Improving Optical Transmission Performance in Multi-Channel Networks Utilizing Convolutional Neural Network-Enabled Digital Signal Processing to Mitigate Four-Wave Mixing
The rapid growth of online services and the global transition to digital platforms, accelerated by the COVID-19 pandemic and the emergence of AI-based services, have led to a surge in demand for high-capacity optical communication networks (OCNs). However, this transition exacerbates the issue of fiber nonlinearity, notably four-wave mixing (FWM), which can significantly impact signal integrity in long-distance OCN transmissions with multiple channels. To address this challenge, this paper presents a novel approach leveraging a convolutional neural network (CNN)-based digital signal processing (DSP) model to mitigate the nonlinear effects of FWM on signal quality. The proposed CNN model, designed with three convolutional layers and trained on a comprehensive dataset of simulated OCN transmissions, demonstrates significant improvement in reducing nonlinear distortions caused by FWM. Our method is compared with traditional compensation techniques, such as digital back-propagation and Volterra series-based equalizers, showing a reduction in power penalties by 1 dB and an enhancement in the power budget by 2 dB. Simulation results indicate that the bit error rates (BERs) remain consistently below the critical threshold of 10−9 across various transmission distances, outperforming conventional methods. The simulation analysis, conducted on a 100 km multi-channel OCN testbed, confirms the CNN equalizer’s efficacy in reducing crosstalk and improving power efficiency, with a demonstrated 15% reduction in required launch power. Moreover, the analytical models used to evaluate the CNN-based DSP model show high accuracy in predicting performance, underscoring the model’s capability to achieve high-quality transmission with lower power requirements. These findings advocate for the integration of our CNN-based DSP model into OCNs, offering a fruitful solution for managing multi-channel, long-distance, and high-data-rate optical transmissions in real-world systems.
Authors
- Haleem Afsar (ORCID: https://orcid.org/0000-0001-6232-1131)
- Teong Chee Chuah (ORCID: https://orcid.org/0000-0002-6285-9481)
- Ying Loong Lee (ORCID: https://orcid.org/0000-0002-0841-449X)
- Ali Alshamrani (ORCID: https://orcid.org/0009-0002-8929-4991)
- Farman Ali (ORCID: https://orcid.org/0000-0002-5400-9831)
- Muhammad Sheraz (ORCID: https://orcid.org/0000-0002-8985-3063)
Institutions
- Multimedia University (MY)
- Taif University (SA)
- Qurtuba University of Science and Information Technology (PK)
- Universiti Tunku Abdul Rahman (MY)
- Nanjing University of Aeronautics and Astronautics (CN)
Publication Details
- Journal
- Fiber & Integrated Optics
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1080/01468030.2024.2412744
- Citations
- 6
- Primary Topic
- Optical Network Technologies
- Type
- article
- Field-Weighted Citation Impact
- 1.25
Funders
- Multimedia University