End-to-End Learning-based Autoencoder Framework for Faster-than-Nyquist Optical Wireless Transmission

We propose a novel autoencoder framework for FTN transmission to jointly optimize the ISI caused by FTN signaling and the impairments of the physical channel. The feasibility of the framework is experimentally demonstrated in a practical optical wireless communication system, achieving the lowest spectrum compression ratio of 0.68.