Traffic-load prediction based on echo state network improved by Bayesian theory in 10G-EPON

With the evolution of 10-gigabit Ethernet passive optical network (10G-EPON), the traffic-load prediction ability is necessary to support soaring services traffic with diversified characteristics and requirements. As a strong candidate to be used for the traffic-load prediction, the echo state network (ESN) may face the pseudo-regression problem and need to be improved for the better traffic-load prediction. To overcome this problem, this paper proposes an ESN based traffic-load prediction scheme using Bayesian theory in 10G-EPON for future-proof. In this proposed approach, Bayesian probability is introduced into the ESN and is used to improve the performance of ESN. According to the architecture between optical line terminal (OLT) and optical network units (ONU) in 10G-EPON, an ESN based on the Bayesian theory (B-ESN) is realized and the B-ESN based traffic load prediction scheme is also developed in OLT. Experiment results show that the proposed scheme can greatly better the accuracy of traffic-load prediction with lower complex degree.