Artificial Intelligence and Machine Learning for 5G Network Monitoring – COMARCH

Summary

The video script likens telecom networks to complex urban traffic systems, highlighting the challenges of managing high data volumes akin to city congestion. It emphasizes the limitations of traditional network management in handling concentrated traffic and the potential for human error under pressure. Comarch's AI-driven solutions offer a remedy, enabling precise monitoring and rapid response to anomalies, thus preventing network 'traffic jams'. By harnessing AI's broad perception and collaborative capabilities, the system optimizes decision-making, allowing operators to implement strategies effectively.

Takeaways

• 🌐 Telecom networks are intricate systems that can be likened to urban traffic systems in complexity and volume.
• 🚦 Data packets and phone conversations traverse telecom networks similarly to how cars navigate city streets and highways.
• 🚧 Traffic in telecom networks can become congested, just like in city traffic, requiring emergency management procedures.
• 🤖 AI can provide a more comprehensive view of network operations, unlike human operators who may have limited data perception.
• 🛠️ In high-pressure situations, human operators might make suboptimal decisions due to time constraints and narrow perspectives.
• 🔍 AI enables precise monitoring of bandwidth and expected values, allowing for quick responses to network anomalies.
• 💡 AI's broader perception can differentiate between temporary parameter spikes and genuine anomalies, preventing unnecessary troubleshooting.
• 🤝 Multiple AI systems can collaborate, using various inference models to enhance the likelihood of selecting the best solution.
• 📈 AI can work in tandem with human operators, allowing them to focus on strategy and policy during network disruptions.
• 🚀 Comarch's investment in AI-driven solutions aims to optimize network management and prevent traffic congestion, akin to reducing traffic jams in a city.

Q & A

How are telecom networks compared to communication systems in big cities?

Telecom networks are compared to communication systems in big cities in terms of complexity and traffic volume, with data packets and phone conversations moving inside telecom systems similarly to how cars move in cities.

What challenges arise when traffic is concentrated in sensitive places within telecom networks?

When traffic is concentrated in sensitive places, the usual rules for maintaining a network can't handle the large amount of incoming data, leading to potential network congestion and inefficiencies.

Why do emergency procedures need to be implemented in telecom networks during high traffic situations?

Emergency procedures are necessary to manage the influx of data and maintain network stability during high traffic situations, as standard maintenance practices may not be sufficient.

How can human operators' limitations affect the management of telecom networks during emergencies?

Human operators, even with the best intentions, can be limited by their data perception and time pressure, which may lead to suboptimal decisions and exacerbate network issues.

What role does artificial intelligence play in improving telecom network management?

Artificial intelligence allows for precise observation of bandwidth and expected values, enabling quick responses to anomalies and reducing the time and money spent on troubleshooting.

How does AI enhance the detection of anomalies in telecom networks?

AI has a broader perception than humans, allowing it to compare all available parameters simultaneously and distinguish between momentary fluctuations and actual anomalies.

What is the advantage of different AI systems working together in telecom networks?

Different AI systems working together can increase the probability of choosing the best solution by using various models of inference, thus improving the overall decision-making process.

How can experienced operators still contribute to telecom network management with AI involvement?

Experienced operators can fulfill strategic and policy roles during impediments, leveraging AI for operational tasks while focusing on higher-level decision-making.

What is the potential impact of AI on preventing 'traffic jams' in telecom networks?

If AI were involved from the beginning, it could potentially prevent 'traffic jams' in telecom networks by efficiently managing data flow and identifying issues before they escalate.

How does AI's broader perception compare to human operators in terms of network management?

AI's broader perception allows it to see the full view of the network's capabilities, unlike human operators who might have a narrow perspective due to limited data perception.