Implementation of Neural Network-Based Linear Block Code Decoder in SAC-OCDMA System

Abstract

This study investigates the application of deep learning technology to realize a linear block code decoder in a spectral-amplitude-coding optical code-division multiple access (SAC-OCDMA) system. To enhance the system’s performance, the neural network approach is employed to design a linear block code decoder capable of independently learning the optimal method for recovering original data from received signals. Simulation results demonstrate that the deep learning-based linear block code decoder outperforms traditional decoders at various signal-to-noise ratios (i.e., different active user numbers), showcasing the potential of deep learning in the field of optical communication.