Learning-Based WiFi Fingerprint Inpainting via Generative Adversarial Networks

TL;DR

This paper introduces a novel generative adversarial network approach for WiFi fingerprint inpainting, addressing unique challenges of irregular data shapes and distribution, to improve indoor positioning accuracy.

Contribution

It presents a new inpainting model tailored for WiFi signals, capturing inter-AP and intra-AP correlations, and distinguishes this problem from traditional image inpainting.

Findings

Effective inpainting of WiFi fingerprints with irregular shapes

Preserves spatial and channel correlations in WiFi data

Enhances indoor positioning accuracy

Abstract

WiFi-based indoor positioning has been extensively studied. A fundamental issue in such solutions is the collection of WiFi fingerprints. However, due to real-world constraints, collecting complete fingerprints at all intended locations is sometimes prohibited. This work considers the WiFi fingerprint inpainting problem. This problem differs from typical image/video inpainting problems in several aspects. Unlike RGB images, WiFi field maps come in any shape, and signal data may follow certain distributions. Therefore, it is difficult to forcefully fit them into a fixed-dimensional matrix, as done with processing images in RGB format. As soon as a map is changed, it also becomes difficult to adapt it to the same model due to scale issues. Furthermore, such models are significantly constrained in situations requiring outward inpainting. Fortunately, the spatial relationships of WiFi signals and the rich information provided among channels offer ample opportunities for this generative model to accomplish inpainting. Therefore, we designed this model to not only retain the characteristic of regression models in generating fingerprints of arbitrary shapes but also to accommodate the observational outcomes from densely deployed APs. This work makes two major contributions. Firstly, we delineate the distinctions between this problem and image inpainting, highlighting potential avenues for research. Secondly, we introduce novel generative inpainting models aimed at capturing both inter-AP and intra-AP correlations while preserving latent information. Additionally, we incorporate a specially designed adversarial discriminator to enhance the quality of inpainting outcomes.