DAMA: A data mining forecasting DBA scheme for XG-PONs

Abstract

The latest new generation passive optical network (NG-PON) standard, known as 10-gigabit-capable passive optical network (XG-PON), enables a very promising architecture that offers 10 Gbps nominal data delivery ratio in the downstream direction. The optical line terminal (OLT) is located within the CO and constitutes the main decision-making tank of the PON. OLT applies a dynamic bandwidth allocation (DBA) scheme for coordinating the transmission opportunities, especially in the upstream direction. According to the standard, a differential fibre distance of 40 km, between ONUs and OLT, is allowed. This outspread deployment implies high propagation delays which should be taken into account of designing the bandwidth allocation. This work is focused on proposing a cognitive DBA scheme which is capable of forecasting the additional bandwidth, which arrives in ONUs, during the transmission coordination between OLT and ONUs. The k-nearest neighbors (k-NN) algorithm is applied for forecasting the additional bandwidth requests of each ONU. In addition, the adopted algorithm is enhanced with an adaptive learning-based method which efficiently selects the most appropriate k value based on the traffic dynamics.