DeepMon: Wi-Fi Monitoring Using Sub-Nyquist Sampling Rate Receivers with Deep Learning
Next-generation Wi-Fi networks employ large signal bandwidth to meet the demands of high data rates, which poses challenges to Wi-Fi monitoring systems that typically rely on a full sampling rate receiver (RX) to capture signals at full bandwidth for demodulation and decoding. Interestingly, preambles of Wi-Fi packets contain unencrypted information that can be decoded to extract Wi-Fi Physical (PHY) layer information such as modulation and coding scheme (MCS), transmission time, and PHY service data unit (PSDU) length. In this paper, we propose DeepMon, which leverages low-cost RXs operating at sub-Nyquist sampling rates and deep learning (DL) to identify the Wi-Fi protocol and decode PHY layer packet properties from the Wi-Fi preamble. To evaluate DeepMon, we use PlutoSDR as the low sampling rate RX to collect a dataset of over 390K real-world 802.11a/n/ac Wi-Fi packets for the DL model training and testing. Our experiments show that for Wi-Fi packets with up to 160 MHz bandwidth, an RX running DeepMon at 3 MHz sampling rate (i.e., a downsampling ratio of >50×) can achieve an average bit decoding accuracy of 96.20% for the legacy signal field, corresponding to a mean absolute error of only 0.077 ms for predicting the packet transmission time.
