RF PIX2PIX Unsupervised Wi-Fi to Video Translation

TL;DR

This paper introduces an unsupervised GAN-based method that reconstructs visual scene information from Wi-Fi signals, eliminating the need for labeled data and surpassing previous approaches in accuracy.

Contribution

It presents a novel unsupervised network based on PIX2PIX GAN architecture that maps Wi-Fi channel information to visual scenes without labeled training data.

Findings

Successfully reconstructs scene images from Wi-Fi signals

Outperforms previous methods requiring object labeling

Demonstrates robust mapping from RF signals to visual data

Abstract

With the proliferation of Wi-Fi devices in the environment, our surroundings are increasingly illuminated with low-level RF scatter. This scatter illuminates objects in the environment much like radar or LIDAR. We show that a novel unsupervised network, based on the PIX2PIX GAN architecture, can recover and visually reconstruct scene information solely from Wi-Fi background energy; in contrast to a significantly less accurate approach by Kefayati (et. all) which requires careful object labeling to recover object location from a scene. This is accomplished by learning a more robust mapping function between the channel state information (CSI) from Wi-Fi packets and Video image sample distributions.