1,299 research outputs found
Benefits and limits of machine learning for the implicit coordination on SON functions
Bedingt durch die Einführung neuer Netzfunktionen in den Mobilfunknetzen der nächsten Generation, z. B. Slicing oder Mehrantennensysteme, sowie durch die Koexistenz mehrerer Funkzugangstechnologien, werden die Optimierungsaufgaben äußerst komplex und erhöhen die OPEX (OPerational EXpenditures). Um den Nutzern Dienste mit wettbewerbsfähiger Dienstgüte (QoS) zu bieten und gleichzeitig die Betriebskosten niedrig zu halten, wurde von den Standardisierungsgremien das Konzept des selbstorganisierenden Netzes (SON) eingeführt, um das Netzmanagement um eine Automatisierungsebene zu erweitern. Es wurden dafür mehrere SON-Funktionen (SFs) vorgeschlagen, um einen bestimmten Netzbereich, wie Abdeckung oder Kapazität, zu optimieren. Bei dem konventionellen Entwurf der SFs wurde jede Funktion als Regler mit geschlossenem Regelkreis konzipiert, der ein lokales Ziel durch die Einstellung bestimmter Netzwerkparameter optimiert. Die Beziehung zwischen mehreren SFs wurde dabei jedoch bis zu einem gewissen Grad vernachlässigt. Daher treten viele widersprüchliche Szenarien auf, wenn mehrere SFs in einem mobilen Netzwerk instanziiert werden. Solche widersprüchlichen Funktionen in den Netzen verschlechtern die QoS der Benutzer und beeinträchtigen die Signalisierungsressourcen im Netz. Es wird daher erwartet, dass eine existierende Koordinierungsschicht (die auch eine Entität im Netz sein könnte) die Konflikte zwischen SFs lösen kann. Da diese Funktionen jedoch eng miteinander verknüpft sind, ist es schwierig, ihre Interaktionen und Abhängigkeiten in einer abgeschlossenen Form zu modellieren. Daher wird maschinelles Lernen vorgeschlagen, um eine gemeinsame Optimierung eines globalen Leistungsindikators (Key Performance Indicator, KPI) so voranzubringen, dass die komplizierten Beziehungen zwischen den Funktionen verborgen bleiben. Wir nennen diesen Ansatz: implizite Koordination. Im ersten Teil dieser Arbeit schlagen wir eine zentralisierte, implizite und auf maschinellem Lernen basierende Koordination vor und wenden sie auf die Koordination zweier etablierter SFs an: Mobility Robustness Optimization (MRO) und Mobility Load Balancing (MLB). Anschließend gestalten wir die Lösung dateneffizienter (d. h. wir erreichen die gleiche Modellleistung mit weniger Trainingsdaten), indem wir eine geschlossene Modellierung einbetten, um einen Teil des optimalen Parametersatzes zu finden. Wir nennen dies einen "hybriden Ansatz". Mit dem hybriden Ansatz untersuchen wir den Konflikt zwischen MLB und Coverage and Capacity Optimization (CCO) Funktionen. Dann wenden wir ihn auf die Koordinierung zwischen MLB, Inter-Cell Interference Coordination (ICIC) und Energy Savings (ES) Funktionen an. Schließlich stellen wir eine Möglichkeit vor, MRO formal in den hybriden Ansatz einzubeziehen, und zeigen, wie der Rahmen erweitert werden kann, um anspruchsvolle Netzwerkszenarien wie Ultra-Reliable Low Latency Communications (URLLC) abzudecken.Due to the introduction of new network functionalities in next-generation mobile networks, e.g., slicing or multi-antenna systems, as well as the coexistence of multiple radio access technologies, the optimization tasks become extremely complex, increasing the OPEX (OPerational EXpenditures). In order to provide services to the users with competitive Quality of Service (QoS) while keeping low operational costs, the Self-Organizing Network (SON) concept was introduced by the standardization bodies to add an automation layer to the network management. Thus, multiple SON functions (SFs) were proposed to optimize a specific network domain, like coverage or capacity. The conventional design of SFs conceived each function as a closed-loop controller optimizing a local objective by tuning specific network parameters. However, the relationship among multiple SFs was neglected to some extent. Therefore, many conflicting scenarios appear when multiple SFs are instantiated in a mobile network. Having conflicting functions in the networks deteriorates the users’ QoS and affects the signaling resources in the network. Thus, it is expected to have a coordination layer (which could also be an entity in the network), conciliating the conflicts between SFs. Nevertheless, due to interleaved linkage among those functions, it is complex to model their interactions and dependencies in a closed form. Thus, machine learning is proposed to drive a joint optimization of a global Key Performance Indicator (KPI), hiding the intricate relationships between functions. We call this approach: implicit coordination. In the first part of this thesis, we propose a centralized, fully-implicit coordination approach based on machine learning (ML), and apply it to the coordination of two well-established SFs: Mobility Robustness Optimization (MRO) and Mobility Load Balancing (MLB). We find that this approach can be applied as long as the coordination problem is decomposed into three functional planes: controllable, environmental, and utility planes. However, the fully-implicit coordination comes at a high cost: it requires a large amount of data to train the ML models. To improve the data efficiency of our approach (i.e., achieving good model performance with less training data), we propose a hybrid approach, which mixes ML with closed-form models. With the hybrid approach, we study the conflict between MLB and Coverage and Capacity Optimization (CCO) functions. Then, we apply it to the coordination among MLB, Inter-Cell Interference Coordination (ICIC), and Energy Savings (ES) functions. With the hybrid approach, we find in one shot, part of the parameter set in an optimal manner, which makes it suitable for dynamic scenarios in which fast response is expected from a centralized coordinator. Finally, we present a manner to formally include MRO in the hybrid approach and show how the framework can be extended to cover challenging network scenarios like Ultra-Reliable Low Latency Communications (URLLC)
Security analysis of mobile edge computing in virtualized small cell networks
Based upon the context of Mobile Edge Computing (MEC) actual research and within the innovative scope of the SESAME EU-funded research project, we propose and assess a framework for security analysis applied in virtualised Small Cell Networks, with the aim of further extending MEC in the broader 5G environment. More specifically, by applying the fundamental concepts of the SESAME original architecture that aims at providing enhanced multi-tenant MEC services through Small Cells coordination and virtualization, we focus on a realistic 5G-oriented scenario enabling the provision of large multi-tenant enterprise services by using MEC. Then we evaluate several security issues by using a formal methodology, known as the Secure Tropos
A Vision of Self-Evolving Network Management for Future Intelligent Vertical HetNet
Future integrated terrestrial-aerial-satellite networks will have to exhibit
some unprecedented characteristics for the provision of both communications and
computation services, and security for a tremendous number of devices with very
broad and demanding requirements in an almost-ubiquitous manner. Although 3GPP
introduced the concept of self-organization networks (SONs) in 4G and 5G
documents to automate network management, even this progressive concept will
face several challenges as it may not be sufficiently agile in coping with the
immense levels of complexity, heterogeneity, and mobility in the envisioned
beyond-5G integrated networks. In the presented vision, we discuss how future
integrated networks can be intelligently and autonomously managed to
efficiently utilize resources, reduce operational costs, and achieve the
targeted Quality of Experience (QoE). We introduce the novel concept of
self-evolving networks (SENs) framework, which utilizes artificial
intelligence, enabled by machine learning (ML) algorithms, to make future
integrated networks fully intelligent and automated with respect to the
provision, adaptation, optimization, and management aspects of networking,
communications, and computation. To envisage the concept of SEN in future
integrated networks, we use the Intelligent Vertical Heterogeneous Network
(I-VHetNet) architecture as our reference. The paper discusses five prominent
communications and computation scenarios where SEN plays the main role in
providing automated network management. Numerical results provide an insight on
how the SEN framework improves the performance of future integrated networks.
The paper presents the leading enablers and examines the challenges associated
with the application of SEN concept in future integrated networks
Will SDN be part of 5G?
For many, this is no longer a valid question and the case is considered
settled with SDN/NFV (Software Defined Networking/Network Function
Virtualization) providing the inevitable innovation enablers solving many
outstanding management issues regarding 5G. However, given the monumental task
of softwarization of radio access network (RAN) while 5G is just around the
corner and some companies have started unveiling their 5G equipment already,
the concern is very realistic that we may only see some point solutions
involving SDN technology instead of a fully SDN-enabled RAN. This survey paper
identifies all important obstacles in the way and looks at the state of the art
of the relevant solutions. This survey is different from the previous surveys
on SDN-based RAN as it focuses on the salient problems and discusses solutions
proposed within and outside SDN literature. Our main focus is on fronthaul,
backward compatibility, supposedly disruptive nature of SDN deployment,
business cases and monetization of SDN related upgrades, latency of general
purpose processors (GPP), and additional security vulnerabilities,
softwarization brings along to the RAN. We have also provided a summary of the
architectural developments in SDN-based RAN landscape as not all work can be
covered under the focused issues. This paper provides a comprehensive survey on
the state of the art of SDN-based RAN and clearly points out the gaps in the
technology.Comment: 33 pages, 10 figure
Empowering the 6G Cellular Architecture with Open RAN
Innovation and standardization in 5G have brought advancements to every facet
of the cellular architecture. This ranges from the introduction of new
frequency bands and signaling technologies for the radio access network (RAN),
to a core network underpinned by micro-services and network function
virtualization (NFV). However, like any emerging technology, the pace of
real-world deployments does not instantly match the pace of innovation. To
address this discrepancy, one of the key aspects under continuous development
is the RAN with the aim of making it more open, adaptive, functional, and easy
to manage. In this paper, we highlight the transformative potential of
embracing novel cellular architectures by transitioning from conventional
systems to the progressive principles of Open RAN. This promises to make 6G
networks more agile, cost-effective, energy-efficient, and resilient. It opens
up a plethora of novel use cases, ranging from ubiquitous support for
autonomous devices to cost-effective expansions in regions previously
underserved. The principles of Open RAN encompass: (i) a disaggregated
architecture with modular and standardized interfaces; (ii) cloudification,
programmability and orchestration; and (iii) AI-enabled data-centric
closed-loop control and automation. We first discuss the transformative role
Open RAN principles have played in the 5G era. Then, we adopt a system-level
approach and describe how these Open RAN principles will support 6G RAN and
architecture innovation. We qualitatively discuss potential performance gains
that Open RAN principles yield for specific 6G use cases. For each principle,
we outline the steps that research, development and standardization communities
ought to take to make Open RAN principles central to next-generation cellular
network designs.Comment: This paper is part of the IEEE JSAC SI on Open RAN. Please cite as:
M. Polese, M. Dohler, F. Dressler, M. Erol-Kantarci, R. Jana, R. Knopp, T.
Melodia, "Empowering the 6G Cellular Architecture with Open RAN," in IEEE
Journal on Selected Areas in Communications, doi: 10.1109/JSAC.2023.333461
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