Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization

• URL: http://arxiv.org/abs/2008.07111v1
• Date: Mon, 17 Aug 2020 06:32:13 GMT
• Title: Semi-Supervised Learning with GANs for Device-Free Fingerprinting Indoor Localization
• Authors: Kevin M. Chen and Ronald Y. Chang
• Abstract summary: Device-free wireless indoor localization is a key enabling technology for the Internet of Things (IoT) This paper proposes a semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system.
• Score: 6.939464860621602
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Device-free wireless indoor localization is a key enabling technology for the Internet of Things (IoT). Fingerprint-based indoor localization techniques are a commonly used solution. This paper proposes a semi-supervised, generative adversarial network (GAN)-based device-free fingerprinting indoor localization system. The proposed system uses a small amount of labeled data and a large amount of unlabeled data (i.e., semi-supervised), thus considerably reducing the expensive data labeling effort. Experimental results show that, as compared to the state-of-the-art supervised scheme, the proposed semi-supervised system achieves comparable performance with equal, sufficient amount of labeled data, and significantly superior performance with equal, highly limited amount of labeled data. Besides, the proposed semi-supervised system retains its performance over a broad range of the amount of labeled data. The interactions between the generator, discriminator, and classifier models of the proposed GAN-based system are visually examined and discussed. A mathematical description of the proposed system is also presented.

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