Is It Time to Modernize Your Approach to Broadband Assurance

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.

Broadband Assurance: Move Beyond Speed Testing

Broadband assurance relies heavily on active speed, latency, jitter and packet-loss testing to indicate service-level performance. These characteristics are critical in delivering reliable broadband.

Modern broadband assurance leverages various techniques and technologies, including static rules, threshold-based anomaly detection, artificial intelligence (AI), and machine learning (ML), to expand beyond testing and measurement to an approach comprising network performance, customer happiness, predictive maintenance, and proactive problem-solving. This holistic approach helps broadband service providers (BSPs) exceed customers' expectations in a competitive market.

Transforming Broadband Assurance with Modern Technology

The digital age demands new broadband assurance measures. Traditional approaches, if not updated, will soon be unable to cope with the complexity and scale of the modern network. BSPs have a unique opportunity to enhance insights, automate procedures, and anticipate users' issues by harnessing the power of supervised and unsupervised ML algorithms and generative AI-based recommendations. This proactive approach is not just beneficial, it's crucial for maintaining excellent service standards and minimizing operational expenses. The potential risks of not modernizing are too significant to ignore.

Automation Enhances Operations

Automation enhanced by pattern recognition, real-time analytics, and generative AI-driven proactive recommendations delivered to the operations team promises to transform broadband assurance. These modern approaches let networks self-optimize and repair, boosting performance and reliability. They make it possible to analyze massive amounts of data, speed up issue resolution, and improve service quality by assisting operations teams in spotting abnormalities they are currently unable to do because of time constraints and a lack of tools.

Network Anomaly Detection and Prediction

Reinforcement learning algorithms can detect anomalous traffic patterns, device faults, and security breaches. Operators can prevent network difficulties by using these algorithms to discover patterns in past data. AI-powered predictive maintenance can predict equipment failures and plan prompt repairs, reducing downtime and enhancing network reliability.

Network Optimization in the Future

Imagine what's possible for network optimization. For example, reinforcement, supervised, and unsupervised ML methods can optimize network performance.

• Dynamically allocating bandwidth to ONTs based on their performance (i.e. positive reward for high throughput and low latency vs. negative reward for low throughput and high latency).
  
  
• Prioritizing certain types of application traffic (i.e. gaming or streaming) over less time-sensitive file download or email traffic.
  
  
• Supervised ML algorithm predicts a likelihood of network congestion or failure happening within a specific window of time into the future.
  
  
• Unsupervised ML algorithm finds an anomaly with an ONT having sudden low signal strength and high error rates which may have been caused by fiber damage.

Integrating modern broadband assurance approaches into operational workflows can help at all layers with network and service performance.

• Increase signal strength.
  
  
• Reduce interference at Layer 1.
  
  
• Improve routing and congestion control at Layer 3.
  
  
• Optimize users' experiences at the application layer by dynamically allocating bandwidth and prioritizing traffic based on application needs.
  
  
• Ensure service continuity.
  
  
• Detect failure points and offer redundancy for such networks.
  
  
• Monitor network health and performance to activate failover mechanisms.
  
  
• Reroute traffic to preserve service continuity.

The benefits can also be dramatic for operations teams. It can help already resource-constrained teams scale by allowing them to focus on mission-critical issues and letting AI/ML-driven workflow automation actions take care of repetitive, less complex issues. It also helps less-tenured team members be more productive and technically equipped.

Finally, it can also help operations teams shift their focus from managing a network to delivering an exceptional service and subscriber experience.