Design And Implementation Of Mobile Network Performance Prediction System Based On Machine Learning

With the rapid development of modern mobile networks,the number of mobile network users has grown rapidly,and at the same time it has brought about an explosive increase in data size.How to evaluate the performance of mobile networks of the context of big data has become an important topic.At present,mainstream mobile networked performance evaluation methods rely on statistical knowledge and expert experience to evaluate network performance of index data,drive to test data,dial to test data,or measurement report data.However,with the ever-increasing data scale,traditional network performance assessment methods are increasingly difficult to cope with the challenges brought by massive data.Therefore,it is necessary to establish a multi-dimensional,more intelligent,mobile network performance evaluation scheme.Based on the above background,this paper uses machine learning related principles,as well as machine learning technology and big data technology,to design a mobile network performance prediction method based on machine learning.This method is based on the Spark distributed computing system.It applies machine learning methods to feature integration and data preprocessing on mobile data.Finally,it uses the LightGBM model for training and result prediction,and implements coverage and interference index fitting experiments for machine learning models.The front-end web system and back-end server with separate front-end and back-end are designed and implemented.The front-end uses Vue framework,Webpack framework,and ElementUI framework to complete web page development.The back-end server is built using Node.js and Express,while MySQL is used as the database of the system.This thesis first introduces the background and significance of topic selection,summarizes and analyzes the current research progress of the evaluation of mobile network performance,and proposes the research content and goals of this topic.After that,introduce the key technologies used for this article,including mobile network performance evaluation,machine learning algorithms,distributed framework,Spark computing engine,etc.Then it introduces the requirements analysis of the system,introduces the outline design of the system,and divides the functional modules of the system.The detailed design and module development of the system was completed.Finally,the system modules were tested for functional modules to verify the reliability and ease of use of the system.