Traffic prediction model for cognitive networks

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.