Version 1
: Received: 16 October 2024 / Approved: 16 October 2024 / Online: 18 October 2024 (07:46:10 CEST)
How to cite:
Freitas, A.; Pires, J. Using Artificial Neural Networks to Evaluate the Capacity and Cost of Multi-Fiber Optical Backbone Networks. Preprints2024, 2024101299. https://doi.org/10.20944/preprints202410.1299.v1
Freitas, A.; Pires, J. Using Artificial Neural Networks to Evaluate the Capacity and Cost of Multi-Fiber Optical Backbone Networks. Preprints 2024, 2024101299. https://doi.org/10.20944/preprints202410.1299.v1
Freitas, A.; Pires, J. Using Artificial Neural Networks to Evaluate the Capacity and Cost of Multi-Fiber Optical Backbone Networks. Preprints2024, 2024101299. https://doi.org/10.20944/preprints202410.1299.v1
APA Style
Freitas, A., & Pires, J. (2024). Using Artificial Neural Networks to Evaluate the Capacity and Cost of Multi-Fiber Optical Backbone Networks. Preprints. https://doi.org/10.20944/preprints202410.1299.v1
Chicago/Turabian Style
Freitas, A. and João Pires. 2024 "Using Artificial Neural Networks to Evaluate the Capacity and Cost of Multi-Fiber Optical Backbone Networks" Preprints. https://doi.org/10.20944/preprints202410.1299.v1
Abstract
A possible solution to address the enormous increase in traffic demands faced by network operators is to rely on multi-fiber optical backbone networks. These networks use multiple optical fibers between adjacent nodes, and, when properly designed, they are capable of handling petabits of data per second (Pbit/s). In this paper, an artificial neural network (ANN) model is investigated to estimate both the capacity and cost of a multi-fiber optical network. Furthermore, a fiber assignment algorithm is also proposed to complement the network design, enabling the generation of datasets for training and testing the developed ANN model. The model consists of three layers, including one hidden layer with 50 hidden units. The results show that for a large network, such as one with 100 nodes, the model can estimate performance metrics with an average relative error of less than 0.4% for capacity and 4% for cost, while achieving a computation time nearly 800 times faster than the heuristic approach used in network simulation. Additionally, the network capacity is around 5 Pbit/s.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
