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)

Review on Radio Resource Allocation Optimization in LTE/LTE-Advanced using Game Theory

Recently, there has been a growing trend toward ap-plying game theory (GT) to various engineering fields in order to solve optimization problems with different competing entities/con-tributors/players. Researches in the fourth generation (4G) wireless network field also exploited this advanced theory to overcome long term evolution (LTE) challenges such as resource allocation, which is one of the most important research topics. In fact, an efficient de-sign of resource allocation schemes is the key to higher performance. However, the standard does not specify the optimization approach to execute the radio resource management and therefore it was left open for studies. This paper presents a survey of the existing game theory based solution for 4G-LTE radio resource allocation problem and its optimization

LTE-Advanced radio access enhancements: A survey

Long Term Evolution Advanced (LTE-Advanced) is the next step in LTE evolution and allows operators to improve network performance and service capabilities through smooth deployment of new techniques and technologies. LTE-Advanced uses some new features on top of the existing LTE standards to provide better user experience and higher throughputs. Some of the most significant features introduced in LTE-Advanced are carrier aggregation, enhancements in heterogeneous networks, coordinated multipoint transmission and reception, enhanced multiple input multiple output usage and deployment of relay nodes in the radio network. Mentioned features are mainly aimed to enhance the radio access part of the cellular networks. This survey article presents an overview of the key radio access features and functionalities of the LTE-Advanced radio access network, supported by the simulation results. We also provide a detailed review of the literature together with a very rich list of the references for each of the features. An LTE-Advanced roadmap and the latest updates and trends in LTE markets are also presented

ENERGY-EFFICIENT DESIGN OF HETEROGENEOUS CELLULAR NETWORKS USING STOCHASTIC GEOMETRY

Ph.DDOCTOR OF PHILOSOPH

Design of static intercell interference coordination schemes for realistic lte-based cellular networks

Today, 3.5 and 4G systems including Long Term Evolution (LTE) and LTE-Advanced (LTE-A) support packet-based services and provide mobile broadband access for bandwidth-hungry applications. In this context of fast evolution, new and challenging technical issues must be e ectively addressed. The nal target is to achieve a signi cant step forward toward the improvement of the Quality of Experience (QoE). To that end, interference management has been recognized by the industry as a key enabler for cellular technologies based on OFDMA. Indeed, with a low frequency reuse factor, intercell interference (ICI) becomes a major concern since the Quality of Service (QoS) is not uniformly delivered across the network, it remarkably depends on user position. Hence, cell edge performance is an important issue in LTE and LTE-A. Intercell Interference Coordination (ICIC) encompasses strategies whose goal is to keep ICI at cell edges as low as possible. This alleviates the aforementioned situation. For this reason, the novelties presented in this Ph.D. thesis include not only developments of static ICIC mechanisms for data and control channels, but also e orts towards further improvements of the energy e ciency perspective. Based on a comprehensive review of the state of the art, a set of research opportunities were identi ed. To be precise, the need for exible performance evaluation methods and optimization frameworks for static ICIC strategies. These mechanisms are grouped in two families: the schemes that de ne constraints on the frequency domain and the ones that propose adjustments on the power levels. Thus, Soft- and Fractional Frequency Reuse (SFR and FFR, respectively) are identi ed as the base of the vast majority of static ICIC proposals. Consequently, during the rst part of this Ph.D. thesis, interesting insights into the operation of SFR and FFR were identi ed beyond well-known facts. These studies allow for the development of a novel statistical framework to evaluate the performance of these schemes in realistic deployments. As a result of the analysis, the poor performance of classic con gurations of SFR and FFR in real-world contexts is shown, and hence, the need for optimization is established. In addition, the importance of the interworking between static ICIC schemes and other network functionalities such as CSI feedback has also been identi ed. Therefore, novel CSI feedback schemes, suitable to operate in conjunction with SFR and FFR, have been developed. These mechanisms exploit the resource allocation pattern of these static ICIC techniques in order to improve the accuracy of the CSI feedback process. The second part is focused on the optimization of SFR and FFR. The use of multiobjective techniques is investigated as a tool to achieve e ective network-speci c optimization. The approach o ers interesting advantages. On the one hand, it allows for simultaneous optimization of several con icting criteria. On the other hand, the multiobjective nature results in outputs composed of several high quality (Pareto e cient) network con gurations, all of them featuring a near-optimal tradeo between the performance criteria. Multiobjective evolutionary algorithms allow employing complex mathematical structures without the need for relaxation, thus capturing accurately the system behavior in terms of ICI. The multiobjective optimization formulation of the problem aims at achieving e ective adjustment of the operational parameters of SFR and FFR both at cell level and network-wide. Moreover, the research was successfully extended to the control channels, both the PDCCH and ePDCCH. Finally, in an e ort to further improve the network energy e ciency (an aspect always considered throughout the thesis), the framework of Cell Switch O (CSO), having close connections with ICIC, is also introduced. By means of the proposed method, signi cant improvements with respect to traditional approaches, baseline con gurations, and previous proposals can be achieved. The gains are obtained in terms of energy consumption, network capacity, and cell edge performance.Actualmente los sistemas 3.5 y 4G tales como Long Term Evolution (LTE) y LTE-Advanced (LTE-A) soportan servicios basados en paquetes y proporcionan acceso de banda ancha m ovil para aplicaciones que requieren elevadas tasas de transmisi on. En este contexto de r apida evoluci on, aparecen nuevos retos t ecnicos que deben ser resueltos e cientemente. El objetivo ultimo es conseguir un salto cualitativo importante en la experiencia de usuario (QoE). Con tal n, un factor clave que ha sido reconocido en las redes celulares basadas en Orthogonal Frequency- Division Multiple Access (OFDMA) es la gesti on de interferencias. De hecho, la utilizaci on de un factor de reuso bajo permite una elevada e ciencia espectral pero a costa de una distribuci on de la calidad de servicio (QoS) que no es uniforme en la red, depende de la posici on del usuario. Por lo tanto, el rendimiento en los l mites de la celda se ve muy penalizado y es un problema importante a resolver en LTE y LTE-A. La coordinaci on de interferencias entre celdas (ICIC, del ingl es Intercell Interfe- rence Coordination) engloba las estrategias cuyo objetivo es mantener la interferencia intercelular (ICI) lo m as baja posible en los bordes de celda. Esto permite aliviar la situaci on antes mencionada. La contribuci on presentada en esta tesis doctoral incluye el dise~no de nuevos mecanismos de ICIC est atica para los canales de datos y control, as como tambi en mejoras desde el punto de vista de e ciencia energ etica. A partir de una revisi on completa del estado del arte, se identi caron una serie de retos abiertos que requer an esfuerzos de investigaci on. En concreto, la necesidad de m etodos de evaluaci on exibles y marcos de optimizaci on de las estrategias de ICIC est aticas. Estos mecanismos se agrupan en dos familias: los esquemas que de nen restricciones sobre el dominio de la frecuencia y los que proponen ajustes en los niveles de potencia. Es decir, la base de la gran mayor a de propuestas ICIC est aticas son la reutilizaci on de frecuencias de tipo soft y fraccional (SFR y FFR, respectivamente). De este modo, durante la primera parte de esta tesis doctoral, se han estudiado los aspectos m as importantes del funcionamiento de SFR y FFR, haciendo especial enfasis en las conclusiones que van m as all a de las bien conocidas. Ello ha permitido introducir un nuevo marco estad stico para evaluar el funcionamiento de estos sistemas en condiciones de despliegue reales. Como resultado de estos an alisis, se muestra el pobre desempe~no de SFR y FFR en despliegues reales cuando funcionan con sus con guraciones cl asicas y se establece la necesidad de optimizaci on. Tambi en se pone de mani esto la importancia del funcionamiento conjunto entre esquemas ICIC est aticos y otras funcionalidades de la red radio, tales como la informaci on que env an los usuarios sobre el estado de su canal downlink (feedback del CSI, del ingl es Channel State Information). De este modo, se han propuesto diferentes esquemas de feedback apropiados para trabajar conjuntamente con SFR y FFR. Estos mecanismos explotan el patr on de asignaci on de recursos que se utiliza en ICIC est atico para mejorar la precisi on del proceso. La segunda parte se centra en la optimizaci on de SFR y FFR. Se ha investigado el uso de t ecnicas multiobjetivo como herramienta para lograr una optimizaci on e caz, que es espec ca para cada red. El enfoque ofrece ventajas interesantes, por un lado, se permite la optimizaci on simult anea de varios criterios contradictorios. Por otro lado, la naturaleza multiobjetivo implica obtener como resultado con guraciones de red de elevada calidad (Pareto e cientes), todas ellas con un equilibrio casi- optimo entre las diferentes m etricas de rendimiento. Los algoritmos evolucionarios multiobjetivo permiten la utilizaci on de estructuras matem aticas complejas sin necesidad de relajar el problema, de este modo capturan adecuadamente su comportamiento en t erminos de ICI. La formulaci on multiobjetivo consigue un ajuste efectivo de los par ametros operacionales de SFR y FFR, tanto a nivel de celda como a nivel de red. Adem as, la investigaci on se extiende con resultados satisfactorios a los canales de control, PDCCH y ePDCCH. Finalmente, en un esfuerzo por mejorar la e ciencia energ etica de la red (un aspecto siempre considerado a lo largo de la tesis), se introduce en el an alisis global el apagado inteligente de celdas, estrategia con estrechos v nculos con ICIC. A trav es del m etodo propuesto, se obtienen mejoras signi cativas con respecto a los enfoques tradicionales y propuestas previas. Las ganancias se obtienen en t erminos de consumo energ etico, capacidad de la red, y rendimiento en el l mite de las celdas.Actualment els sistemes 3.5 i 4G tals com Long Term Evolution (LTE) i LTE- Advanced (LTE-A) suporten serveis basats en paquets i proporcionen acc es de banda ampla m obil per a aplicacions que requereixen elevades taxes de transmissi o. En aquest context de r apida evoluci o, apareixen nous reptes t ecnics que han de ser resolts e cientment. L'objectiu ultim es aconseguir un salt qualitatiu important en l'experi encia d'usuari (QoE). Amb tal , un factor clau que ha estat reconegut a les xarxes cel lulars basades en Orthogonal Frequency-Division Multiple Access (OFDMA) es la gesti o d'interfer encies. De fet, la utilizaci o d'un factor de re us baix permet una elevada e ci encia espectral per o a costa d'una distribuci o de la qualitat de servei (QoS) que no es uniforme a la xarxa, dep en de la posici o de l'usuari. Per tant, el rendiment en els l mits de la cel la es veu molt penalitzat i es un problema important a resoldre en LTE i LTE-A. La coordinaci o d'interfer encies entre cel les (ICIC, de l'angl es Intercell Interfe- rence Coordination) engloba les estrat egies que tenen com a objectiu mantenir la interfer encia intercel lular (ICI) el m es baixa possible en les vores de la cel la. Aix o permet alleujar la situaci o abans esmentada. La contribuci o presentada en aquesta tesi doctoral inclou el disseny de nous mecanismes de ICIC est atica per als canals de dades i control, aix com tamb e millores des del punt de vista d'e ci encia energ etica. A partir d'una revisi o completa de l'estat de l'art, es van identi car una s erie de reptes oberts que requerien esfor cos de recerca. En concret, la necessitat de m etodes d'avaluaci o exibles i marcs d'optimitzaci o de les estrat egies de ICIC est atiques. Aquests mecanismes s'agrupen en dues fam lies: els esquemes que de neixen restriccions sobre el domini de la freq u encia i els que proposen ajustos en els nivells de pot encia. Es a dir, la base de la gran majoria de propostes ICIC est atiques s on la reutilitzaci o de freq u encies de tipus soft i fraccional (SFR i FFR, respectivament). D'aquesta manera, durant la primera part d'aquesta tesi doctoral, s'han estudiat els aspectes m es importants del funcionament de SFR i FFR, fent especial emfasi en les conclusions que van m es enll a de les ben conegudes. Aix o ha perm es introduir un nou marc estad stic per avaluar el funcionament d'aquests sistemes en condicions de desplegament reals. Com a resultat d'aquestes an alisis, es mostra el pobre acompliment de SFR i FFR en desplegaments reals quan funcionen amb les seves con guracions cl assiques i s'estableix la necessitat d'optimitzaci o. Tamb e es posa de manifest la import ancia del funcionament conjunt entre esquemes ICIC est atics i altres funcionalitats de la xarxa radio, tals com la informaci o que envien els usuaris sobre l'estat del seu canal downlink (feedback del CSI, de l'angl es Channel State Information). D'aquesta manera, s'han proposat diferents esquemes de feedback apropiats per treballar conjuntament amb SFR i FFR. Aquests mecanismes exploten el patr o d'assignaci o de recursos que s'utilitza en ICIC est atic per millorar la precisi o del proc es. La segona part se centra en l'optimitzaci o de SFR i FFR. S'ha investigat l' us de t ecniques multiobjectiu com a eina per aconseguir una optimitzaci o e ca c, que es espec ca per a cada xarxa. L'enfocament ofereix avantatges interessants, d'una banda, es permet l'optimitzaci o simult ania de diversos criteris contradictoris. D'altra banda, la naturalesa multiobjectiu implica obtenir com resultat con guracions de xarxa d'elevada qualitat (Pareto e cients), totes elles amb un equilibri gaireb e optim entre les diferents m etriques de rendiment. Els algorismes evolucionaris multiobjectiu permeten la utilitzaci o d'estructures matem atiques complexes sense necessitat de relaxar el problema, d'aquesta manera capturen adequadament el seu comportament en termes de ICI. La formulaci o multiobjectiu aconsegueix un ajust efectiu dels par ametres operacionals de SFR i FFR, tant a nivell de cel la com a nivell de xarxa. A m es, la recerca s'est en amb resultats satisfactoris als canals de control, PDCCH i ePDCCH. Finalment, en un esfor c per millorar l'e ci encia energ etica de la xarxa (un aspecte sempre considerat al llarg de la tesi), s'introdueix en l'an alisi global l'apagat intel ligent de cel les, estrat egia amb estrets vincles amb ICIC. Mitjan cant el m etode proposat, s'obtenen millores signi catives pel que fa als enfocaments tradicionals i propostes pr evies. Els guanys s'obtenen en termes de consum energ etic, capacitat de la xarxa, i rendiment en el l mit de les cel les

Definition and specification of connectivity and QoE/QoS management mechanisms – final report

This document summarizes the WP5 work throughout the project, describing its functional architecture and the solutions that implement the WP5 concepts on network control and orchestration. For this purpose, we defined 3 innovative controllers that embody the network slicing and multi tenancy: SDM-C, SDM-X and SDM-O. The functionalities of each block are detailed with the interfaces connecting them and validated through exemplary network processes, highlighting thus 5G NORMA innovations. All the proposed modules are designed to implement the functionality needed to provide the challenging KPIs required by future 5G networks while keeping the largest possible compatibility with the state of the art

Self-organised multi-objective network clustering for coordinated communications in future wireless networks

The fifth generation (5G) cellular system is being developed with a vision of 1000 times more capacity than the fourth generation (4G) systems to cope with ever increasing mobile data traffic. Interference mitigation plays an important role in improving the much needed overall capacity especially in highly interference-limited dense deployment scenarios envisioned for 5G. Coordinated multi-point (CoMP) is identified as a promising interference mitigation technique where multiple base stations (BS) can cooperate for joint transmission/reception by exchanging user/control data and perform joint signal processing to mitigate inter-cell interference and even exploit it as a useful signal. CoMP is already a key feature of long term evolution-advanced (LTE-A) and envisioned as an essential function for 5G. However, CoMP cannot be realized for the whole network due to its computational complexity, synchronization requirement between coordinating BSs and high backhaul capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. This PhD thesis aims to investigate optimum dynamic CoMP clustering solutions in 5G and beyond wireless networks with massive small cell (SC) deployment. Truly self-organised CoMP clustering algorithms are investigated, aiming to improve much needed spectral efficiency and other network objectives especially load balancing in future wireless networks. Low complexity, scalable, stable and efficient CoMP clustering algorithms are designed to jointly optimize spectral efficiency, load balancing and limited backhaul availability. Firstly, we provide a self organizing, load aware, user-centric CoMP clustering algorithm in a control and data plane separation architecture (CDSA) proposed for 5G to maximize spectral efficiency and improve load balancing. We introduce a novel re-clustering algorithm for user equipment (UE) served by highly loaded cells and show that unsatisfied UEs due to high load can be significantly reduced with minimal impact on spectral efficiency. Clustering with load balancing algorithm exploits the capacity gain from increase in cluster size and also the traffic shift from highly loaded cells to lightly loaded neighbours. Secondly, we develop a novel, low complexity, stable, network-centric clustering model to jointly optimize load balancing and spectral efficiency objectives and tackle the complexity and scalability issues of user-centric clustering. We show that our clustering model provide high spectral efficiency in low-load scenario and better load distribution in high-load scenario resulting in lower number of unsatisfied users while keeping spectral efficiency at comparably high levels. Unsatisfied UEs due to high load are reduced by $68.5\%$ with our algorithm when compared to greedy clustering model. In this context, the unique contribution of this work that it is the first attempt to fill the gap in literature for multi-objective, network-centric CoMP clustering, jointly optimizing load balancing and spectral efficiency. Thirdly, we design a novel multi-objective CoMP clustering algorithm to include backhaul-load awareness and tackle one of the biggest challenges for the realization of CoMP in future networks i.e. the demand for high backhaul bandwidth and very low latency. We fill the gap in literature as the first attempt to design a clustering algorithm to jointly optimize backhaul/radio access load and spectral efficiency and analyze the trade-off between them. We employ 2 novel coalitional game theoretic clustering methods, 1-a novel merge/split/transfer coalitional game theoretic clustering algorithm to form backhaul and load aware BS clusters where spectral efficiency is still kept at high level, 2-a novel user transfer game model to move users between clusters to improve load balancing further. Stability and complexity analysis is provided and simulation results are presented to show the performance of the proposed method under different backhaul availability scenarios. We show that average system throughout is increased by 49.9% with our backhaul-load aware model in high load scenario when compared to a greedy model. Finally, we provide an operator's perspective on deployment of CoMP. Firstly, we present the main motivation and benefits of CoMP from an operator's viewpoint. Next, we present operational requirements for CoMP implementation and discuss practical considerations and challenges of such deployment. Possible solutions for these experienced challenges are reviewed. We then present initial results from a UL CoMP trial and discuss changes in key network performance indicators (KPI) during the trial. Additionally, we propose further improvements to the trialed CoMP scheme for better potential gains and give our perspective on how CoMP will fit into the future wireless networks