Evolutionary strategy for practical design of passive optical networks

Passive optical networks (PONs) are an important and interesting technology for broadband access as a result of the growing demand for bandwidth over the past 10 years. An arduous and complex step in the design of such networks involves determining the placement of equipment, optical fiber cables and several other parameters relevant to the proper functioning of the network. In this paper, we propose an evolutionary strategy to optimize the infrastructure design of PONs by using genetic algorithm technique. This meta-heuristic is capable of elaborating fast, automatic and efficient solutions for the design and planning of PONs. Our proposal has been developed using real maps, aiming to minimize deployment costs and time spent to carry out PON projects, achieving pre-defined quality criteria. We considered, in our simulations, two scenarios (non-dense and dense), four possible topologies and two regions of interest. The non-dense consists of a scenario in which subscribers are distributed in a dispersed manner in the region of interest. The dense has a considerably higher number of subscribers distributed in a very close way to each other. Based on the obtained results, the potential of our proposal is quite clear, as well as its relevance from a technical, economic, and commercial point of view

GPON PLOAMd Message Analysis Using Supervised Neural Networks

This paper discusses the possibility of analyzing the orchestration protocol used in gigabit-capable passive optical networks (GPONs). Considering the fact that a GPON is defined by the International Telecommunication Union Telecommunication sector (ITU-T) as a set of recommendations, implementation across device vendors might exhibit few differences, which complicates analysis of such protocols. Therefore, machine learning techniques are used (e.g., neural networks) to evaluate differences in GPONs among various device vendors. As a result, this paper compares three neural network models based on different types of recurrent cells and discusses their suitability for such analysis