DeepSpectrum: A Deep-Learning-based Spectrum Identification for Wireless Signals

DeepSpectrum: A Deep-Learning-based Spectrum Identification for Wireless Signals

연구 분야: Infrastructure

학회: 2023 19th International Conference on Mobility, Sensing and Networking (MSN)

With the deployment of Internet of Things, we have seen many wireless devices working in the crowded unlicensed 2.4GHz band, leading to severe cross-technology coexistence problem. The sensing of signal type and channel can assist wireless devices with more efficient transmission strategy to improve the network performance. In this paper, we present DeepSpectrum, a wireless signal identification system based on convolutional neural networks, to identify the type and estimate the channel of four wireless signals, including Wi-Fi, Zigbee, Lora and Bluetooth. DeepSpectrum consists of a master model for the initial identification of the wireless signals, and an auxiliary model to further improve the accuracy of the Lora channel estimation. We thoroughly evaluate DeepSpectrum using various test data at different signal-to-interference ratio (SIR) levels. Our evaluation results show that at 0\mathrm{~dB} SIR, the average identification accuracy for the four signals is over \mathbf{9 9 \%} and the average error of central frequency is only 21KHz, which indicates that we can get the channel of Wi-Fi, Zigbee, and Bluetooth correctly. As for Lora with flexible central frequencies, the average bandwidth error is only 76KHz.