DWDM PATH OUTAGE PREDICTION AND ALTERNATIVE PATH RECOMMENDATION

Smart overview of the Invention

The patent application focuses on predicting path outages in Dense Wavelength Division Multiplexing (DWDM) networks and recommending alternative communication paths. It utilizes parameterized DWDM signals to determine network characteristics, which are then used to train machine learning models. These models predict potential outages and suggest alternate paths to ensure continuous communication.

Technical Field

The application pertains to DWDM networks, which allow multiple communication channels to be multiplexed over a single fiber using different wavelengths. These networks are susceptible to outages due to various factors, such as physical issues with fiber or dense wavelength spacing. The proposed solution addresses these challenges by leveraging machine learning.

Methodology

The invention involves determining the network topology of a fiber network, selecting parameter values, and applying these values to create parameterized DWDM signals. Characteristics of the fiber network are assessed, and a machine learning model is trained using these parameters and characteristics. This model predicts path outages and recommends alternate paths by analyzing factors like spectral efficiency and power efficiency.

System Components

Key components include a processing system with a processor and memory storing executable instructions for performing operations. The system includes a software-defined domain controller (SDDM), network elements like transponders, muxponders, and switchponders interconnected by fiber. The SDDM facilitates data generation, model building, and outage prediction.

Applications and Benefits

The invention provides methods for predicting DWDM path outages and recommending optimal alternative paths. By learning the properties of a DWDM optical network, it enhances network reliability. The approach includes adding flex transponder ports for each network element site, launching channels with various DWDM properties, and continuously analyzing them through an analytics engine. This proactive outage management can maintain service continuity with minimal disruption.