  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.
