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.

翻译：近年来，联合通信与感知（JC&S）技术取得了显著成功，特别是在利用商用现成（COTS）Wi-Fi设备的sub-6 GHz频段进行定位、手势识别和姿态分类等应用方面。深度学习及大型公开数据集的存在对于实现这些成果起到了关键作用。然而，在毫米波频段（30-300 GHz）——该频段已显示出实现更高精度感知性能的潜力——针对COTS Wi-Fi感知领域的研究明显不足。研究硬件受限、缺乏大型数据集、COTS硬件功能有限以及数据采集的复杂性等挑战，阻碍了对该领域的全面探索。本研究旨在通过开发一种能够生成合成毫米波信道状态信息（CSI）样本的方法来解决这些挑战。具体而言，我们在现有数据集上使用生成对抗网络（GAN）生成了30,000个额外的CSI样本。增强样本与原始数据表现出高度一致性，这一点通过极高的GAN训练分数和GAN测试分数得以体现。此外，我们将增强样本集成到姿态分类模型的训练中。实验表明，增强样本能够有效补充真实数据，并提升分类模型的泛化能力。