Selective Unsupervised Learning-Based Wi-Fi Fingerprint System Using Autoencoder and GAN

Abstract

In this article, we propose an automatic Wi-Fi fingerprint system that combines an unsupervised dual radio mapping (UDRM) algorithm with the aim of reducing the time-cost needed to acquire Wi-Fi signals. Our proposed system is appropriate for indoor environments and utilizes a minimum description length principle (MDLP)-based radio map feedback (RMF) algorithm that simultaneously optimizes and updates the radio map. In the training phase, the proposed UDRM algorithm generates a radio map of the entire building based on the measured radio map of one reference floor. It does this by selectively applying a modified autoencoder and a generative adversarial network according to the spatial structures. Our proposed learning-based UDRM algorithm does not require labeled data, which is essential for supervised and semisupervised learning algorithms. It has a relatively low dependence on received signal strength indicator (RSSI) data sets. Our proposed RMF algorithm analyzes the distribution characteristics of the RSSIs for newly measured access points (APs) and feeds the analyzed results back to the radio map. The MDLP, which is applied to the proposed algorithm, improves the positioning performance and optimizes the size of the radio map by preventing the indefinite updating of the RSSI and by updating the newly added APs in the radio map.