ARTIFICIAL INTELLIGENCE FOR TELECOMMUNICATIONS

Artificial Intelligence is becoming widely used in a lot of sectors, including the telecommunication industry. Indeed, AI applications in the telecommunication industry are growing more and more, becoming increasingly popular.

Communication service providers have turned to companies that offer highly customized services in order to take part in the digital transformation process that has taken hold in these years. They use AI for many purposes, from improving customer experience to predictive maintenance to enhance network reliability.

In this article we will show you some examples of use cases on how AI has impacted telcommunications enterprises, analyzing its potential.

AI FOR ANOMALY DETECTION

AI models are effective in detecting anomalous conditions. In fact, an AI algorithm can learn from data what normal and abnormal conditions are with the purpose of intercepting deviations from the optimal functioning.

Anomaly detection can include the recognition of hardware or software malfunctionings, exceptional traffic, congestions, intrusions, etc.

In addition, AI models are fundamental in the development of a predictive maintenance framework.
They are able to:

1) Detect unexpected extreme conditions in the network
2) Predict when the next exceptional events will occur
3) Optimize automatic failure recovery

Among the benefits of predictive maintenance, you can find: higher uptime, a better service quality, lower maintenance costs and an improved risk management.

AI FOR NETWORK OPTIMIZATION

Telecom companies can use AI advanced algorithms to detect and predict network anomalies and to fix problems before customers are impacted.

Regarding this, Connection Service Providers operations provide fertile ground for AI models. On the basis of the results you want to achieve, there are several models such as:

– Routing algorithms: they are useful to optimize the dynamic scheduling of the optimal route in the network based on advanced analytics for finding patterns in the data.
– Reinforcement learning: in this case, AI simulates the behavior of the users and optimizes operations accordingly.
– Forecasting models: in the prediction of future traffic loads for planning, AI models perform better than most traditional techniques.

Network operators can leverage on the data-driven insights provided by Artificial Intelligence to reduce latency, optimize load balancing, and improve traffic demand forecasting in order to achieve better customer satisfaction.

AI FOR TRAFFIC CLASSIFICATION AND FLOW CLUSTERING

Another useful feature of AI models is their ability to find hidden patterns in the data.

This ability can be exploited, among other applications, for traffic profiling purposes, allowing:

1) Identification of clusters of sessions based on their characteristics, and strategic management of sessions according to cluster features
2) Identification of traffic-flow-based clusters: identify and classify different network conditions based on the traffic flow characteristics
3) Detection of malicious sessions

Consequently, an algorithm can be trained to identify clusters of observations sharing common characteristics, and to classify new observations.

AI PIPELINE

An AI / ML model is developed through a cyclic process, whose phases are:

By using a software platform which is capable of managing the whole AI pipeline in a simple and automatic way, it is possible to reduce errors and enhance control over the process.
Moreover, a graphical user interface allows to perform advanced pre-processing, model design, training and deployment operations with just a couple of clicks, according to the code-free AI vision.
Finally, automatic data acquisition and model execution ensure that AI runs smoothly without human intervention.

Our software paltform Rebecca AI allows agility in the AI pipeline, great customizability and integration with other platforms and data sources.

OUR EXPERIENCE

The gained extensive experience in Artificial Intelligence, Machine Learning and Advanced Analytics projects, allows us to be flexible in exploring further applications of AI, on the basis of business-relevant information that our customers want to extract from data.

To make this possible, we rely on our software platform Rebecca AI. With Rebecca AI you can build and train your AI models without the need to code, connect and run complex datasets, visualize results in a customizable dashboard and monitor your models performance and malfunctions. You can also share and sell your AI models, as well as buy models created and trained by other users.

What is common across AI projects is the commitment to a scientific rigour and to the methodology and tools employed.

In conclusion, in an increasingly interconnected world, the opportunities of Artificial Intelligence in the telecommunications industry are enormous. Above all, in a highly competitive field such as telecommunications, the AI can only keep on developing and accelerating its growth, considering that its tools and applications are upgraded quickly and become more sophisticated.