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Fiber Bragg Grating based an Optimal OADM for Performance Enhancement in DWDM using Artificial Neural Network
Objectives: This paper proposed a new method for performance enhancement of Optical Add or Drop Multiplexer (OADM) with the Dense Wavelength Division Multiplexing (DWDM) based on the artificial intelligence. Methods/Statistical Analysis: The FF-NN is trained and tested in the MATLAB platform from the parameters obtained from simulation. The NN is trained for 70 signals with different number of channels and with different data rates as well as it is tested with 30 signals. The classification of the signals to be added or dropped is determined by the FF-NN. The DWDM network is initially modeled with the OptiSystem software tool with different number of channels with different data rates and channel spacing. Findings: The parameters BER, OSNR, Jitter and Chromatic Dispersion have been calculated. The training and testing of the neural network is carried out on the MATLAB platform and based on this, the signal will add/drop or allow the signal to pass. The result shows that the proposed method achieves the accuracy of 97.28% on classifying the signals to be dropped from the fiber or passed through it without any interruption regarding its ability to make the transmission with minimum error. The performance of the proposed method is also analyzed in terms of Transmitted & Received Signal Power and compared with the conventional OADM system with multiple filters. Applications/Improvements: The proposed method offers a viable solution to increase the performance of OADM with the DWDM. The improvement includes the use of hybrid algorithms, to further increase the performance.
Dense Wavelength Division Multiplexing, Feed Forward Artificial Neural Network, Fiber Bragg Grating, Optical Multiplexer, Performance Enhancement, Signal Power.
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