基于深度学习的高速率无源波分复用系统智能集成优化与性能预测研究

Research on Intelligent Integration Optimization and Performance Prediction of High-Rate Passive WDM Systems Based on Deep Learning

摘要: 高速无源波分复用(WDM)系统在工程化集成与封装环节中，常面临性能参数漂移、耦合损耗难以精确管控以及故障定位过度依赖人工经验等瓶颈。为此，文章引入长短期记忆网络(LSTM)，确立了一种新型的智能化系统集成框架。该方案立足于软件工程化设计理念，通过构建基于LSTM的时序预测与故障诊断模型，实现了对插入损耗、串扰及耦合效率等核心指标的精准刻画，并完成了对系统传输劣化与封装失效的自动辨识。经高速WDM实验平台及实测数据集验证，该方法不仅能够有效克服人工调试的随机性，显著提升系统的集成效率与稳定性，还为高可靠性光通信系统的智能化演进提供了理论依据与工程参考。

Abstract: High-speed passive wavelength division multiplexing (WDM) systems frequently encounter critical bottlenecks in engineering integration and packaging, including performance parameter drift, inaccurate control of coupling loss, and over-reliance on manual experience for fault localization. To tackle these challenges, this paper introduces long short-term memory (LSTM) networks and proposes a novel intelligent system integration framework. Grounded in software engineering principles, the framework employs LSTM-based time-series prediction and fault diagnosis models to achieve precise characterization of key metrics such as insertion loss, crosstalk, and coupling efficiency, as well as automatic identification of transmission degradation and packaging failure. Validated on a high-speed WDM testbed using measured datasets, the proposed method effectively reduces the uncertainty caused by manual debugging, significantly improves system integration efficiency and stability, and provides a theoretical foundation and engineering reference for the intelligent development of highly reliable optical communication systems.