WiFi-GEN: High-Resolution Indoor Imaging from WiFi Signals Using Generative AI

TL;DR

WiFi-GEN introduces a generative AI approach to convert WiFi signals into high-resolution indoor images, surpassing traditional methods in accuracy and quality, and provides a large dataset for this novel task.

Contribution

This work pioneers WiFi indoor imaging as a multi-modal image generation task using generative AI, achieving significant improvements over physical model-based methods.

Findings

Shape reconstruction accuracy increased by 275%.

Frechet Inception Distance score reduced by 82%.

Released a large-scale dataset with 80,000 WiFi-signal and image pairs.

Abstract

Indoor imaging is a critical task for robotics and internet-ofthings. WiFi as an omnipresent signal is a promising candidate for carrying out passive imaging and synchronizing the up-to-date information to all connected devices. This is the first research work to consider WiFi indoor imaging as a multi-modal image generation task that converts the measured WiFi power into a high-resolution indoor image. Our proposedWiFi-GEN network achieves a shape reconstruction accuracy that is 275% of that achieved by physical model-based inversion methods. Additionally, the Frechet Inception Distance score has been significantly reduced by 82%. To examine the effectiveness of models for this task, the first large-scale dataset is released containing 80,000 pairs of WiFi signal and imaging target. Our model absorbs challenges for the model-based methods including the nonlinearity, ill-posedness and non-certainty into massive parameters of our generative AI network. The network is also designed to best fit measured WiFi signals and the desired imaging output. Code: https://github.com/CNFightingSjy/WiFiGEN