AI-powered application-based dynamic slicing management method

Abstract

The invention relates to an AI-powered, application-based dynamic slicing management method actively used in 5G and beyond mobile communication systems, aiming to dynamically improve user experience and provide personalized service based on user usage-based actions.

Description

1 TARIFF AI-powered application-based dynamic slicing management method Technical Area The invention enables users to actively use technology in 5G and beyond mobile communication systems. 5 that dynamically improve the user experience, tailored to the user's usage-based actions. AI-powered and application-based dynamic slicing aimed at providing a service. It is related to the management method. State of the Art With the transition of mobile networks to 5G, the issue of "slicing" is emerging. The aim here is to divide the mobile network into virtual slices. Better user 10 The goal is to define customized segments for the experience and provide segment-specific service. Advanced 5G (5G With Advanced, Dynamic Slicing improvement is included. This allows a user Instead of statically receiving service from a single slice, this development allows you to use more slices. They gain the ability to use it. This also opens up opportunities for various application areas and developments. This invention offers the user a better service with the support of artificial intelligence. 15 Dynamic slicing, enhanced for this purpose, will be used. In conclusion, due to the negative aspects described above and the current solutions being the subject of discussion... Due to its shortcomings, an improvement is needed in the relevant technical field. It has been made. Purpose of the Invention 20 The invention was created by drawing inspiration from existing situations and overcoming the aforementioned drawbacks. It aims to solve the problem. The main purpose of the invention is to enable users to change slices in the context of dynamic slicing. It involves taking the logic to a more advanced level and making it more dynamic depending on usage. This development involves an application that analyzes user usage on terminals. 25 This application analyzes the usage hours and days of the terminals. By doing this, a prediction model is created using artificial intelligence. Users can see this information at the relevant times. By anticipating this, the request to join the relevant segment of the core network is made in advance. 2 is being sent. This way, a more seamless and higher quality experience is provided. that is intended. To achieve the objectives described above, artificial intelligence-powered and application-based solutions are needed. It is a dynamic slicing management method based on; its feature is: - the mobile analytics application on the user's terminal, the user's terminal 5 usage-based data regarding the use of the mobile network. conducting usage analyses by examining their movements, - Mobile analytics using data from the aforementioned usage analyses Continuous training of the artificial intelligence model in the application, - Which slice of the terminal does the trained AI model perform best in? 10 predicting the performance it will deliver before starting to use it, - The slice decision is first made to the RAN network, then to the core network. transmission, - the incoming segment decision of the terminal route selection policy in the core network According to the information, the terminal must perform the relevant slice assignment, 15 - after the aforementioned slice assignment, the relevant orchestrations in the core network to be done It includes the steps involved in the process. The structural and characteristic features and all the advantages of the invention are given in the figures below. Thanks to the detailed explanation written with references to these figures, 20 This will be understood, and therefore the evaluation should also take these forms and detailed explanations into consideration. It needs to be done by taking precautions. Figures that will help understand the invention. Figure 1 shows the elements that enable the realization of the method described in the invention. It is a representative drawing showing the relationship between them. 25 Explanation of Part References 1. Terminal 2. Mobile analytics application 3. RAN network 3 4. Core network 5. Terminal route selection policy Detailed Description of the Invention In this detailed explanation, the preferred configurations of the method in question are described only. This is explained to facilitate a better understanding of the subject. 5 Terminals (1) are user equipment for mobile network use. Mobile analysis application (2), artificial intelligence that analyzes user usage on the terminal It is a supported estimation application. RAN Network (3) refers to base stations. (RAN: Radio Access Network). Core network (4), slice The network through which the decision is transmitted is the RAN network (3). Terminal route selection 10 Policy (5) (URSP) is the function that assigns the user slice in the core network (4). In the method described in the invention, the dynamic slicing aspect involves the user slicing the slices. Taking the change logic to a more advanced level and making it more dynamic based on usage. It is intended to be implemented. For this development, the user's terminals (1) A mobile analytics application (2) is used to analyze their usage. 15 The procedural steps of the method described in the invention are as follows: - First, the mobile analytics application (2) on the user terminal (1) of the user Regarding the use of the mobile network via terminal (1), usage-based It analyzes usage patterns by examining their movements. Hourly, daily, and holiday. Usage areas such as days are analyzed. With this data, mobile analytics 20 (2) The artificial intelligence model in the application is continuously trained. - In which slice of terminal (1) does the trained AI model perform better? It predicts the performance it will deliver before it even starts being used. In this way terminal (1) is placed in the relevant slice before initiating a stream. - The slice decision is first to the RAN network (3), then to the core network (4) 25 It is transmitted. - Terminal route selection policy (5) in the core network (4) based on incoming information It performs the relevant slice assignment accordingly. Delay, speed, or upload. Assigning to the most optimal time slots according to their needs. It is carried out. 30 4 - After the aforementioned slice assignment, the relevant (4) in the core network Orchestrations are created. This provides the user with a more seamless and higher-quality experience.

Claims

REQUESTS 1. It is an AI-powered and application-based dynamic slicing management method. feature; - the mobile analytics application (2) on the user terminal (1), the user's Regarding the use of the mobile network via terminal (1), usage-based 5 conducting usage analyses by examining their movements, - Mobile analytics using data from the aforementioned usage analyses (2) continuous training of the artificial intelligence model in the application, - in which slice of terminal (1) the trained AI model performs better Predicting the performance it will deliver before starting to use it, 10 - slice decision first to RAN network (3), then to core network (4) transmission, - the incoming slice of the (5) terminal route selection policy in the core network (4). According to the decision information, the terminal (1) should perform the relevant slice assignment, - after the aforementioned slice assignment, the relevant 15 in the core network (4) orchestration It includes the steps of the process.

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