基于深度学习的光纤通信网络性能提升策略研究

Research on Performance Improvement Strategy of Optical Fiber Communication Network Based on Deep Learning

光纤通信网络作为高速数据传输的主要媒介,其性能的优化对于满足日益增长的网络需求至关重要。文章着重探讨光纤通信网络面临的若干关键挑战,包括复杂的网络拓扑、动态流量特性、高级调制格式下的信道非线性效应以及故障检测与定位难题等,结合深度学习在数据处理和模式识别方面的强大能力,阐述一系列基于深度学习的光纤通信网络性能提升策略,旨在揭示深度学习对光纤通信网络性能提升的显著价值,为该领域的进一步研究与实践提供全面而深入的理论参考。

With the coming of the information age,optical fiber communication network is the main medium of high-speed data transmission,and its performance optimization is very important to meet the increasing network demand.This paper presents a systematic review and in-depth analysis of deep learning technology in improving the performance of optical fiber communication networks,focusing on several key challenges faced by optical fiber communication networks,including complex network topologies,dynamic traffic characteristics,channel nonlinear effects under advanced modulation formats,and fault detection and location problems.Combined with the strong ability of deep learning in data processing and pattern recognition,a series of performance improvement strategies based on deep learning are described.This review aims to reveal the significant value of deep learning to improve the performance of optical fiber communication networks,and provide a comprehensive and in-depth theoretical reference for further research and practice in this field.