Enhanced approach for artificial neural network-based optical fiber channel modeling: Geometric constellation shaping WDM system as a case study
Year: 2024
Type: article
Abstract: Recently, there has been increasing interest in applying machine learning (ML) approaches to enhance the performance of optical communication systems. This paper applies some of these approaches to design advanced wavelength-division multiplexed (WDM)-coherent optical fiber communication (OFC) systems assisted by the constellation shaping technique. A theoretical design and performance investigation are reported assuming end-to-end deep learning (E2EDL) autoencoder (AE)-assisted system configuration. A flexible artificial neural network (ANNs)-based optical fiber channel modeling approach suitable for different multi-span transmission links in OFCs is presented. This approach is applied to E2EDL-based geometric constellation shaping WDM systems and the results reveal that using a bi-directional gated recurrent unit (Bi-GRU)-neural network (NN) gives the best modeling that tracks the numerical nonlinear interference noise fiber model with much less computation time(~7%). This work is implemented using the Python programming language and utilizing the TensorFlow framework to develop the simulation models.
Authors: A. M. Abbass, R. S. Fyath
Institutions: Mustansiriyah University, Nahrain University
Language: English
Topic: Optical Network Technologies
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
Funder: Al-Nahrain University
OpenAlex ID: W4406212455