CSI2Image: Image Reconstruction from Channel State Information Using Generative Adversarial Networks

TL;DR

This paper introduces CSI2Image, a GAN-based method for converting wireless channel state information into images to assess physical space data, demonstrating effectiveness in simple and complex sensing scenarios.

Contribution

The paper presents a novel CSI-to-image conversion technique using GANs and a new evaluation methodology for assessing reconstructed physical space information.

Findings

GANs improve image reconstruction in complex sensing tasks

Generator-only learning suffices for simple problems

Proposed evaluation method effectively measures physical information capture

Abstract

This study aims to find the upper limit of the wireless sensing capability of acquiring physical space information. This is a challenging objective, because at present, wireless sensing studies continue to succeed in acquiring novel phenomena. Thus, although a complete answer cannot be obtained yet, a step is taken towards it here. To achieve this, CSI2Image, a novel channel-state-information (CSI)-to-image conversion method based on generative adversarial networks (GANs), is proposed. The type of physical information acquired using wireless sensing can be estimated by checking wheth\-er the reconstructed image captures the desired physical space information. Three types of learning methods are demonstrated: gen\-er\-a\-tor-only learning, GAN-only learning, and hybrid learning. Evaluating the performance of CSI2Image is difficult, because both the clarity of the image and the presence of the desired physical space information must be evaluated. To solve this problem, a quantitative evaluation methodology using an object detection library is also proposed. CSI2Image was implemented using IEEE 802.11ac compressed CSI, and the evaluation results show that the image was successfully reconstructed. The results demonstrate that gen\-er\-a\-tor-only learning is sufficient for simple wireless sensing problems, but in complex wireless sensing problems, GANs are important for reconstructing generalized images with more accurate physical space information.