EP-4740398-A1 - METHOD FOR PREDICTING A VARIATION IN THE QUALITY OF SERVICE IN A CELLULAR TELECOMMUNICATION NETWORK, CORRESPONDING PREDICTION DEVICE AND CORRESPONDING COMPUTER PROGRAM

Abstract

The invention relates to a method for predicting a variation in the quality of service (QoS) in a cellular telecommunication network comprising at least one base station to which a user device is connected. Such a method comprises: - collecting (DUE) at least one item of location information (IL) comprising an identifier of the base station; - selecting (S_MOD), from among a set of key performance indicators (KPI) of the network, at least one indicator of interest representative of the service quality; - predicting (C_MOD), via machine learning, a future value of the indicator of interest on the basis of at least one temporal trajectory of the indicator of interest, the temporal trajectory being representative of past values of the indicator of interest collected by the prediction device; - transmitting (ADAPT), within the network, a message for adjusting at least one parameter of the network on the basis of the predicted future value.

Inventors

• DRISSI, Maroua
• SIMON, OLIVIER

Assignees

• ORANGE

Dates

Publication Date

20260513

Application Date

20240618

Claims (11)

1. 1. Method for predicting a variation of a quality of service (QoS) in a cellular telecommunications network comprising at least one base station to which at least one user equipment is connected, characterized in that said method is implemented by a prediction device and comprises: a collection (DUE) of at least one location information (IL) comprising an identifier of said at least one base station to which said at least one user equipment is connected, a selection (S_MOD), from a set of key performance indicators (KPI) of said network, of at least one key performance indicator of interest representative of said quality of service provided by said at least one base station to said at least one user equipment, a prediction (C_MOD) by automatic learning of a future value of said at least one key performance indicator of interest from at least one time trajectory of said at least one key performance indicator of interest, said at least one time trajectory being representative of past values of said at least one key performance indicator of interest, collected by said prediction device, a transmission (ADAPT), within said cellular telecommunications network, of a message for adjusting at least one parameter of said cellular telecommunications network based on said predicted future value.
2. 2. Prediction method according to claim 1, characterized in that it further comprises: a determination of a user profile based on said at least one location information and at least one service nature information.
3. 3. Prediction method according to claim 2, characterized in that said selection of said at least one key performance indicator of interest takes into account said user profile and past values of said key network performance indicators, collected by said prediction device.
4. 4. Prediction method according to claim 3, characterized in that it further comprises a generation of at least one prediction model associating said at least one user profile with said at least one temporal trajectory of said at least one key performance indicator of interest.
5. 5. Prediction method according to claim 4, characterized in that said prediction implements an artificial intelligence module configured to implement said at least one prediction model.
6. 6. Device for predicting a variation in a quality of service in a cellular telecommunications network comprising at least one base station to which at least one user equipment is connected, characterized in that said prediction device is configured to: collect at least one location information comprising an identifier of said at least one base station to which said at least one user equipment is connected, select, from a set of key performance indicators of said network, at least one key performance indicator of interest representative of said quality of service provided by said at least one base station to said at least one user equipment, predict by machine learning a future value of said at least one key performance indicator of interest from at least one time trajectory of said at least one key performance indicator of interest, said at least one time trajectory being representative of past values of said at least one key performance indicator of interest, collected by said prediction device, transmit within said cellular telecommunications network a message for adjusting at least one parameter of said cellular telecommunications network as a function of said predicted future value.
7. 7. Prediction device according to claim 6, characterized in that it is further configured to determine a user profile based on said at least one location information and at least one service nature information.
8. 8. Prediction device according to claim 7, characterized in that said selection of said at least one key performance indicator of interest takes into account said user profile and past values of said key network performance indicators, collected by said prediction device.
9. 9. Prediction device according to claim 8, characterized in that it implements a machine learning module configured to generate and implement at least one prediction model associating said at least one user profile with said at least one temporal trajectory of said at least one key performance indicator of interest.
10. 10. Computer program product comprising program code instructions for implementing a method according to any one of claims 1 to 5, when executed by a processor.
11. 11. A computer-readable recording medium comprising program code instructions which, when executed by a processor, cause the processor to implement a method according to any one of claims 1 to 5.

Description

DESCRIPTION Method for predicting a variation in quality of service in a cellular telecommunications network, corresponding prediction device and computer program. Field of invention The field of the invention is that of cellular radiocommunications. In particular, the proposed solution applies in particular, but not exclusively, in the context of LTE/4G (in English "Long Term Evolution") or 5G NR (in English "New Radio") mobile networks. More particularly, the invention relates to the use of a machine learning model for predicting the quality of service (QoS) in a cellular telecommunications network, also called a mobile network. Previous Art Historically, cellular telecommunications networks, or mobile networks, were designed to provide a basic level of service, such as voice and text communication. Over the past few decades, technological advances in user equipment (e.g., smartphones, tablets, laptops, etc.) and changing user expectations have led to a shift in how users interact with their devices and the Internet. Indeed, users now expect mobile networks to provide a diverse range of services, including high-speed Internet, streaming video and real-time gaming. One consequence of this evolution is the adaptation of mobile networks. In particular, to meet new user expectations, mobile network operators must be able to differentiate the configuration of the mobile network according to specific services and user needs. This means that mobile network operators must be able to adapt network performance according to the type of service provided and the user's needs. For example, a mobile network may need to prioritize a streaming video service over voice communication or provide faster speeds for online gaming applications. To enable mobile networks to adapt, new technologies have emerged such as: real-time analytics capabilities based on machine learning and new radio communication technologies such as 5G. These new technologies have enabled networks mobile to provide new capabilities (e.g. data storage or analysis) and new services to users (e.g. high-speed Internet). One of the key enablers of this transformation of mobile networks is the ability to store and analyze large volumes of data in real time using machine learning. In fact, machine learning models enable mobile networks to detect patterns and anomalies in network traffic and adjust their parameters accordingly. In other words, by analyzing network data at the time of anomalies observed in the past, an artificial intelligence model can infer the factors that cause them and therefore predict them in the future. In addition, the ability to differentiate mobile network parameters based on specific service and user needs has become a key requirement for mobile network operators. Furthermore, the emergence of new capabilities (such as edge computing) linked to the deployment of 5G networks is expected to bring significant improvements to mobile networks. For example, 5G networks are designed to provide ultra-reliable low latency communication (URLLC), which enables new use cases such as autonomous vehicles, remote surgery and real-time monitoring of critical infrastructure (e.g. automated factories). Furthermore, the implementation of 5G networks also leads to the development of network slicing (or “network slicing”), which allows mobile network operators to create multiple virtual mobile networks on a single physical mobile network infrastructure. Each mobile network slice can be customized with specific features and service levels to meet the needs of different users and use cases. Thus, the implementation of 5G networks opens new opportunities for businesses and individuals to develop and use a variety of innovative applications. However, the most demanding 5G network use cases will require Quality of Service (QoS) control to ensure that they meet the required performance levels for each user and for each service. Quality of service monitoring encompasses several critical parameters, including latency, jitter, throughput, and radio signal quality metrics such as RSRP (Reference Signal Received Power) and RSSI (Received Signal Strength Indicator). These parameters are representative of network performance and therefore determine the level of quality of service that can be provided on the network. As such, it is essential that they are carefully managed and monitored. Implementing Network Slicing technology can enable QoS control in 5G networks, providing different QoS levels for different network slices. In this way, network operators can ensure that the most demanding use cases receive the required level of service while optimizing the use of radio resources. However, one of the significant challenges that mobile network operators face in providing QoS control for 5G networks is the constantly changing radio conditions. As users move to different areas (or cells) of the mobile network, they experience different radio conditions. Mobile network op