Yi Gong
Southern University of Science and Technology, China
Yi Gong is currently a professor with the Southern University of Science and Technology, Shenzhen, China. His research interests include cellular networks, mobile computing, and intelligent signal processing for wireless communications and related applications. He was a recipient of the Best Paper Award of China Communications, in 2020 and the IEEE Jack Neubauer Memorial Award, in 2024. He was on the editorial Board of IEEE Transactions on Wireless Communications, and IEEE Transactions on Vehicular Technology.
Most wireless communication systems assume active cooperation between transmitters and receivers, where shared protocols enable receivers to easily synchronize, estimate channel conditions, and recover information. In non-cooperative settings, however, received signals need to be interpreted without such prior information. This challenge is critical for spectrum monitoring, wireless security, and cooperative networks operating amidst unfamiliar systems and interference. Accurately recognizing wireless signals is difficult as background noise, propagation effects, and interference often obscure essential feature characteristics. As the density of spectrum-sharing wireless systems grows, traditional methods struggle to remain effective, necessitating adaptive, data-driven approaches capable of learning directly from raw observations. In this talk, we demonstrate how one-dimensional wireless signals can be effectively transformed for robust recognition using deep learning models. Furthermore, we introduce advanced techniques designed to enhance recognition performance under realistic channel conditions. This work has also been extended to radio frequency fingerprinting, where we conducted extensive evaluations on numerous commercial wireless devices and achieved promising recognition performance.