Journal
IEEE INTERNET OF THINGS JOURNAL
Volume 6, Issue 6, Pages 10191-10204Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2936580
Keywords
Channel state information (CSI); generative adversarial networks (GANs); human activity recognition; Internet of Things (IoT); WiFi
Categories
Funding
- National Natural Science Foundation of China [61402151, 61806074]
- Science and Technology Foundation of Henan Province of China [182102210238, 162102410010]
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As a cornerstone service for many Internet of Things applications, channel state information (CSI)-based activity recognition has received immense attention over recent years. However, recognition performance of general approaches might significantly decrease when applying the trained model to the left-out user whose CSI data are not used for model training. To overcome this challenge, we propose a semi-supervised generative adversarial network (GAN) for CSI-based activity recognition (CsiGAN). Based on the general semi-supervised GANs, we mainly design three components for CsiGAN to meet the scenarios that unlabeled data from left-out users are very limited and enhance recognition performance: 1) we introduce a new complement generator, which can use limited unlabeled data to produce diverse fake samples for training a robust discriminator; 2) for the discriminator, we change the number of probability outputs from k + 1 into 2k + 1 (here, k is the number of categories), which can help to obtain the correct decision boundary for each category; and 3) based on the introduced generator, we propose a manifold regularization, which can stabilize the learning process. The experiments suggest that CsiGAN attains significant gains compared to the state-of-the-art methods.
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