论文标题
Wi-Fi传感的量子传输学习
Quantum Transfer Learning for Wi-Fi Sensing
论文作者
论文摘要
除了数据通信之外，商业上的Wi-Fi设备还可以用于监视人类活动，跟踪设备的运动并感知环境环境。特别是，在60ghz IEEE 802.11AD/AY标准中固有可用的空间束属性已显示在这些室内传感任务的开销和通道测量粒度方面有效。在本文中，我们调查了转移学习，以减轻Wi-Fi设置和环境随时间变化时人类监视任务中的域转移。作为一项概念验证研究，我们考虑了未来量子就绪的社会的量子神经网络（QNN）以及经典的深神经网络（DNN）。 DNN和QNN的有效性通过人体姿势识别的内部实验验证，数据大小有限，其精度超过90％。
Beyond data communications, commercial-off-the-shelf 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.
