Quantum Transfer Learning for Wi-Fi Sensing
This addresses domain adaptation for indoor sensing tasks using commercial Wi-Fi, but it is incremental as it applies existing methods to a new context.
The paper tackles domain shift in Wi-Fi sensing for human monitoring by investigating transfer learning with quantum and classical neural networks, achieving over 90% accuracy in human pose recognition with limited data.
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.