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arXiv 2501.07830eess.SP
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用于宽带光纤传输的深度学习波形信道建模:模型比较、挑战与潜在解决方案

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

Minghui Shi, Hang Yang, Zekun Niu, Chuyan Zeng, Junzhe Xiao, Yunfan Zhang, Mingzhe Chen, Weisheng Hu, Lilin Yi

AI总结:

本文提出一种DSP辅助的精度评估方法,全面比较了宽带光纤传输中的深度学习波形建模方案,发现FDD-BiLSTM表现最优,并分析了多信道高速率下的挑战及物理知识与模型优化的潜在解决方案。

摘要:

快速且准确的波形仿真对于理解光纤信道特性、开发数字信号处理(DSP)技术、优化光网络配置以及推动光纤传输系统向宽带化发展至关重要。凭借强大的非线性拟合能力和高效的并行计算,深度学习(DL)已成为一种强大的波形建模工具,与传统的分步傅里叶方法(SSFM)相比,具有高精度和低复杂度的优势。然而,大多数DL方法是针对少信道和低速率的WDM系统设计的,使其在宽带系统中的可扩展性存在不确定性。此外,缺乏标准化的精度评估方法以及波形误差与传输性能误差之间的结果不一致,阻碍了各种DL方案的公平比较。本文引入了一种结合非线性DSP的DSP辅助精度评估方法,为评估DL模型的精度提供了公平的基准。利用该方法,我们对从简单配置到更复杂的宽带设置的各种DL方案进行了全面比较。结合双向长短期记忆的特征解耦分布式方法与其他DL方案相比取得了更好的性能。此外,在更多信道和更高速率的场景下,FDD-BiLSTM的性能优势将进一步提升。然而,随着信道数和符号率的增加,FDD-BiLSTM的性能仍会逐渐恶化。我们从三个角度分析了这些挑战:更复杂的线性和非线性效应,以及SSFM所需的更高采样率。为了应对这些挑战,我们从两个方面讨论了潜在的解决方案:融入更多的先验物理知识和优化DL模型的结构。

英文摘要:

Fast and accurate waveform simulation is critical for understanding fiber channel characteristics, developing digital signal processing (DSP) technologies, optimizing optical network configurations, and advancing the optical fiber transmission system towards wideband. Deep learning (DL) has emerged as a powerful tool for waveform modeling, offering high accuracy and low complexity compared to traditional split-step Fourier method (SSFM), due to its strong nonlinear fitting capabilities and efficient parallel computation. However, most DL methods are designed for few-channel and low-rate WDM systems, leaving their scalability to wideband systems uncertain. Moreover, the lack of a standardized accuracy evaluation method and the inconsistent results between waveform errors and transmission performance errors, hinders fair comparisons of various DL schemes. In this paper, we introduce a DSP-assisted accuracy evaluation method integrated with nonlinear DSP, providing a fair benchmark for evaluating the accuracy of DL models. Using this method, we conduct a comprehensive comparison of DL schemes, ranging from simple configurations to more complex wideband setups. The feature decoupled distributed method combining with bidirectional long short-term memory achieves the better performance compared to other DL schemes. Furthermore, in scenarios with more-channel and higher-rate, the performance advantages of FDD-BiLSTM will be further improved. However, as the number of channels and symbol rates increase, the performance of FDD-BiLSTM still gradually deteriorate. We analyze these challenges from three perspectives: the more intricate linear and nonlinear effects, the higher sampling rate required for SSFM. To address these challenges, we discuss potential solutions from two aspects: incorporating more prior physical knowledge and optimizing the structure of DL models.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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