Enhanced resource allocation in elastic optical network using deep learning and optimization process
Year: 2025
Type: article
Abstract: Elastic optical networks offer several advantages in bandwidth allocation compared to traditional fixed-grid optical networks. These advantages stem from their ability to flexibly and efficiently allocate resources, meeting the dynamic and diverse demands of modern communication networks. It is crucial to handle dynamic traffic loads and proactively manage the resources in elastic optical networks with a productive technique. Deep learning is an effective tool for complex data analysis and real-time decision making. We address a model that integrates two deep neural networks: generative adversarial networks (GAN) for data augmentation; and echo state networks (ESN) for network’s requirement prediction. Furthermore, an optimization process is carried out for efficient spectrum allocation. The GAN provides a considerable and reliable quantity of data necessary to train the ESN model that could provide a desired output. The ESN model is further enhanced by optimizing the essential parameters enabling it to learn diverse traffic patterns and anticipate unusual situations. By using a GAN-ESN approach, there is a substantial benefit in reducing latency, saving energy and optimizing bandwidth allocation. The simulation results confirm that the proposed scheme can significantly improve the performance of resource management and achieve a high degree of fairness(95%accuracy) in the evaluation metrics.
Source: Optical Fiber Technology
Authors: Subbulakshmi Easwaran, Mehdi Shadaram
Institution: The University of Texas at San Antonio
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,360
OpenAlex ID: W4408919802