To enhance the versatility and connectivity of optical add/drop multiplexers (OADMs) in DWDM optical networks, these devices equipped with wavelength routing and switching capabilities are of paramount importance. OADMs can serve as access nodes (AN) within any DWDM network, enabling the selective addition or removal of signals as needed. The modeling of a DWDM system encompasses varying aspects such as the number of channels, data rates, and channel spacing, with a focus on parameters like bit error rate (BER), optical signal-to-noise ratio (OSNR), jitter, and dispersion. Through optimization, these parameters can be fine-tuned, and the classification of signals can be examined using Artificial Neural Networks.

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Performance Evaluation of OADM for DWDM Applications Using Artificial Neural Networks

  • Parveen Bajaj,
  • Ritu Gupta,
  • Gaurav Aggarwal

摘要

To enhance the versatility and connectivity of optical add/drop multiplexers (OADMs) in DWDM optical networks, these devices equipped with wavelength routing and switching capabilities are of paramount importance. OADMs can serve as access nodes (AN) within any DWDM network, enabling the selective addition or removal of signals as needed. The modeling of a DWDM system encompasses varying aspects such as the number of channels, data rates, and channel spacing, with a focus on parameters like bit error rate (BER), optical signal-to-noise ratio (OSNR), jitter, and dispersion. Through optimization, these parameters can be fine-tuned, and the classification of signals can be examined using Artificial Neural Networks.