CSI4Free: GAN-Augmented mmWave CSI for Improved Pose Classification

This refers to the gan-generated dataset for the paper" CSI4Free: GAN-Augmented mmWave CSI forImproved Pose Classification. 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. The repository is available here: https://github.com/nisarnabeel/Dataset-GAN-Augmented-mmWave-CSI-for-improved-pose-classification Paper Link:https://ieeexplore.ieee.org/document/10646223/本数据集对应论文《CSI4Free：用于改进姿态分类的GAN增强毫米波信道状态信息》。 摘要：近年来，通信与感知一体化（Joint Communication and Sensing, JC&S）技术已取得显著进展，尤其在利用商用现货（Commercial-off-the-shelf, COTS）Wi-Fi设备的sub-6 GHz频段实现定位、手势识别及姿态分类等应用方面成果斐然。深度学习与大规模公开数据集的存在是达成此类成果的关键支撑。然而，在毫米波频段（30-300 GHz）——该频段已展现出更精准的感知性能潜力——商用现货Wi-Fi感知领域的研究却明显匮乏。诸如可用研究硬件有限、大规模数据集缺失、商用现货硬件功能受限以及数据采集复杂度高等诸多挑战，严重阻碍了该领域的全面探索。本研究旨在通过开发一种可生成合成毫米波信道状态信息（Channel State Information, CSI）样本的方法，解决上述痛点。具体而言，我们基于现有数据集，利用生成对抗网络（Generative Adversarial Network, GAN）生成了30000条额外的CSI样本。实验结果表明，增强后的样本与原始数据具有极高的一致性，从GAN训练与GAN测试的高分值中可得到充分印证。此外，我们将增强样本融入姿态分类模型的训练流程，观察发现增强样本可有效补充真实数据，显著提升分类模型的泛化能力。 本数据集开源仓库地址：https://github.com/nisarnabeel/Dataset-GAN-Augmented-mmWave-CSI-for-improved-pose-classification 论文链接：https://ieeexplore.ieee.org/document/10646223/