TR2022-044

Quantum Transfer Learning for Wi-Fi Sensing


Abstract:

Beyond data communications, commercial-off-theshelf Wi-Fi devices can be used to monitor human activities, track device locomotion, and sense the ambient environment. In particular, spatial beam attributes that are inherently available in the 60-GHz IEEE 802.11ad/ay standards have shown to be effective in terms of overhead and channel measurement granularity for these indoor sensing tasks. In this paper, we investigate transfer learning to mitigate domain shift in human monitoring tasks when Wi-Fi settings and environments change over time. As a proof-of-concept study, we consider quantum neural networks (QNN) as well as classical deep neural networks
(DNN) for the future quantum-ready society. The effectiveness of both DNN and QNN is validated by an in-house experiment for human pose recognition, achieving greater than 90% accuracy with a limited data size.

 

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      Date: November 28, 2023 - November 30, 2023
      Where: Virtual
      MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
      Brief
      • On November 28, 2023, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang will give a 3-hour tutorial presentation at the first IEEE Virtual Conference on Communications (VCC). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addresses recent trends, challenges, and advances in sensing and communications. P. Wang presents use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discusses the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial is conducted virtually.

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      MERL Contacts: Toshiaki Koike-Akino; Pu (Perry) Wang
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Machine Learning, Signal Processing
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      • On December 8, 2022, MERL researchers Toshiaki Koike-Akino and Pu (Perry) Wang gave a 3.5-hour tutorial presentation at the IEEE Global Communications Conference (GLOBECOM). The talk, titled "Post-Deep Learning Era: Emerging Quantum Machine Learning for Sensing and Communications," addressed recent trends, challenges, and advances in sensing and communications. P. Wang presented on use cases, industry trends, signal processing, and deep learning for Wi-Fi integrated sensing and communications (ISAC), while T. Koike-Akino discussed the future of deep learning, giving a comprehensive overview of artificial intelligence (AI) technologies, natural computing, emerging quantum AI, and their diverse applications. The tutorial was conducted remotely. MERL's quantum AI technology was partly reported in the recent press release (https://us.mitsubishielectric.com/en/news/releases/global/2022/1202-a/index.html).

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  • Related Video

  • Related Publication

  •  Koike-Akino, T., Pu, W., Wang, Y., "Quantum Transfer Learning for Wi-Fi Sensing", arXiv, May 2022.
    BibTeX arXiv
    • @article{Koike-Akino2022may,
    • author = {Koike-Akino, Toshiaki and Pu, Wang and Wang, Ye},
    • title = {Quantum Transfer Learning for Wi-Fi Sensing},
    • journal = {arXiv},
    • year = 2022,
    • month = may,
    • url = {https://arxiv.org/abs/2205.08590}
    • }