Six Key AI/ML Applications for Optical Networking

AI applications in optical networks are becoming increasingly important for enhancing the performance and reliability of data transport. By leveraging AI/ML in optical networks, network operators can achieve higher data rates, improved reliability, and lower operational costs. AI allows for the management of complex networks at a scale and speed that would be unattainable with traditional methods. As optical network technology evolves and data demands grow, the role of AI is expected to expand even further, driving innovation in network design, operation, and maintenance.

What are the possible AI/ML applications for Optical Networking ?

1. Network Design, Planning and Optimization:

   • Traffic Prediction: AI can predict traffic patterns and adjust bandwidth allocation proactively to meet demand, thus optimizing the use of network resources.

   • Route Optimization: Machine learning algorithms analyze network data to determine the most efficient paths for data packets, reducing latency and congestion driving to the concept of Self-Healing Networks

   • Self-Configuring Networks: AI/ML enables optical networks to configure themselves automatically when new devices are added or when changes in traffic are detected.

   • Resource Allocation: AI/ML dynamically allocates network resources such as wavelengths and bandwidth, optimizing for current network conditions and demand.

2. Failure Prediction:

   • By analyzing network data (hystorical and current), AI can predict when components are likely to fail and schedule maintenance before issues occur, improving network reliability.

3. Anomaly Detection for Proactive Restoration: AI/ML systems can monitor the network for anomalies that may indicate an impending failure, allowing for preemptive restoration of the services

4. Adaptive Transmission Systems:

   • Modulation Format Adjustment: AI/ML can select the optimal modulation format for data transmission based on real-time network conditions, such as signal quality and channel impairments.

   • Power Level Optimization: AI/ML algorithms adjust the power levels of optical signals to ensure efficient transmission while minimizing interference and cross-talk.

5. Learn from Real network:

   • Network Data Interpretation: AI/ML techniques allows to provide constructive data interpreation from Optical Time Domain Reflectometer (OTDR) and ONM raw data

6. Quality of Transmission (QoT) Estimation:

   • QoT Prediction: AI models predict the quality of transmission for new connections based on various network parameters, helping to ensure that SLAs (Service Level Agreements) are met.

Learn from Real Network : Automatic OTDR events recognition Let’s take a closer look at the learn from real network application. Optical experts analyze OTDR traces to identify faults in fiber links and guarantee the quality of transmissions. This is achieved by examining event signatures, which denote the location in the traces of the malfunctioning of a specific device or a fault, such as a broken fiber, a bad connector, or a bent fiber. OTDR systems operate by injecting a short laser pulse at one end of the fiber and measuring the backscattered and reflected light with a photodiode at the same location. The result of this process is termed OTDR trace, i.e., a graphical representation of the optical power as a function of the distance along the fiber. A typical example is reported in the below picture

Illustration of an OTDR trace with multiple events. The text annotations describe the root causes of these events.

It is now possible to use the recent automatic event detection AI/ML algorithms to bypass time-consuming and tedious human inspections. The application is “trained” to understand and recognize the different event patterns like the one below.

Possible patterns used to “train” the alghorithm.

AI/ML events recognition is a visual recognition process: the AI/ML can see events that mathematical OTDR analysis cannot find. This results in a very powerful analysis for the user to extrapolate where the optical fiber had an issue in order to be able to fix it.

Example of an AI/ML describe the “events” to the user.

Streamline and Simplify Managing Optical Networks Cognitive networks are a subset of AI applications tailored specifically for network management, capable of gathering data, learning from it, devising strategies, making decisions, and executing appropriate actions. Machine learning algorithms are the cornerstone of this approach, offering in-depth insights into network behavior, which, in turn, enable operators to make informed and efficient decisions for network optimization.

These principles are equally relevant to optical networks, where they unlock a multitude of use cases, including network optimization, proactive network recovery, and enhanced analysis of network conditions. Although we are in the early stages of integrating AI and ML into network management, the potential is undeniable. AI and ML tools present a valuable asset for network operators, promising significant advancements in the efficiency and reliability

For more information, please refer to NCS 1000 product page