SWIRLS: Sniffing Wi-Fi Using Radios with Low Sampling Rates

Next-generation Wi-Fi systems embrace large signal bandwidth to achieve significantly improved data rates, while requiring efficient methods for network monitoring and spectrum sharing applications. A radio receiver (RX) operating at low sampling rates can largely improve the energy- and cost-efficiency in such systems if it can extract useful network information such as the duration and structure of wireless packets. In this paper, we present the design of SWIRLS, a novel framework for sniffing Wi-Fi Physical layer information using RXs operating at sampling rates that are (much) smaller than the signal bandwidth. SWIRLS consists of three modules tailored for low sampling rate RXs: joint packet detection, optimized RX frequency selection, and packet property decoder. We implement SWIRLS using three software-defined radio platforms and extensively evaluate SWIRLS in real-world scenarios. The experiments show that for 20/40MHz 802.11n packets, SWIRLS with 5MHz sampling rate can achieve a mean absolute error (MAE) of transmission time and physical service data unit length decoding of 0.06 ms and 1.91 kB, respectively, at only 10 dB signal-to-noise ratio. With the same setting, SWIRLS simultaneously achieves a classification accuracy for the modulation and coding scheme, number of spatial streams, and bandwidth of 95.3%, 96.1%, and 95.6%, respectively. In an extreme case for 160 MHz 802.11ac/ax packets, SWIRLS with 2.5MHz sampling rate (i.e., a downsampling ratio of 64) can still achieve an MAE of transmission time decoding of 0.47/0.67 ms.