Machine Learning Based DWDM Design Using Regression Modelling

TL;DR: The regression based correlated DWDM design (R-DWDM) is improvised mechanism over the independent parameter based simulations and thus improves accuracy through the derived R-value for optical parameters such as input power, channel spacing, optical gain and data rate.

Abstract:  In this paper, we discuss the non-linearity problems such as Four Wave Mixing (FWM) and high signal distortion with low Output Signal to Noise Ratio (OSNR) in the design of a 64-channel DWDM system using Regression learning technique. The occurrence of FWM in a DWDM system with high number of channels reduces the performance of an optical fiber system in terms of bandwidth and increases computational complexity. High signal distortion with low OSNR reduces network throughput, energy efficiency and thus forces re-transmission. To overcome the above problems, DWDM system with higher number of channel necessitates an optimized design based on correlation factors of optical dependent and independent variable factors such as BER, Q-factor, signal power, noise power and OSNR. Proposed here is a regression optimized DWDM system design. Regression is used here for correlating and optimizing the optical parameters. In this paper, the problem of non-linearity is solved through optimized DWDM design based on correlated parameters in 16, 32 and 64- Channeled DWDM system. The regression based correlated DWDM design (R-DWDM) is improvised mechanism over the independent parameter based simulations and thus improves accuracy. The R-DWDM design system shows higher accuracy through the derived R-value for optical parameters such as input power, channel spacing, optical gain and data rate. The enhancement achieved through the regression based optimized DWDM design is evaluated in terms of optical measurements such as signal power, noise power, Q-factor and BER.