  Recent advancements in wireless perception technologies, including mmWave,
WiFi, and acoustics, have expanded their application in human motion tracking
and health monitoring. They are promising alternatives to traditional
camera-based perception systems, thanks to their efficacy under diverse
conditions or occlusions, and enhanced privacy. However, the integration of
deep learning within this field introduces new challenges such as the need for
extensive training data and poor model generalization, especially with sparse
and noisy wireless point clouds. As a remedy, data augmentation is one solution
well-explored in other deep learning fields, but they are not directly
applicable to the unique characteristics of wireless signals. This motivates us
to propose a custom data augmentation framework, WixUp, tailored for wireless
perception. Moreover, we aim to make it a general framework supporting various
datasets, model architectures, sensing modalities, and tasks; while previous
wireless data augmentation or generative simulations do not exhibit this
generalizability, only limited to certain use cases. More specifically, WixUp
can reverse-transform lossy coordinates into dense range profiles using
Gaussian mixture and probability tricks, making it capable of in-depth data
diversity enhancement; and its mixing-based method enables unsupervised domain
adaptation via self-training, allowing training of the model with no labels
from new users or environments in practice. In summary, our extensive
evaluation experiments show that WixUp provides consistent performance
improvement across various scenarios and outperforms the baselines.
