# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 1 Introduction  
Convergence among networks of different technologies has become a reality. The processing power of mobile devices and the diversity of services that can be used with them guide the way that access technology infrastructures are modeled. The term convergence hence refers to the possibility of providing user access through different technologies, enabling the use of various services such as voice, video, and data in general [1]. The application of the Network Slicing (NS) concept provides an avenue to new mobile network solutions, including 5G networks (Fifth Generation Mobile Networks) and future communication systems due to stricter requirements in terms of their Key Performance Indicators (KPIs)--isolation, latency, mobility, peak data rate, and so on, as shown in Figure 1. Although several proposals point out paths in domains that involve heterogeneous technologies, it is still not possible to aggregate several functionalities in a single and fully functional approach to set the operation and management mechanisms of each slice, or to provide subsidies for scalability, orchestration, and support for decision making [1,2]. The task of selecting a Radio Access Network (RAN) in an environment of heterogeneous technologies is difficult since operators can provide specific types of slices directly to meet the requirements of an application or multiple slices for the requirements of the same user. Therefore, there is still no solution or technique that understands all the aspects and mechanisms of access to these technologies [3,4]. The implementation of new selection techniques becomes necessary due to the demand in the growing use of vehicular networks, patient monitoring, smart cities, and Internet of Things (IoT), among other technologies and scenarios involving network convergence, mobility management, and service continuity in 5G networks and beyond [5, 6, 7, 8, 9, 10, 11]. The main issues arising from this process are as follows: How to choose the best slice? Do the selected slices provide the necessary requirements for the user? Are the selection criteria modeled in a generic way, independently of the access technology? In this work, a study was conducted on the use of computational tools for classifying slices, to support the decision-making process using aids for our proposed Network Slice Selection Function Decision-Aid Framework (NSSF DAF). Four strategies were suggested, namely, VIKOR (Visekriterijumska Optimizacija i Kompromisno Resenje), COPRAS (Complex Proportional Assessment), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Promethee II (Preference Ranking Organization Method for Enrichment Evaluations) [13]. The general objective was to verify the similarity among these methods and applications in the slice classification and selection processes considering a specific scenario and then propose the framework that provides the best experience to the user. Machine Learning (ML) with the K-means clustering algorithm was selected, adopting a hybrid solution to the implementation and operation of the Network Slice Selection Function (NSSF) service in a multi-domain slicing environments of heterogeneous mobile networks. To validate the framework, testbeds were conducted to map the necessary Quality of Service (QoS) requirements. Figure 1: 5G vs. 6G Network Requirements. Based on [12]. Thus, a new approach that employs techniques aimed at the integration and interoperability between RAN or Open Radio Access Network (O-RAN), Edge and Core networks, based on an efficient and robust Slice Selection service (virtual network selection), under an architecture that provides compatibility with the standards specification in progress, is a promising solution for 5G mobile networks.