensing algorithms are often too complex for energy-constrained edge devices. This motivates the development of learning techniques that balance accuracy performance and energy efficiency. Spiking Neural Networks (SNNs) are a promising alternative, processing information as sparse binary spike trains and potentially reducing energy consumption by orders of magnitude. In this work, we propose a spiking convolutional autoencoder (SCAE) that learns tailored spike-encoded representations of channel impulse responses (CIR), jointly trained with an SNN for human activity recognition (HAR), thereby eliminating the need for Doppler domain preprocessing. The results show that our SCAE-SNN achieves F1 scores comparable to a hybrid approach (almost 96%), while producing substantially sparser spike encoding (81.1% sparsity). We also show that encoding CIR data prior to classification improves both HAR accuracy and efficiency. The code is available at https://github.com/ele-ciccia/SCAE-SNN-HAR.

翻译：ISAC技术实现了普适性监测，但现代感知算法通常过于复杂，难以在能量受限的边缘设备上部署。这促使了平衡精度性能与能效的学习技术的发展。脉冲神经网络（SNNs）作为一种有前景的替代方案，通过处理稀疏二进制脉冲序列来处理信息，并可能将能耗降低数个数量级。在本研究中，我们提出了一种脉冲卷积自编码器（SCAE），该模型学习通道冲激响应（CIR）的定制化脉冲编码表示，并与用于人体活动识别（HAR）的SNN联合训练，从而消除了对多普勒域预处理的需求。实验结果表明，我们的SCAE-SNN在实现与混合方法相当的F1分数（接近96%）的同时，生成了显著更稀疏的脉冲编码（81.1%的稀疏度）。我们还证明了在分类之前对CIR数据进行编码能够同时提升HAR的准确性和效率。相关代码可在https://github.com/ele-ciccia/SCAE-SNN-HAR获取。