Traffic prediction model for cognitive networks

摘要

As cognitive networks become so booming, many traditional network utilities must be reconsidered owing to uncertain and complicated changes after spectrum decision. It is a challenge for nodes to predict unknown network traffic precisely combined with spectrum characteristics. In this paper, we present a Relevance Vector Machine (RVM) based traffic prediction model. Based on the judgment of spectrum and wireless environments characteristics, networks traffic can be predicted with periodical samples training to form a close loop feedback. Simulation results for our model are presented and compared to Least Square Support Vector Machine (LS-SVM) scheme, and the simulation results show that the RVM solution improved prediction accuracy up to 60% at most.