  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.
