  Deep learning has significantly advanced wireless sensing technology by
leveraging substantial amounts of high-quality training data. However,
collecting wireless sensing data encounters diverse challenges, including
unavoidable data noise, limited data scale due to significant collection
overhead, and the necessity to reacquire data in new environments. Taking
inspiration from the achievements of AI-generated content, this paper
introduces a signal generation method that achieves data denoising,
augmentation, and synthesis by disentangling distinct attributes within the
signal, such as individual and environment. The approach encompasses two
pivotal modules: structured signal selection and signal disentanglement
generation. Structured signal selection establishes a minimal signal set with
the target attributes for subsequent attribute disentanglement. Signal
disentanglement generation disentangles the target attributes and reassembles
them to generate novel signals. Extensive experimental results demonstrate that
the proposed method can generate data that closely resembles real-world data on
two wireless sensing datasets, exhibiting state-of-the-art performance. Our
approach presents a robust framework for comprehending and manipulating
attribute-specific information in wireless sensing.
