基于改进迁移学习的光通信网络流量数据连续插值研究

Research on continuous interpolation of traffic data in opticalcommunication networks based on improved transfer learning

光通信网络流量数据具有大规模和高维度的特点,而数据量纲不一致,数据之间的差异会被放大,使得插值效果不理想,所以提出基于改进迁移学习的光通信网络流量数据连续插值方法。通过Box-Cox变换法对流量数据展开标准化处理,统一数据量级与量纲。通过深度学习理论与VNet技术改进卷积神经网络,通过更新网络参数使连续插值结果与理想数据进行匹配,得到流量数据连续插值结果。实验表明,所提方法的信噪比始终高于27.83 dB,频率-波形分布图与理想数据的频率-波形分布图相似度最高,决定系数在0.8以上,能够获得高质量插值结果。

The traffic data in optical communication networks has the characteristics of large-scale and high dimensionality,and the inconsistency of data dimensions amplifies the differences between the data,resulting in unsatisfactory interpolation effects.Therefore,a continuous interpolation method for optical communication network traffic data based on improved transfer learning is proposed.The Box-Cox transformation method is used to standardize the traffic data and unify the data scales and dimensions.The convolutional neural network is improved using deep learning theory and VNet technology.By updating the network parameters,the continuous interpolation results are matched with the ideal data,obtaining the continuous interpolation results of the traffic data.Experimental results show that the signal-to-noise ratio of the proposed method is always higher than 27.83 dB,and the frequency-waveform distribution graph is most similar to the ideal data,with a coefficient of determination above 0.8,which can obtain high-quality interpolation results.