Artificial intelligence based quality of transmission predictive model for cognitive optical networks

• Published in: Optik (Stuttgart) Vol. 257; p. 168789
• Main Authors: Singh, Harinder, Ramya, D., Saravanakumar, R., Sateesh, Nayani, Anand, Rohit, Singh, Swarnjit, Neelakandan, S.
• Format: Journal Article
• Language: English
• Published: Elsevier GmbH 01.05.2022
• Subjects: Artificial intelligence; Machine learning; Optical networks; Predictive model; Quality of Transmission; Transmission equation; Optical networks; Transmission equation; Artificial intelligence; Quality of Transmission; Predictive model; Machine learning
• ISSN: 0030-4026; 1618-1336
• DOI: 10.1016/j.ijleo.2022.168789

Due to the advancements in 5 G technologies, high-definition, and the internet of things (IoT), the capacity demand of optical networks has been exponentially increased. Optical communication networks offer several metrics such as high transmission capacity, low transmission loss, better anti-interference, robustness, etc which offers new opportunities to the communication field. To satisfy the increasing demands of optical networks, effective network resource utilization become essential. So, it is needed to design proper planning tools with superior accuracy for quality of transmission (QoT) in optical networks. Recently, artificial intelligence (AI) techniques pose new opportunities for resolving these issues and machine learning (ML) algorithms offer better performance over the analytical approaches. With this motivation, this paper presents a novel AI based cognitive QoT prediction (AI-CQoT) model for optical communication networks. The proposed AI-CQoT model aims to predict the QoT for the quality of service (QoS) link setup using AI techniques with the transmission equation based synthetic data generation. The proposed model uses the Label weighting extreme learning machine (LW-ELM) model for the prediction process which includes a link and signal characteristics as input features. Besides, the LW-ELM model is trained by the use of transmission equations. For improving the predictive performance of the LW-ELM model, the parameters such as weight matrix W and penalty factor C are optimally tuned by the use of the shuffled shepherd optimization (SSO) algorithm. A detailed experimental validation is performed to highlight the improved performance of the AI-CQoT model and the results are investigated in terms of different performance measures.