With the development of mobile network technology, network traffic has not only experienced exponential explosive growth, but also its application scenarios have become more and more extensive. It is a challenging proposition to find an efficient and accurate matching prediction model based on massive idiosyncratic data. This scheme proposes to introduce EMD modal decomposition to decompose the local feature components of data based on the time-scale features of the data itself, to do cluster analysis on the components by K-mean clustering algorithm, and then to model and predict the clustered local feature components by XGBoost model, so as to reduce the data dimensionality and prediction complexity. The results show that the modeling and prediction of the clustered local feature components using XGBoost model effectively improves the model prediction accuracy.

This paper first describes the new data characteristics and the standard collection method of LTE network measurement report. Then the network structure evaluation methods based on LTE MRO measurement report are proposed, including the output and calculation methods of over coverage ,overlapping coverage and other indicators.