Enhanced Optical Wireless Communications via Deep Neural Network Assisted Pre-Equalization for Faster-than-Nyquist Transmission

The Faster-than-Nyquist (FTN) technology is widely used in optical wireless communication (OWC) systems to improve data rates and spectrum efficiency. However, it introduces inter-symbol interference (ISI), which can affect communication reliability. To address this issue, we propose a pre-equalization algorithm based on a deep neural network (DNN). The performance analysis primarily focuses on the bit-error-rate (BER) under a Gamma-Gamma atmospheric turbulence channel with varying acceleration factors. Simulation results show that our scheme effectively reduces the degradation in BER caused by ISI. Additionally, we observe an inverse relationship between the BER performance and the atmospheric refractive index constants as well as transmission distance, while a direct proportionality exists with respect to the filter roll-off factor and laser wavelength. Furthermore, comparing with conventional minimum mean square error (MMSE) and zero-forcing (ZF) algorithms highlights the superior performance of our proposal.