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Volume 13 | Issue 6 | Year 2026 | Article Id. IJECE-V13I6P113 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I6P113

Prediction of Long-Haul Dark Pulse Propagation Using a Hybrid Physics-Guided Data-Driven Model

Chinu Priyadarshani, Krishna Chandra Patra, Prabeen Kumar Sahu

Received Revised Accepted Published
14 Mar 2026 13 Apr 2026 12 May 2026 27 Jun 2026

Citation :

Chinu Priyadarshani, Krishna Chandra Patra, Prabeen Kumar Sahu, "Prediction of Long-Haul Dark Pulse Propagation Using a Hybrid Physics-Guided Data-Driven Model," International Journal of Electronics and Communication Engineering, vol. 13, no. 6, pp. 154-169, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I6P113

Abstract

In the optical fiber system, Long-Haul Dark Pulse Propagation (LHDPP) plays a vital role. The complex wavelength-dependent nonlinear interactions and the absence of dynamic oscillation modeling in existing approaches made it challenging to predict the propagation characteristics accurately. This work aims to develop a unified physics-guided and data-driven framework for modeling, characteristics prediction, and performance evaluation of dark pulse propagation in a Long-Haul Fiber Optic System. The proposed model integrates the coupled nonlinear Schrödinger equation with the Split-Step Fourier Method (SSFM) to simulate Long- Haul Dark Pulse Propagation under realistic conditions. A nonlinear polarization-maintained optical loop mirror-based multi-wavelength generation scheme, along with an early–late feed-forward equalized gate synchronization mechanism, is employed to enhance system fidelity. To capture dynamic wavelength interactions, a novel Tukey sliding window-based dynamic dual wavelength oscillation modeling approach is introduced using spectral conversion and cross-correlation analysis. Furthermore, a Deep Long Dirichlet E-Swish Short-Term Memory (DLDESTM) model is developed as a computationally efficient surrogate to learn the spatiotemporal dynamics of nonlinear propagation. The proposed framework achieves a prediction accuracy of 98.36%, demonstrates close agreement with experimental results at 1550 nm, and reduces computational complexity by approximately 46 times compared to conventional SSFM simulations. These results confirm improved prediction accuracy, enhanced signal quality, and reduced computational burden. The proposed approach provides a robust and scalable solution for accurate LHDPP characterization, enabling efficient design for next-generation optical communication systems.

Keywords

Coupled NLSE, Dark Pulse Propagation, Long-Haul Fiber Optic Communication, Physics-Guided Deep Learning, Split-Step Fourier Method.

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