CSI4Free: GAN-Augmented mmWave CSI for Improved Pose Classification

TL;DR

This paper introduces a GAN-based method to generate synthetic mmWave CSI data, enhancing pose classification accuracy by augmenting limited real datasets, thus advancing COTS Wi-Fi sensing at higher frequencies.

Contribution

The work presents a novel GAN-based approach to synthesize large-scale mmWave CSI data, addressing hardware and data scarcity challenges in COTS Wi-Fi sensing.

Findings

Generated 30,000 synthetic CSI samples with high consistency.

Augmented data improved pose classification model generalization.

GAN-augmented training outperformed models trained on real data alone.

Abstract

In recent years, Joint Communication and Sensing (JC&S), has demonstrated significant success, particularly in utilizing sub-6 GHz frequencies with commercial-off-the-shelf (COTS) Wi-Fi devices for applications such as localization, gesture recognition, and pose classification. Deep learning and the existence of large public datasets has been pivotal in achieving such results. However, at mmWave frequencies (30-300 GHz), which has shown potential for more accurate sensing performance, there is a noticeable lack of research in the domain of COTS Wi-Fi sensing. Challenges such as limited research hardware, the absence of large datasets, limited functionality in COTS hardware, and the complexities of data collection present obstacles to a comprehensive exploration of this field. In this work, we aim to address these challenges by developing a method that can generate synthetic mmWave channel state information (CSI) samples. In particular, we use a generative adversarial network (GAN) on an existing dataset, to generate 30,000 additional CSI samples. The augmented samples exhibit a remarkable degree of consistency with the original data, as indicated by the notably high GAN-train and GAN-test scores. Furthermore, we integrate the augmented samples in training a pose classification model. We observe that the augmented samples complement the real data and improve the generalization of the classification model.