Jingyu Tong
ABSTRACT
Wireless sensing has attracted considerable attention because it can sense the state of the targets by analyzing the surrounding wireless signals, which has become the key role of the artificial intelligence of things (AIoT). As the number of sensory nodes increases, large amounts of redundant data are exchanged between sensory terminals and the AI cloud. To process such large amounts of data efficiently and decrease power consumption, a machine-learning approach that operates close to or inside sensors must be developed. To this end, we present the radio-frequency neural network (RFNN), a physical neural network taking advantage of a group of transmissive intelligent surfaces (i.e., metasurfaces) to mimic the computations of a fully-connected neural network. The design is spurred by the capability of RFNNs to perform expensive multiplication and additions at the speed of light, with ultra-low power consumption. We prototype RFNN at 5 GHz for WiFi sensing regarding nine wireless sensing tasks. Extensive evaluations demonstrate the comparably equivalent inference ability as the conventional electronic neural networks while consuming less energy.
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Cross Ref
- R. Ayyalasomayajula, A. Arun, C. Wu, S. Sharma, A. R. Sethi, D. Vasisht, and D. Bharadia, "Deep learning based wireless localization for indoor navigation," in Proc. of ACM MobiCom, 2020.Google Scholar
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- U. Ha, S. Assana, and F. Adib, "Contactless seismocardiography via deep learning radars," in Proc. of ACM MobiCom, 2020.Google Scholar
- C. Jiang, J. Guo, Y. He, M. Jin, S. Li, and Y. Liu, "mmvib: Micrometer-level vibration measurement with mmwave radar," in Proc. of ACM MobiCom, 2020.Google ScholarDigital Library
- VREMSoftwareDevelopment, "WiFi Analyzer," https://github.com/VREMSoftwareDevelopment/WiFiAnalyzer, 2023, accessed: 2023-03-09.Google Scholar
Index Terms
- Radio Frequency Neural Networks for Wireless Sensing
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