OSNR Monitoring for QPSK and QAM in Fiber-Optic Networks Using Machine Learning

This work discusses performance analysis of OSNR monitoring QPSK and QAM in optical fiber communication systems which are simple and cost-effective. The key future of advanced fiber-optic networks, live tracking for optical signal-to-noise ratio will become essential for optical system accomplishment. The optical fiber link generates chromatic dispersion and polarization-mode dispersion travel with the photonics signal, i.e., establish optical signal-to-noise ratio observe error. A low-bandwidth photodetector is important for the optical signal-to-noise ratio monitoring scheme, i.e., lower the cost and decrease the effects of chromatic dispersion and polarization-mode dispersion. We need to indicate an optical neural network for an optical signal-to-noise ratio monitoring scheme. Chromatic dispersion is the best estimation parameter to transmit light of different wavelengths at different speeds. The most potent modulation format for calculating bit error rate and optical signal-to-noise ratio is quadrature phase-shift keying, and quadrature amplitude modulation (4-QAM, 16-QAM, 64-QAM, and 256-QAM). The simulation results express that the preferred optical signal-to-noise ratio monitoring schemes achieve the best classification accuracy and low computational complexity. The simulation results find that the optical signal signal-to-noise ratio ranges from 0 to 20 dB for QPSK and QAM signals. The suggested method executes a high optical signal signal-to-noise ratio evaluation precision with a higher approximation error below 0.5 dB.