Paper Info
Title
Machine Learning-Based Channel Analysis for User Concentric Optical Switching Networks
Abstract
Optical switching networks (OSN) rely on both hardware components and signals for performing efficient switching operations achieving service level requirements of the end-user. In this manuscript, an incremental learning-based wavelength assignment is introduced to minimize asynchronous channel selection and dispersion-free switch-over in OSN. This method accounts on the wavelength dispersion characteristics of the light path for establishing and reconnecting communications between end-to-end devices. The learning process segregates favorable and conflict channels based on blocking probability and channel capacity assigned to the optical users. A conventional wavelength division multiplexing technique is employed for improving data transmission rates. This method is appropriate for evading blocking rates in optical networks to improve throughput rate and to leverage OSN performance. Machine learning (ML) paradigm is designed for OSN leverages the communication rate influenced by channel conflicts and utilization capacity. Therefore, the number of transmissions likely requires optimal switch-over with the knowledge of channel constraints to improve the performance of OSN.
Year
DOI
Venue
2020
10.1007/s00034-019-01165-3
Circuits, Systems, and Signal Processing
Keywords
Field
DocType
Channel switching, Dispersion, Incremental learning, Optical switching network, Wavelength assignment
Wavelength-division multiplexing,Throughput (business),Asynchronous communication,Optical switch,Data transmission,Communication channel,Service level requirement,Artificial intelligence,Channel capacity,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
39
2
0278-081X
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Ahmad Ali AlZubi133.76
Abdulaziz Alarifi233.76
Waleed Alnumay300.34