Neural Network Innovations in Next-Generation Optical Fiber and WDM Networks: Tackling Signal Degradation and Resource Optimization

Neural networks have been included in optical fiber communication along with WDM networks, which have demonstrated new paths for increasing such systems' performance, availability, and effectiveness. Optical fiber communication used for high data rates suffers from signal attenuation, noises and nonlinearities, especially at longer transmission distances. Neural networks provide solutions through identifying/recognizing complicated signal patterns and at the same time correcting the existing distortions on a real-time basis. They can be used for such applications as signal equalization, nonlinearity correction, and noise removal, and can enhance data transfer quality by many times. When several data channels are carried out simultaneously using different wavelengths, it is found that neural networks are very effective in controlling the WDM networks. They are capable of predicting the traffic pattern, doing dynamic resource allocation efficiently, and providing an optimized routing and wavelength assignment (RWA) model compared to conventional techniques. In addition, with the development of neural networks, OPM functions are brought to a higher level in terms of speed and accuracy of the assessment of other important optical metrics, including OSNR and chromatic dispersion. Furthermore, such networks can also recognize possible faults, thus providing stable and reliable communication. A continued increase in the requirement of data transmission rate makes the use of neural networks in optical fiber communication and WDM systems crucial in handling the main issue, providing innovative and optimized solutions for future-generation communication systems