Due to sophisticated deployments of all kinds of wireless networks (e.g., 5G, Wi-Fi, Bluetooth, LEO satellite, etc.), multiband signals distribute in a large bandwidth (e.g., from 70 MHz to 8 GHz). Consequently, for network monitoring and spectrum sharing applications, a sniffer for extracting physical layer information, such as structure of packet, with low sampling rate (especially, sub-Nyquist sampling) can significantly improve their cost- and energy-efficiency. However, to achieve a multiband signals sniffer is really a challenge. To this end, we propose Sums, a system that can sniff and analyze multiband signals in a blind manner. Our Sums takes advantage of hardware and algorithm co-design, multi-coset sub-Nyquist sampling hardware, and a multi-task deep learning framework. The hardware component breaks the Nyquist rule to sample GHz bandwidth, but only pays for a 50 MSPS sampling rate. Our multi-task learning framework directly tackles the sampling data to perform spectrum sensing, physical layer protocol recognition, and demodulation for deep inspection from multiband signals. Extensive experiments demonstrate that Sums achieves higher accuracy than the state-of-theart baselines in spectrum sensing, modulation classification, and demodulation. As a result, our Sums can help researchers and end-users to diagnose or troubleshoot their problems of wireless infrastructures deployments in practice.

翻译：由于各类无线网络（如5G、Wi-Fi、蓝牙、低轨卫星等）的复杂部署，多频带信号分布在极宽带宽内（例如70 MHz至8 GHz）。因此，在网络监测与频谱共享应用中，能够以低采样率（特别是亚奈奎斯特采样）提取物理层信息（如数据包结构）的嗅探器，可显著提升其成本效益与能源效率。然而，实现多频带信号嗅探仍面临巨大挑战。为此，我们提出Sums系统，该系统能够以盲方式嗅探并分析多频带信号。Sums采用硬件与算法协同设计，结合多通道余弦亚奈奎斯特采样硬件及多任务深度学习框架。硬件组件突破奈奎斯特限制对GHz级带宽进行采样，仅需50 MSPS的采样率。我们的多任务学习框架直接处理采样数据，实现频谱感知、物理层协议识别及解调，从而对多频带信号进行深度检测。大量实验表明，Sums在频谱感知、调制分类与解调任务中均优于当前最先进的基线方法。因此，Sums可帮助研究人员与终端用户在实践中诊断及排查无线基础设施部署中的问题。