Research Journal of Engineering and Technology

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Application of Artificial Neural Networks for Optimal Design of OADM for DWDM Applications


Affiliations
1 Department of Electronics & Communication Engineering, IKGPTU, Jalandhar, Punjab, India
2 Department of Electronics & Communication Engineering, Maharaja Ranjit Singh State Technical University, Bathinda, Punjab, India
3 Department of Electronics & Communication Engineering, SSIET, Derabassi, Mohali, Punjab, India
     

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For greater flexibility and connectivity, optical add/drop multiplexers (OADMs) with wavelength routing devices and switches play a crucial role in DWDM optical networks. OADM can act as access node (AN) in any DWDM network. They can add or drop signals as per our requirement. A DWDM system is modeled using different number of channels, data rates and spacing between channels in terms of bit error rate (BER), optical signal to noise ratio (OSNR), Jitter and Dispersion. These parameters are then optimized and classification of signals analyzed by using Artificial Neural Networks. Comparison was performed between FBG/OADM and OFDM in respect of transmitted and received signal powers.

Keywords

Optical Add/Drop Multiplexer, Dense Wavelength Division Multiplexing, Feed Forward Artificial Neural Network.
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  • Application of Artificial Neural Networks for Optimal Design of OADM for DWDM Applications

Abstract Views: 229  |  PDF Views: 5

Authors

Parveen Bajaj
Department of Electronics & Communication Engineering, IKGPTU, Jalandhar, Punjab, India
A. K. Goel
Department of Electronics & Communication Engineering, Maharaja Ranjit Singh State Technical University, Bathinda, Punjab, India
Harbhajan Singh
Department of Electronics & Communication Engineering, SSIET, Derabassi, Mohali, Punjab, India

Abstract


For greater flexibility and connectivity, optical add/drop multiplexers (OADMs) with wavelength routing devices and switches play a crucial role in DWDM optical networks. OADM can act as access node (AN) in any DWDM network. They can add or drop signals as per our requirement. A DWDM system is modeled using different number of channels, data rates and spacing between channels in terms of bit error rate (BER), optical signal to noise ratio (OSNR), Jitter and Dispersion. These parameters are then optimized and classification of signals analyzed by using Artificial Neural Networks. Comparison was performed between FBG/OADM and OFDM in respect of transmitted and received signal powers.

Keywords


Optical Add/Drop Multiplexer, Dense Wavelength Division Multiplexing, Feed Forward Artificial Neural Network.