Kohonen Self-Organizable Maps based Classification of Optical Code Division Multiple Access Codes

• Main Authors: S, Shankari, Hariharan, G., Kushwaha, Sumit, Vidhya, K., Reddy, R. Vijaya Kumar, Madan, Parul
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial Neural Network (ANN); Codes; Integrated optics; OCDMA codes; Optical computing; Optical fibers; Optical network; Optical network units; Quality of service; Self-organizing feature maps; self-organizing Kohonen maps
• ISSN: 2767-7788
• DOI: 10.1109/ICICT57646.2023.10134033

Passive Optical Networks (PONs) are a type of fiber-optic network that use passive components, such as splitters and combiners, to distribute optical signals to multiple end-users. PONs typically consist of an Optical Line Terminal (OLT), and Optical Network Units (ONUs) or Optical Network Terminals (ONTs). PONs is also more energy-efficient and environmentally friendly than traditional networks, as they use passive components that do not require power. Overall, PONs is a cost-effective and efficient way to deliver high-speed broadband services to end-users. They offer several advantages over traditional networks and are expected to play a critical role in the deployment of next-generation broadband services. Optical code division multiple access (OCDMA) is an important candidate for implementing access control in PONs because it provides related features such as higher security and flexible on-demand bandwidth. In this context, this article presents a new method for classifying OCDMA codes using artificial neural networks, in particular Kohonen self-organizing maps (S OM), which is important for higher security of the network management system. for users, improving the quality of service (QoS). The code classification success rate was 87.5%, which justifies pattern identification methods to improve the optical code detection process.