How is AI transforming the telecommunications sector?

AI is a branch of computer science that simulates human intelligence through the processing of very large datasets. It is being implemented in economic, social, and technological sectors, and the telecommunications sector is no exception to this reality.

It is developed through the collection and analysis of data. Using algorithms and mathematical methods, AI can extract patterns that then allow it to simulate human intelligence. The more data is entered and the more varied it is, the more information it will have to generate more effective solutions.

AI enhances the performance of many traditional processes, which is why several companies are implementing it to improve the quality of their products and services.

AI uses various techniques, some of which are briefly described below:

Machine learning (ML)

ML is a branch of machine learning. Through this technique, using algorithms and classical models, computers are trained and "learn." Data is processed logically, and certain patterns are identified, leading to the generation of "intelligence." This technology uses classical techniques, such as linear regression, for example.

Deep learning (DL)

DL is a subcategory within machine learning. It involves a deeper approach to ML: more sophisticated tools are used, such as artificial neural networks, which produce better results.

Uses of AI in telecommunications.

Let’s look at some potential applications that AI offers in the telecommunications sector.

Network monitoring and management.

Users and traffic are growing increasingly, and telecommunications networks have become more complex. Therefore, improving network management and efficiency has become a priority for all telecom operators.

AI plays a crucial role in analyzing network issues and can generate improvements by preventing problems before they occur, in an automated manner.

For example, engineers at Orange in France developed an AI-based system that anticipates a communication network congestion 30 minutes in advance by predicting the evolution of several performance parameters or indicators. It has an 80% success rate and allows them to prevent issues, saving time for those managing the network.

"Generally, we consider performance indicators individually. With this AI solution, we now take into account the impact of multiple variables at the same time," says Sylvain Allio, one of the engineers at Orange.

As Sylvain states, AI provides a greater understanding of the network than was previously available. When a network issue is predicted, we focus on the equipment causing the problems or the neighboring equipment. Thanks to AI, not only can future issues be predicted, but the elements causing them can also be identified, allowing for proactive measures to prevent them before they occur.

Energy consumption and network efficiency

In addition to monitoring or improving network management and stability, another application of AI in telecommunications networks is improving the energy efficiency of the networks.

By collecting data on network consumption and the capacity provided to customers, AI techniques can be used to evaluate the efficiency of the network and take action if necessary. In this regard, Orange has also committed to reducing its carbon footprint emissions by 30% before 2025, something it plans to achieve by using AI.

The company Ericsson also applies machine learning (ML) techniques to save energy. They manage the energy-saving mode of radio transmitters when it is predicted that user traffic will fall below a certain value. This technique, using ML, generated a 14% energy consumption saving per site, surpassing manual management.

Threat detection with AI.

Another topic that is becoming very common in telecommunications networks is cybersecurity. Nowadays, it is crucial to be able to detect threats in time and protect customers' confidential information from digital attacks.

Many companies have been able to tackle these attacks by using methods powered by automation, AI, and machine learning. This has helped them detect suspicious activities more effectively and in real-time.

AI enables the detection of internal attackers, suspicious IP addresses, and malicious files within seconds. The continuous learning of AI and the vast amount of data it collects facilitate the identification of security threats, reducing detection and attack response times.

For example, the company Nokia has created a software-based system called Deepfield Defender. This software uses an updated data source called Deepfield Secure Genome (patented by Nokia), which gathers information from the internet (IP addresses, internet traffic) and, using AI techniques, classifies and divides these flows into different security categories. This data source is aware of previous attacks and unsafe servers. With that information, along with additional data obtained from the network, Deepfield Defender can detect DDoS attacks more quickly.

What are these incidents about? DDoS (Distributed Denial of Service) is an attack aimed at a specific network, server, or service. In this attack, the target network, including the server or service that makes it up, is flooded with internet traffic originating from distributed sources but coordinated by the attacker. This causes a significant disruption in the usual traffic of the affected network.

Improvements in user experience

Telecommunications operators can also use AI to improve the user experience.

Based on customer data, telecom operators can apply AI to detect their preferences and offer personalized services. For example, AI can be used to identify trends and predict consumption patterns to recommend a specific commercial package to the user or customer, considering their current or future needs, with different connection speeds or call quality levels.

AI: A Differentiator for the Industry?

Given that telecommunications networks have an increasing number of users and traffic, it is crucial to have tools that allow for monitoring, improving efficiency, preventing attacks, and retaining customers.

Algorithms for network management, improving energy efficiency, or making networks more secure and providing better services to customers are just a few examples of the many applications AI could have in telecommunications networks, making them more stable, secure, and sustainable.

Therefore, the application of AI can be key for telecommunications operators as a way to improve service quality and customer insight, and it will be a differentiator in the future when choosing an operator or a brand.