Fugang Liu

Papers

Total Citations

143

H-Index

About

Dr. Fugang Liu is a leading researcher at the intersection of deep learning and wireless communications, with a primary focus on automatic modulation recognition and signal classification. His most impactful contribution, the 2020 survey "Deep Learning for Modulation Recognition: A Survey With a Demonstration," has garnered 143 citations, establishing itself as a foundational reference in the field. In this seminal work, Dr. Liu systematically reviews a wide array of deep learning architectures—from convolutional to recurrent neural networks—applied to the challenging task of identifying modulation schemes in wireless signals. By bridging the gap between computer vision breakthroughs and radio frequency intelligence, his research demonstrates how DL can dramatically outperform traditional feature-based methods. This work not only provides a comprehensive taxonomy of existing approaches but also includes practical demonstrations that guide future implementations. Dr. Liu’s contributions are particularly vital for advancing cognitive radio, spectrum monitoring, and secure communications. His scholarship serves as an essential roadmap for students and engineers seeking to harness deep learning for intelligent signal processing, making him a key figure in the ongoing evolution of autonomous wireless systems.