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

Comarch Telecoms

22 Oct 201901:52

Summary

TLDRThe 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.

Outlines

00:00

🚀 Managing Telecom Networks with AI

This paragraph compares telecom networks to urban communication systems in terms of complexity and traffic volume. It highlights the challenges faced during high traffic periods, where traditional network management strategies may not suffice. The text emphasizes the limitations of human operators under pressure and the potential of artificial intelligence (AI) to provide a broader perspective and more precise observation of network capabilities. AI's ability to quickly respond to anomalies and distinguish between temporary fluctuations and actual issues is highlighted. The paragraph also mentions the collaborative potential of different AI models to enhance decision-making and the continued role of human operators in strategy and policy during network impediments.

Mindmap

Keywords

💡Telecom networks

Telecom networks refer to the complex systems of telecommunication that facilitate the transmission of data and voice across various distances. In the context of the video, they are likened to the communication systems in big cities, highlighting both their complexity and the high volume of traffic they handle. The comparison is used to illustrate how data packets and phone conversations move within these networks, similar to how cars navigate city streets and highways.

💡Data packets

Data packets are units of data that are transmitted over a network. They are the basic building blocks of internet communication. In the video, data packets are compared to cars in a city, emphasizing how they move within the telecom systems. This analogy helps to visualize the flow of information and the potential for congestion, which is a central theme in discussing network management.

💡Traffic volume

Traffic volume in the context of telecom networks refers to the amount of data or the number of calls being transmitted at a given time. The video uses this term to draw a parallel with the volume of car traffic in cities, which can become concentrated and lead to congestion. High traffic volume is a key factor in the challenges faced by network operators, as it can strain the system and affect performance.

💡Emergency procedures

Emergency procedures are predefined actions taken to manage unexpected or critical situations. In the video, they are mentioned as the standard response when the network faces an influx of data that exceeds usual capacity. These procedures are crucial for maintaining network stability but may not always be sufficient, especially when operators are under time pressure and have limited data perception.

💡Artificial Intelligence (AI)

Artificial Intelligence is the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The video highlights AI's role in telecom network management, where it can observe all bandwidth and expected values with precision, allowing for quick responses to anomalies. AI's broader perception and ability to compare all available parameters simultaneously make it a valuable tool in network optimization.

💡Anomalies

Anomalies in the context of telecom networks refer to deviations from the normal or expected patterns of data flow or system performance. The video emphasizes the importance of AI in quickly identifying and responding to these anomalies at their source, which can prevent the escalation of issues and the waste of resources in troubleshooting.

💡Bandwidth

Bandwidth is the maximum rate of data transfer across a given path. In the video, it is mentioned as one of the key parameters that AI can observe and manage in telecom networks. Efficient bandwidth management is essential for ensuring that data packets and phone conversations can move smoothly without causing traffic jams within the network.

💡Inference models

Inference models in AI are algorithms used to make predictions or decisions based on data. The video suggests that different AI systems can work together using various inference models to increase the probability of choosing the best solution for network management. This collaboration among AI systems enhances the overall effectiveness of the network's response to traffic and anomalies.

💡Operator

An operator in the context of telecom networks is a person responsible for managing and overseeing the network's performance. The video notes that even the most experienced operators can be limited by their human perspective and the pressure of time, which may lead to suboptimal decisions. However, with AI assistance, operators can focus on strategy and policy, leveraging AI's capabilities to handle the technical aspects of network management.

💡Traffic jams

In the video, traffic jams are used metaphorically to describe the congestion that can occur in telecom networks when there is too much data or too many calls being processed at once. The analogy is drawn from city traffic to illustrate the negative impact of such congestion on network performance. The video suggests that with AI, these 'traffic jams' could be prevented by managing the network more effectively from the outset.

Highlights

Telecom networks are compared to city communication systems in complexity and traffic volume.

Data packets and phone conversations move inside telecom systems similarly to cars in cities.

Traffic in telecom systems can become concentrated in sensitive places, disrupting fluid movement.

Standard network maintenance rules may fail under the pressure of large amounts of incoming data.

Emergency procedures are implemented during situations of high data concentration.

Even experienced operators can struggle with limited data perception and time pressure.

Narrow perspective and inability to connect distant situations can lead to non-optimum decisions.

AI-based solutions are invested in to overcome human limitations in network management.

Artificial intelligence allows for precise observation of bandwidth and expected values.

AI can quickly respond to anomalies in the source, saving time and resources.

AI has a broader perception than humans, comparing all available parameters simultaneously.

AI can distinguish between momentary parameter fluctuations and real anomalies.

Different AI systems can collaborate using various models of inference to increase the probability of the best solution.

Experienced operators can still apply strategy and policy during network impediments.

AI's broader perception could prevent network 'traffic jams' if implemented from the start.