  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.
