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