NSDI '23 – SLNet: A Spectrogram Learning Neural Network for Deep Wireless Sensing

SLNet: A Spectrogram Learning Neural Network for Deep Wireless Sensing

Zheng Yang and Yi Zhang, Tsinghua University; Kun Qian, University of California San Diego; Chenshu Wu, The University of Hong Kong

Advances in wireless technologies have transformed wireless networks from a pure communication medium to a pervasive sensing platform, enabling many sensorless and contactless applications. After years of effort, wireless sensing approaches centering around conventional signal processing are approaching their limits, and meanwhile, deep learning-based methods become increasingly popular and have seen remarkable progress. In this paper, we explore an unseen opportunity to push the limit of wireless sensing by jointly employing learning-based spectrogram generation and spectrogram learning. To this end, we present SLNet, a new deep wireless sensing architecture with spectrogram analysis and deep learning co-design. SLNet employs neural networks to generate super-resolution spectrogram, which overcomes the limitation of the time-frequency uncertainty. It then utilizes a novel polarized convolutional network that modulates the phase of the spectrograms for learning both local and global features. Experiments with four applications, i.e., gesture recognition, human identification, fall detection, and breathing estimation, show that SLNet achieves the highest accuracy with the smallest model and lowest computation among the state-of-the-art models. We believe the techniques in SLNet can be widely applied to fields beyond WiFi sensing.

View the full NSDI ’23 program at https://www.usenix.org/conference/nsdi23/technical-sessions .