Expanded Combinatorial Designs as Tool to Model Network Slicing in 5G

The network slice management function (NSMF) in 5G has a task to configure the network slice instances and to combine network slice subnet instances from the new-generation radio access network and the core network into an end-to-end network slice instance. In this paper, we propose a mathematical model for network slicing based on combinatorial designs such as Latin squares and rectangles and their conjugate forms. We extend those designs with attributes that offer different levels of abstraction. For one set of attributes we prove a stability Lemma for the necessary conditions to reach a stationary ergodic stage. We also introduce a definition of utilization ratio function and offer an algorithm for its maximization. Moreover, we provide algorithms that simulate the work of NSMF with randomized or optimized strategies, and we report the results of our implementation, experiments and simulations for one set of attributes.Comment: Accepted for publication in IEEE Acces

Design and development of handover simulator model in 5G cellular network

In the modern era of technology, the high speed internet is the most important part of human life. The current available network is reckoned to be slow in speed and not be up to snuff for data transmission regarding business applications. The objective of handover mechanism is to reassign the current session handle by internet gadget. The globe needs the next generation high mobility and throughput performance based internet model. This research paper explains the proposed method of design and development for handover based 5G cellular network. In comparison to the traditional method, we propose to control the handovers between base-stations using a concentric method. The channel simulator is applied over the range of the frequencies from 500 MHz to 150 GHz and radio frequency for the 700 MHz bandwidth. The performance of the simulation system is calculated on the basis of handover preparation and completion time regarding base station as well as number of users. From this experiment we achieve the 7.08 ms handover preparation time and 9.98 ms handover completion time. The author recommended the minimum handover completion time, perform the high speed for 5G cellular networks

The Network Slicing and Performance Analysis of 6G Networks using Machine Learning

6G technology is designed to provide users with faster and more reliable data  transfer as compared to the current 5G technology. 6G is rapidly evolving and provides a large bandwidth, even in underserved areas. This technology is extremely anticipated and is currently booming for its ability to deliver massive network capacity, low latency, and a highly improved user experience. Its scope is immense, and it’s designed to connect everyone and everything in the world. It includes new deployment models and services with extended user capacity. This study proposes a network slicing simulator that uses hardcoded base station coordinates to randomly distribute client locations to help analyse the performance of a particular base station architecture. When a client wants to locate the closest base station, it queries the simulator, which stores base station coordinates in a K-Dimensional tree. Throughout the simulation, the user follows a pattern that continues until the time limit is achieved. It gauges multiple statistics such as client connection ratio, client count per second, Client count per slice, latency, and the new location of the client. The K-D tree handover algorithm proposed here allows the user to connect to the nearest base stations after fulfilling the required criteria. This algorithm ensures the quality requirements and decides among the base stations the user connects to

Empowering the Internet of Vehicles with Multi-RAT 5G Network Slicing

Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.EC/H2020/825496/EU/5G for cooperative & connected automated MOBIility on X-border corridors/5G-MOBI

Quality of service evaluation based on network slicing for software-defined 5G systems

Este artículo presenta la evaluación de los parámetros de calidad del servicio proporcionadas por la segmentación de recursos para redes 5G basadas en un entorno de red definido por software. El controlador Floodlight realizó decisiones de asignación de ancho de banda definiendo segmentos de red a perfiles de usuario en topologías particulares. El objetivo es controlar los recursos de ancho de banda que permiten garantizar valores de latencia y confiabilidad de acuerdo con el tipo de servicio en una red 5G. De esta forma, fue posible demostrar la versatilidad y escalabilidad del controlador Floodlight, que redujo la tasa de pérdida en un 10% en una red congestionada y permitió retrasos de menos de 700ms en aplicaciones como VoIP y transmisión de video compartiendo un canal con una velocidad de bits limitada de 5 Mbps.This paper presents the evaluation of the quality of service parameters provided by the network slicing approach for 5G networks based on a software-defined networking environment. The open source controller Floodlight made bandwidth allocation decisions by assigning network slices to user profiles on particular topologies. The objective is to control the bandwidth resources that allow to guarantee latency and reliability values according to the type of service in a sliced 5G network. Thus, it was possible to demonstrate the versatility and scalability of the Floodlight controller, which reduced the loss rate by 10% in a congested network and ensured delays of less than 700ms in applications such as VoIP and video streaming sharing a channel with a limited bit rate of 5 Mbps