An efficient optimization scheme for WDM/TDM PON network planning

Abstract

With the growing popularity of bandwidth demanding services such as HDTV, VoD, and video conferencing applications, there is an increasing demand on broadband access. To meet this demand, the access networks are evolving from the traditional DSL (xDSL more recently) and cable techniques to a new generation of fiber-based access techniques. While EPONs and GPONs have been the most studied passive optical access networks (PONs), WDM-PON is now clearly seen as the next generation trend with an hybrid set of switching equipment.We propose here an original optimization scheme for the deployment of greenfield PON networks where we minimize the overall deployment cost. Given the geographical location of ONUs and their incoming/outgoing traffic demands, the newly proposed scheme optimizes the placement of splitters/AWGs in a PON and the link dimensioning in order to provision the overall demand.The optimization scheme proceeds in three phases. In the first phase, we generate several potential equipment hierarchies, where each equipment hierarchy is associated with an ONU partition such that a switching equipment is associated with each cluster, each ONU belongs to a single cluster, and the splitting ratio of the equipment corresponds to the number of ONUs in the cluster. In the second phase, for each equipment hierarchy, we make use of a column generation (CG) mathematical model to select the type and location of the switching equipment that leads to the minimum cost multi-stage equipment topology which accommodates all the traffic demand. The third phase selects the best hierarchy among all the generated and dimensioned hierarchies.The optimization model encompasses the particular cases where all switching equipment are either splitters and AWGs, and outputs the location of the switching equipment together with the dimensioning of the PON network. We performed numerical experiments on various data sets in order to evaluate the performance of the optimization model, and to analyze the type of equipment hierarchies which are generated depending on the traffic and the location of the ONUs.