Jinxiang Song
Jinxiang Song is a PhD student in the Communication Systems Group. His research interests are in Machine Learning and Digital Communication.
Showing 16 publications
Spatial Signal Design for Positioning via End-to-End Learning
Autoencoders for Physical-Layer Communications: Approaches and Applications
Blind Frequency-Domain Equalization Using Vector-Quantized Variational Autoencoders
Experimental Demonstration of Learned Pulse Shaping Filter for Superchannels
End-to-End Learning for Integrated Sensing and Communication
Learning Optimal PAM Levels for VCSEL-based Optical Interconnects
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning
Model-Based End-to-End Learning for WDM Systems With Transceiver Hardware Impairments
Periodicity-Enabled Size Reduction of Symbol Based Predistortion for High-Order QAM
Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User Communication
Symbol-Based Supervised Learning Predistortion for Compensating Transmitter Nonlinearity
End-to-end Autoencoder for Superchannel Transceivers with Hardware Impairments
Over-the-fiber Digital Predistortion Using Reinforcement Learning
Benchmarking End-to-end Learning of MIMO Physical-Layer Communication
Learning Physical-Layer Communication with Quantized Feedback
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Showing 1 research projects
6G Artificial Intelligence Radar