Articles liés à Generative Learning for Wireless Communications

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Synopsis

Generative learning (GL) has emerged as an essential tool for data processing and network optimization in the broad area of next-generation communication systems. Generative Learning for Wireless Communications: Fundamentals and Applications provides a comprehensive and systematic tutorial for applying generative learning models to wireless communications. It explains the core concepts of state-of-the-art generative learning models, including generative adversarial nets, variational autoencoder, and other advanced models, such as transformers and diffusion models, and then shows their application to specific areas in wireless communications.

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À propos des auteurs

Dr. Songyang Zhang received the Ph.D. degree from the Department of Electrical and Computer Engineering at the University of California, Davis, CA, USA. He is currently an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Louisiana at Lafayette, Lafayette, LA, USA.



Dr. Shuai Zhang received his Ph.D. degree from the Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute (RPI) in 2021. He is currently an Assistant Professor in the Ying Wu College of Computing at the New Jersey Institute of Technology (NJIT), NJ, USA.



Prof. Chuan Huang received his Ph.D. degree from the Department of Electrical and Computer Engineering at Texas A&M University, College Station, TX, USA, in 2012. He is currently a Professor in Shenzhen Institute for Advanced Study at University of Electronic Science and Technology of China, Shenzhen, China.

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