Are Small Issues Spiraling Out of Control? AI and Machine Learning May Help

Technology is outpacing traditional broadband assurance measures in the ever-changing telecommunications industry. The need for fast, dependable, and seamless internet has never been greater. Maintaining service quality becomes harder as networks become more sophisticated. Large-scale issues, including security breaches, can start small as seemingly unrelated anomalous events but eventually and quickly can spiral out of control. Detecting those issues manually is like finding a needle in a haystack.
 

Artificial intelligence (AI) and machine learning (ML) offer unique capabilities to enhance anomaly detection and resolution that improve broadband assurance, including its applications, benefits, and future developments.

Enhancing Broadband Assurance With AI and ML

Automation, pattern recognition, and real-time analytics using AI and ML are transforming broadband assurance. These technologies let networks self-optimize and repair, boosting performance and reliability. Through massive data analysis, AI and ML can find abnormalities that humans cannot, speeding up issue resolution and improving service quality.

Network Anomaly Detection and Prediction Using AI

AI enhances existing analysis and rules-based recommendations to identify, detect, and act on anomalous traffic patterns, device faults, network degradation, and security breaches. Operators can prevent network difficulties by using ML algorithms to discover patterns in past data. AI-powered predictive maintenance can anticipate equipment failures and plan prompt repairs, reducing downtime and enhancing network reliability.

ML Algorithms for Network Optimization

Supervised, unsupervised and reinforcement ML models can optimize network performance in the following ways:

• Supervised learning methods categorize and forecast network situations using previous data.
• Unsupervised learning can reveal network data patterns and correlations for improvement.
• Reinforcement learning dynamically adjusts network parameters for optimal performance.

AI and ML can supercharge processes for identifying and analyzing anomalous network, service, and subscriber behavior to determine if it will have a negative effect on overall experience. For example, changes in light levels and Bit Interleaved Parity (BIP) errors can appear for various reasons. The question is whether those incidents are the early warning signs of an impending outage or just random occurrences.

AI/ML Network Optimization Use Cases

Using AI/ML to sift through and analyze multiple data streams can help pinpoint potential trends and identify problems. For example, analyzing the level of BIP errors and signal strength over time along with the ambient temperature and time of day can pinpoint trends with connectors, optical components or the optical distribution network that, over time, could escalate into an outage or severe degradation in performance. The addition of AI/ML analysis will help proactively identify and take care of the root cause before the problem escalates.
 

Another use case involves identifying rogue optical network terminals (ONTs) before they become problematic to the entire passive optical network. ONTs use a predetermined timeslot to transmit upstream. One potential issue is if the ONT transmits outside its allotted time frame, preventing other ONTs from communicating with the optical line terminal (OLT). Early warning signs include elevated level of BIP errors, missed bursts or uncorrected errors that could possibly indicate a failing ONT. In addition, the OLT monitors the traffic and has a mechanism to detect if there is an ONT transmitting outside of its allotted time slot. Leveraging AI/ML across these metrics and looking for patterns over time can help quickly identify and resolve rogue ONT issues and minimize or mitigate down time.
 

Other applications where anomalous behavior detection and mitigation can help include:

• Open loop or closed loop actions to increase or adjust signal strength to optimize experience.
• Improving routing and congestion control at Layer 3.
• Optimizing users’ experiences at the application layer by dynamically allocating bandwidth and prioritizing traffic based on application needs.

Transforming Broadband Assurance With Modern Technology

The digital age requires new broadband assurance measures. Traditional approaches cannot address the complexity and scale of the modern network. Service providers may improve insights, automate procedures, and foresee users’ difficulties by using AI and ML. This proactive approach is crucial for excellent service standards and low operational expenses.

To learn about how to modernize your broadband assurance strategy, download this recent Calix eBook, “Assuring the Broadband Experience by Delivering Operational Efficiency and Service Excellence.”