Deep Learning Waveform Modeling for Wideband Optical Fiber Channel Transmission: Challenges and Potential Solutions

Fast and accurate optical fiber communication simulation system are crucial for optimizing optical networks, developing digital signal processing algorithms, and performing end-to-end (E2E) optimization. Deep learning (DL) has emerged as a valuable tool to reduce the complexity of traditional waveform simulation methods, such as split-step Fourier method (SSFM). DL-based schemes have achieved high accuracy and low complexity fiber channel waveform modeling as its strong nonlinear fitting ability and high efficiency in parallel computation. However, DL-based schemes are mainly utilized in single-channel and few-channel wavelength division multiplexing (WDM) systems. The applicability of DL-based schemes in wideband WDM systems remains uncertain due to the lack of comparison under consistent standards and scenarios. In this paper, we propose a DSP-assisted accuracy evaluation method to evaluate the performance for DL-based schemes, from the aspects of waveform and quality of transmission (QoT) errors. We compare the performance of five various DL-based schemes and valid the effectiveness of DSP-assisted method in WDM systems. Results suggest that feature decoupled distributed (FDD) achieves the better accuracy, especially in large-channel and high-rate scenarios. Furthermore, we find that the accuracy of FDD still exhibit significant degradation with the number of WDM channels and transmission rates exceeds 15 and 100 GBaud, indicating challenges for wideband applications. We further analyze the reasons of performance degradation from the perspective of increased linearity and nonlinearity and discuss potential solutions including further decoupling scheme designs and improvement in DL models. Despite DL-based schemes remain challenges in wideband WDM systems, they have strong potential for high-accuracy and low-complexity optical fiber channel waveform modeling.

Improve the Fitting Accuracy of Deep Learning for the Nonlinear Schrödinger Equation Using Linear Feature Decoupling Method

We utilize the Feature Decoupling Distributed (FDD) method to enhance the capability of deep learning to fit the Nonlinear Schrodinger Equation (NLSE), significantly reducing the NLSE loss compared to non decoupling model.

Solving dynamic multi-objective optimization problems via support vector machine

Dynamic Multi-objective Optimization Problems (DMOPs) refer to optimization problems that objective functions will change with time. Solving DMOPs implies that the Pareto Optimal Set (POS) at different moments can be accurately found, and this is a very difficult job due to the dynamics of the optimization problems. The POS that have been obtained in the past can help us to find the POS of the next time more quickly and accurately. Therefore, in this paper we present a Support Vector Machine (SVM) based Dynamic Multi-Objective Evolutionary optimization Algorithm, called SVM-DMOEA. The algorithm uses the POS that has been obtained to train a SVM and then take the trained SVM to classify the solutions of the dynamic optimization problem at the next moment, and thus it is able to generate an initial population which consists of different individuals recognized by the trained SVM. The initial populuation can be fed into any population based optimization algorithm, e.g., the Nondominated Sorting Genetic Algorithm II (NSGA-II), to get the POS at that moment. The experimental results show the validity of our proposed approach.