Publication

Abstract : This paper attempts to reconstruct and optimize a DWDM system by avoiding distortions such as Four-Wave Mixing (FWM) and high signal distortion due Inter-Channel Interference (ICI) using supervised and unsupervised learning approaches. FWM in high channel DWDM system reduces the network flexibility, transmission capacity and increases computational difficulties. The ICI and Signal distortion that affects spectral efficiency, increases network latency, leading to undesirable data re-transmission. To solve these above stated issues, the DWDM system necessitates optical variables optimization using supervised and unsupervised regression learning. In this paper, we reconstruct the DWDM system design using supervised and unsupervised regression learning approaches, which are used to identify, correlate, and optimize the FWM influencing optical parameters. Furthermore, trained datasets are generated from parameter-based simulations. Results are analyzed using supervised and unsupervised regression approaches, which improves the DWDM mechanism and achieves accuracy through a computerized regression model controller. Thus, the reconstructed DWDM system is re - designed with optimized FWM parameters obtained through supervised machine learning approaches, and unsupervised training evaluates the proposed R-DWDM system to predict Q-factor, OSNR, signal, and noise power levels accurately.