Wireless Signal

Wireless signal research focuses on extracting information and improving functionality from radio frequency (RF) signals, aiming to enhance applications ranging from communication systems to sensing and imaging. Current research emphasizes data-driven approaches, employing deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative models (e.g., diffusion models, GANs) to address challenges such as interference rejection, signal classification, and efficient data augmentation. These advancements are significant for improving the accuracy and efficiency of wireless technologies across diverse fields, including medical imaging, indoor localization, and next-generation communication systems.