Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks

• Autor(a) principal: Souza,André Luiz Nunes de
• Data de Publicação: 2021
• Outros Autores: Sutili,Tiago, Cruz Júnior,José Hélio da, Figueiredo,Rafael Carvalho
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Journal of Microwaves. Optoelectronics and Electromagnetic Applications
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-10742021000400689
• topic: digital signal processing; elastic optical networks; modulation format blind identification

Abstract In an elastic network paradigm, where the transceiver is able to control several characteristics of the transmitted signal according to the optical link quality and capacity demand, receivers able to automatically detect the modulation format are fundamental to recover the transmitted signal without the necessity of headers that reduce system capacity. This work presents a simulated performance comparison of six methods for blind identification of modulation format in high-capacity optical systems: k-nearest neighbors (KNN), k-means, fuzzy c-means, deep neural networks, support-vector machine (SVM) and peak-to-average power ratio (PAPR). The transmitted channels were 64-GBd modulated with the following modulation formats available at the transceiver: QPSK, 16QAM, 64QAM, and 256QAM. The optical link was emulated considering several impairments, as amplified spontaneous emission from optical amplifiers, frequency and phase noise from lasers, and polarization rotation and differential group delay from the propagation. The support-vector machine algorithm presented the most robust results.