The ever-increasing demand of mobile internet traffic is pushing operators to look for solutions to increase the available bandwidth per user and per unit of area. At the same time, they need to reduce the load in the core network at a reasonable cost in their future 5G deployments. Today's trend points to the deployment of extremely dense networks in order to provide ubiquitous connectivity at high data rates. However, this is hard to couple with the current mobile networks' architecture, which is heavily centralized, posing difficult challenges when coping with the foreseen explosion of mobile data. Additionally, future 5G networks will exhibit disparate types of services, posing different connectivity requirements. Distributed Mobility Management is emerging as a valid framework to design future mobile network architectures, taking into account the requirements for large traffic in the core and the rise of the extremely dense wireless access networks. In this article, we discuss the adoption of a Distributed Mobility Management approach for mobile networks, and analyze the operation of the main existing solutions proposed so far, including a first practical evaluation based on experiments with real Linux-based prototype implementations.
Multi-access Edge Computing (MEC) technologies bring important improvements in terms of network bandwidth, latency and use of context information, critical for services like multimedia streaming, augmented and virtual reality. In future deployments, operators will need to decide how many MEC Points of Presence (PoPs) are needed and where to deploy them, also considering the number of base stations needed to support the expected traffic. This article presents an application of inhomogeneous Poisson point processes with hard-core repulsion to model feasible MEC infrastructure deployments. With the presented methodology a mobile network operator knows where to locate the MEC PoPs and associated base stations to support a given set of services. We evaluate our model with simulations in realistic scenarios, namely Madrid city center, an industrial area, and a rural area.
5G requires a redesign of transport networks in order to feed the increasingly bandwidth hungry Radio Access Networks and to benefit of the performance/cost efficiency provided by the integration of both backhaul and fronthaul segments over the same transport substrate as well as the incorporation of Cloud RAN architectures. In addition, to increase its usage and costefficiency, this new transport network should allow simultaneous use by different tenants, e.g. MVNOs, OTTs, or vertical industries. This paper presents the 5G Transport Network architecture designed in the 5G-Crosshaul project to address this challenge. An SDN/NFV-based control plane has been designed that enables multi-tenancy through network slicing. The proposed solution allows for a flexible and efficient allocation of transport network resources (networking and computing) to multiple tenants by leveraging on widespread architectural frameworks for NFV (ETSI NFV) and SDN (e.g., Open Daylight and ONOS).
Edge computing and network slicing might be considered as main pillars of the upcoming 5G systems as they inject flexibility in the network management operations. While one prominent architectural framework for edge computing has been recently defined by the ETSI standard organization, namely Multiaccess Edge Computing (MEC), network slicing has reached its momentum by fostering interest in different standardization bodies and fora. To better understand how such distinct network slicing definitions impact on the standardized MEC framework, ETSI has recently published a study on the matter. In this paper, we first overview with a comprehensive analysis the different network slicing concepts and their relationship. Then, we elaborate on the ETSI study to provide an integrated view of network slicing technology within the context of MEC. Finally, we report on the open challenges in the ETSI study and we propose two solutions to evolve the current MEC framework towards end-to-end multi-slice support and efficient multi-tenant inter-slice communication in 5G deployments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.