Parameter Setting of Load Forecasting Model for Adjacent Base Stations of Mobile Communication Based on Particle Swarm Optimization

Abstract

In order to study the parameter setting of load forecasting model of mobile communication adjacent base station, the particle swarm optimization algorithm is used to make intelligent optimization of the parameter values in the support vector regression model, so as to obtain a prediction model with better parameters. Through the verification of base station load prediction scheme assisted by the nearest neighbor base station, we establish the support vector regression model only using the base station itself historical information that introducing adjacent base station historical information, and compare the performance of the two parts. It is proved that the base station assisted load prediction scheme with assisted adjacent base stations can achieve better performance.