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- Title
Supervised regression modelling for mitigation of four-wave mixing in dense wavelength-division multiplexing systems.
- Authors
Venkatesan K.; Chandrasekar A.; Ramesh P. G. V.
- Abstract
A recent global crisis associated with COVID-19 has encouraged millions of people to work from home, thus causing a drastic increase in overall network traffic, data-rate requirements and end network capabilities. This has also produced more noise, cross-talk and undesirable optical-fibre nonlinearities, especially a fourwave mixing (FWM) effect that deteriorates performance of dense wavelengthdivision multiplexing (DWDM) systems. A presence of FWM in the DWDM systems imposes increasing complexity and latency of networks, and decreases their spectral efficiency. In its turn, this degrades efficient utilization of optical bandwidth. To mitigate the above problems, we suggest a supervised regression modelling (SRM). A relevant SRM-DWDM approach performs self-parametric optimization of the DWDM systems with machine-learning techniques and finds real trade-offs among various factors that affect the FWM. Our model reduces complexity of modelling and computational time, resulting in accurate and reliable prediction of parameter values. We also evaluate the performance of our SRMDWDM technique by comparing its data with the iterative results obtained for different parameters (e.g., output signal-to-noise ratio, Q-factor, signal power and noise power). Finally, we specify the procedures necessary for global optimization of DWDM systems.
- Subjects
FOUR-wave mixing; REGRESSION analysis; WAVELENGTH division multiplexing; TELECOMMUTING; SIGNAL-to-noise ratio; GLOBAL optimization
- Publication
Ukrainian Journal of Physical Optics, 2021, Vol 22, Issue 1, p12
- ISSN
1609-1833
- Publication type
Academic Journal
- DOI
10.3116/16091833/22/1/12/2021