Efficient RSOA Modelling using Polar Complex-Valued Neural Networks
Dghais, W. Dghais
; Ribeiro, V. M. C.
; Liu, Z.
Vujicic , Z.
Optics Communications Vol. ?, Nº ?, pp. 1 - 4, September, 2014.
ISSN (print): 0030-4018
Journal Impact Factor: 1,552 (in 2008)
Digital Object Identifier: 10.1016/j.optcom.2014.08.031
his work presents an effective solution to reduce the computational complexity order of
behavioral model generation for reflective semiconductor optical amplifier (RSOA). The proposed
model is based on a complex valued (CV) neural network (CVNN) structure, using polar CV basis
functions architecture. The CV model parameters are extracted by means of nonlinear complex-domain
Levenberg-Marquardt algorithm, from recorded experimental 20 Msymbol/s 64-quadrature amplitude
modulation (QAM) input-output data. The evaluation results of polar CVNN model proves to be more
adequate to accurately describe the nonlinear dynamic magnitude and phase distortions of RSOA,
compared to double-input double-output real-valued neural network (RVNN) rectangular structure.
Additionally, significant reduction of the computational cost is achieved in comparison to the RVNN