9 research outputs found
Algoritmos de transferência de redes LTE em meios de transporte massivo
Handover in LTE occurs when a device moves from the cell coverage serving it towards another; a process where the user established session must not be interrupted due to this cell change. Handovers in LTE are classified as hard ones, since the link with the serving cell is interrupted before establishing the new link with the target cell. This entails a larger failure risk and, consequently, a potential deterioration in the quality of service. This article presents a review of the handover algorithms in LTE, focusing on the ones oriented to massive means of transport. We show how the new algorithms offer a larger success in handovers, increasing the networkdata rate. This indicates that factors such as speed, position, and direction should be included in the algorithms to improve the handover in means of transport. We also present the algorithms focused on mobile relays such as an important study field for future research works.El traspaso en LTE se presenta cuando un equipo pasa de la cobertura de una celda a la de otra, un proceso en el que se debe asegurar que el usuario no vea interrumpida su sesión, como efecto de ese cambio de celda. Los traspasos en LTE son del tipo duro, en ellos, el enlace con la celda servidora se interrumpe antes de establecer el nuevo enlace con la celda destino, lo que conlleva a un mayor riesgo de falla y con ello a un probable deterioro de la calidad del servicio al usuario. Este artículo revisa algoritmos de traspaso LTE, enfocándose en aquellos orientados a medios de trasporte masivo. Muestra cómo los nuevos algoritmos ofrecen una tasa mayor de traspasos exitosos y con ello una mejor tasa de transferencia de datos; evidencia que factores como la velocidad, la posición y la dirección deben ser incluidos en los algoritmos dirigidos a mejorar el traspaso en medios de transporte; y presenta a los algoritmos enfocados en relays móviles, como un importante campo de estudio para futuras investigaciones.A transferência em LTE ocorre quando um dispositivo passa da cobertura de uma célula para outra, um processo no qual deve ser assegurado que o usuário não veja sua sessão interrompida, como resultado dessa mudança de célula. As transferências em LTE são do tipo duro, nelas, o link com a célula do servidor é interrompido antes de se estabelecer o novo link com a célula alvo, o que leva a um maior risco de falha e, portanto, a uma provável deterioração da qualidade do serviço ao usuário. Este artigo revisa os algoritmos de transferência LTE, com foco naqueles orientados a meios de transporte massivo. Mostra como os novos algoritmos oferecem uma taxa maior de transferências bem-sucedidas e, com isso, uma melhor taxa de transferência de dados; evidencia de que fatores como a velocidade, a posição e a direção devem ser incluídos nos algoritmos que visam melhorar a transferência nos meios de transporte; e apresenta os algoritmos focados em relés móveis, como um importante campo de estudo para futuras pesquisas
A survey of machine learning techniques applied to self organizing cellular networks
In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future
Self organization in 3GPP long term evolution networks
Mobiele en breedbandige internettoegang is realiteit. De internetgeneratie vindt het immers normaal om overal breedbandige internettoegang te hebben. Vandaag zijn er al 5,9 miljard mobiele abonnees ( 87% van de wereldbevolking) en 20% daarvan hebben toegang tot een mobiele breedbandige internetverbinding. Dit wordt aangeboden door 3G (derde generatie) technologieën zoals HSPA (High Speed Packet Access) en 4G (vierde generatie) technologieën zoals LTE (Long Term Evolution). De vraag naar hoogkwalitatieve diensten stelt de mobiele netwerkoperatoren en de verkopers van telecommunicatieapparatuur voor nieuwe uitdagingen: zij moeten nieuwe oplossingen vinden om hun diensten steeds sneller en met een hogere kwaliteit aan te bieden. De nieuwe LTE-standaard brengt niet alleen hogere pieksnelheden en kleinere vertragingen. Het heeft daarnaast ook nieuwe functionaliteiten in petto die zeer aantrekkelijk zijn voor de mobiele netwerkoperator: de integratie van zelfregelende functies die kunnen ingezet worden bij de planning van het netwerk, het uitrollen van een netwerk en het controleren van allerhande netwerkmechanismen (o.a. handover, spreiding van de belasting over de cellen). Dit proefschrift optimaliseert enkele van deze zelfregelende functies waardoor de optimalisatie van een mobiel netwerk snel en automatisch kan gebeuren. Hierdoor verwacht men lagere kosten voor de mobiele operator en een hogere kwaliteit van de aangeboden diensten
Coordinating Coupled Self-Organized Network Functions in Cellular Radio Networks
Nutzer der Mobilfunknetze wünschen und fordern eine Steigerung des
Datendurchsatzes, die zur Erhöhung der Netzlast führt. Besonders seit der
Einführung von LTE erhöht sich daher die Anzahl und Dichte der Zellen in
Mobilfunknetzen. Dies führt zusätzlich zur Zunahme der Investitions- und
Betriebskosten, sowie einer höheren Komplexität des Nerzbetriebs. Der
Einsatz selbstorganisierter Netze (SONs) wird vorgeschlagen, um diese drei
Herausforderungen zu bewältigen. Einige SON-Funktionen (SF) wurden sowohl
von Seiten der Netzbetreiber als auch von den Standardisierungsgremien
vorgeschlagen. Eine SF repräsentiert hierbei eine Netzfunktion, die
automatisiert werden kann. Ein Beispiel ist die Optimierung der Robustheit
des Netzes (Mobility Robustness Optimization, MRO) oder der Lastausgleich
zwischen Funkzellen (Mobility Load Balancing, MLB).
Die unterschiedlichen SON-Funktionen werden innerhalb eines Mobilfunknetzes
eingesetzt, wobei sie dabei häufig gleiche oder voneinander abhängige
Parameter optimieren. Zwangsläufig treten daher beim Einsatz paralleler
SON-Funktionen Konflikte auf, die Mechanismen erfordern, um diese
Konflikte aufzulösen oder zu minimieren. In dieser Dissertation werden
Lösungen aufgezeigt und untersucht, um die Koordination der SON-Funktionen
zu automatisieren und, soweit möglich, gleichmä{\ss}ig zu verteilen.
Im ersten Teil werden grundsätzliche Entwürfe für SFs evaluiert, um die
SON-Koordination zu vereinfachen. Basierend auf der Beobachtung, dass die
Steurung der SON-Funktion sich ähnlich dem generischen Q-Learning Problem
verhält, werden die SFs als Q-Learning-Agenten entworfen. Dieser Ansatz
wurde mit sehr positiven Ergebnissen auf zwei SFs (MRO und MLB) angewandt.
Die als Q-Learning-Agenten entworfenen SFs werden für zwei
unterschiedliche Ansätze der SON-Koordination evaluiert. Beide
Koordinierungsansätze betrachten dabei die SON-Umgebung als ein
Multi-Agenten-System. Der erste Ansatz basierend auf einer
räumlich-zeitlichen Entkoppelung separiert die Ausführung von
SF-Instanzen sowohl räumlich als auch zeitlich, um die Konflikte zwischen
den SF-Instanzen zu minimieren. Der zweite Ansatz wendet kooperatives
Lernen in Multi-Agenten-Systemen als automatisierten Lösungsansatz zur
SON-Koordination an. Die einzelnen SF-Instanzen lernen anhand von
Utility-Werten, die sowohl die eigenen Metriken als auch die Metriken der
Peer-SF-Instanzen auswerten. Die Intention dabei ist, durch die erlernte
Zustands-Aktions-Strategie Aktionen auszuführen, die das beste Resultat
für die aktive SF, aber auch die geringste Auswirkung auf Peer-SFs
gewährleisten. In der Evaluation des MRO-MLB-Konflikts zeigten beide
Koordinierungsansätze sehr gute Resultate.Owing to increase in desired user throughput and to the subsequent increase
in network traffic, the number and density of cells in cellular networks
have increased, especially starting with LTE. This directly translates into
higher capital and operational expenses as well as increased complexity of
network operation. To counter all three challenges, Self-Organized
Networks (SON) have been proposed. A number of SON Functions (SFs) have
been defined both from the network operator community as well as from the
standardization bodies. In this respect, a SF represents a network
function that can be automated e.g. Mobility Robustness Optimization (MRO)
or Mobility Load balancing (MLB).
The different SFs operate on the same radio network, in many cases
adjusting the same or related parameters. Conflicts are as such bound to
occur during the parallel operation of such SFs and mechanisms are required
to resolve or minimize the conflicts. This thesis studies the solutions
through which SON functions can be coordinated in an automated and
preferably distributed manner.
In the first part we evaluate the design principles of SFs that aim at
easing the coordination. With the observation that the SON control loop is
similar to a generic Q-learning problem, we propose designing SFs as
Q-learning agents. This framework is applied to two SFs (MRO and MLB) with
very positive results. Given the designed QL based SFs, we then
evaluate two SON coordination approaches that consider the SON environment
as a Multi-Agent System (MAS). The first approach based on
Spatial-Temporal Decoupling (STD) separates the execution of SF instances
in space and time so as to minimize the conflicts among instances. The
second approach applies multi-agent cooperative learning for an automated
solution towards SON coordination. In this case individual SF instances
learn based on utilities that aggregate their own metrics as well as the
metrics of peer SF instances. The intention in this case is to ensure that
the learned state-action policy functions apply actions that guarantee the
best result for the active SF but also have the least effect on the peer
SFs. Both coordination approaches have been evaluated with very positive
results in simulations that consider the MRO - MLB conflict
Inter-RAT Mobility Robustness Optimization in Self-Organizing Networks
The massive growth in mobile data communication requires new more efficient Radio Access Technology (RAT) such as Long Term Evolution (LTE) being deployed on top of legacy mobile communication systems. Inter-RAT handovers are triggered either when the signal level of the serving RAT becomes weak while a sufficiently high signal level is
measured from another RAT, or by traffic steering policies for balancing the load among different RATs, for example. Trouble-free operation of inter-RAT handovers requires an optimal setting of the handover parameters which is typically different for each cell and even location. Without knowing the detailed radio propagation conditions, directions and speeds of User Equipments (UEs), network planning can only provide a default setting which needs to be manually optimized during network operation with the aid of drive tests and expert knowledge. This manual optimization requires
extensive human intervention which increases Operational Expenses (OPEX) of mobile operators and yields sub-optimal mobility performance due to limited means for more detailed root cause analysis. Therefore, automatic mechanisms have been requested by
mobile operators to optimize the inter-RAT handover parameters. This optimization is known as inter-RAT Mobility Robustness Optimization (MRO) which is one of the use cases in Self-Organizing Network (SON).
The technical complexities and requirements on MRO are too difficult to be tackled efficiently and properly by existing manual optimization methods. Considering that mobile networks consist of a high number of cells, the number of handover thresholds to be optimized in a network is significant. Moreover, the intricate dependencies and interactions among the handover thresholds of different neighboring cells make MRO problems even more challenging and complicated. Current optimization methods such as the local search method Simulated Annealing, for example, can be used offline
in the network planning phase, however, they cannot be applied online in real-time networks to dynamically react on the changes in the environment and traffic. From that perspective, new optimization methods are needed to address the challenges and limitations imposed by MRO. In this thesis, several novel and feasible inter-RAT MRO
methods have been proposed and analyzed.
New key performance indicators which capture the different types of mobility failure events are proposed by the author of this thesis for the inter-RAT scenario. An inter-RAT handover is triggered by a dual-threshold measurement event where the first threshold corresponds to the serving cell and the second to the neighboring target cell of another RAT. This dual-threshold measurement event requires a more precise analysis of Too Late Handovers (TLHs). A TLH which is caused by the misconfigured serving cell threshold is distinguished from that which can be resolved by the target cell threshold. Thus, there are two types of TLHs in contrast to the intra-RAT case where a single type of TLH handover exists.
Inter-RAT handover thresholds of currently standardized RATs are configured and optimized cell-specifically. That is, the same handover thresholds are applied by the UEs irrespective of the neighboring handover target cell. The limitations of a cell-specific
optimization approach are analyzed and a new cell-group specific optimization approach where the handover thresholds are differentiated with respect to a group of neighboring target cells is proposed. For both cell-specific and cell-group specific optimization approaches, an automatic algorithm is developed to optimize the inter-
RAT handover thresholds. In order to analyze the impact of Time-to-Trigger (TTT), which is a time interval affecting the triggering of handovers, the MRO algorithm is extended to allow a joint optimization of handover thresholds and TTT. Based on findings that even cell-group specific parameters cannot resolve all mobility failure events in some cells where radio conditions are not stationary along the cell border, a more advanced location-specific approach is proposed. Unlike cell-based optimization approaches, the handover thresholds are configured and optimized per cell-area and
they can be differentiated with respect to neighboring target cells.
Simulative investigations are carried out to evaluate the performance of the different optimization approaches. It has been shown that mobility failure events are rather located in specific cells. Accordingly, the same UEs are probably affected all the time by these mobility failures which leads to high user dissatisfaction. This clearly indicates the need of cell-specific handover thresholds to resolve the mobility problems in some cells. Moreover, it is shown that the optimization of target cell threshold in a cell-group
specific manner yields an additional performance improvement compared to cell-specific optimization approach. The joint optimization approach of handover thresholds and TTT has shown advantages only when the handover thresholds are configured cell-specifically
rather than cell-group specifically. The mobility failure events that are not resolved by cell-based optimization approaches are mitigated by cell-area based optimization approach.
The investigations and concepts in this thesis have directly impacted 3rd Generation Partnership Project (3GPP) standard. Several contributions related to cell-specific and cell-group specific optimization approaches have been submitted and adopted by LTE Release (Rel.) 11 standard
Optimierung der Handover Entscheidung in Infrastrukturnetzen unter Verwendung von realistischen Simulationsumgebungen
The development of mobile communication services and technologies in recent years boosts the importance and ubiquity of terminal equipments in our everyday life. The main drivers for this development are the reliability of the offered services and the user friendliness, allowing a huge variety of communication services with a single device. To assure a high communication quality and the usability of the services a seamless connectivity is beneficial or even mandatory, e.g. for voice calls, video streaming, gaming or safety-critical application based on car-to-car communication. Due to the cellular
nature of infrastructure networks, mobile users will cross cell boundaries and need to switch the serving cell with the help of a handover procedure. The timing of the handover is essential to keep the mobile devices connected to the network.
The introduction of measurement based optimisation in the context of self-organising networks enables the optimisation of the handover decision. The key enabler for the optimisation are a cost function that incorporates the relevant handover performance indicators, a reasonable observation time to evaluate the performance and an optimisation algorithm that reliably improves the handover performance in various, ever-changing network conditions. In the recent years several handover optimisation algorithms have been investigated. Nevertheless, the influence of the target function on the optimisation, the dimensioning of the observation window and the impact of network condition changes have not been investigated so far.
In this dissertation a detailed analysis of the handover performance indicators is presented. Beyond that, additional system information or measurements are valued as potential candidates to allow further improvement of the handover performance. Particular attention is paid to the ability to adapt to changing network conditions since the introduction of new cell layers (small cells), new techniques like adaptive antenna systems or spectrum sharing or the introduction of new communication technologies like LTE-Advanced increases the complexity of future mobile communication networks. Finally, we develop
an optimisation algorithm that reliably and quickly optimises the handover performance in various and fast-changing network conditions.Mobile Endgeräte gewinnen in unserem täglichen Leben zunehmend an Bedeutung. Dieser Trend wird vorangetrieben durch die rasante Entwicklung der Mobilfunktechnologien und neu angebotene Dienste in den letzten Jahren. Immer mehr Dienstleistungen werden über ein einzelnes Endgerät bereitgestellt. Um eine hohe Übertragungsqualität zur Nutzung der Dienste sicherzustellen, ist eine nahtlose Verbindung zum Kommunikationsnetzwerk wünschenswert oder sogar obligatorisch, z.B. für Sprachverbindungen, Video-Streaming, Onlinespiele oder sicherheitsrelevante Anwendungen der Car-to-Car-Kommunikation. Bedingt durch die zellulare Struktur der Mobilfunknetze ist zur Aufrechterhaltung der Kommunikation ein Zellwechsel (Handover) im Randbereich des Versorgungsgebietes einer Zelle notwendig. Der genaue Zeitpunkt des Zellwechsels ist dabei von besonderer Bedeutung.
Die Einführung der messungsbasierten Selbst-Optimierung für Mobilfunknetze ermöglicht die Optimierung der Zellwechsel-Entscheidung. Die wesentlichen Voraussetzungen für eine Optimierung sind eine Optimierungszielfunktion auf Basis der Leistungsindikatoren, eine angemessene Beobachtungszeit sowie die Entwicklung eines möglichst allgemeingültigen Optimierungsverfahrens. In den letzten Jahren sind viele solcher Verfahren untersucht und veröffentlicht worden. Dennoch sind der Einfluss der Zielfunktion auf die Optimierung, die Dimensionierung des Beobachtungszeitraums und die Auswirkungen von Netzzustandsänderungen auf die Optimierung bisher weitgehend vernachlässigt worden.
In dieser Arbeit wird eine detaillierte Analyse der Zellwechsel-Leistungsindikatoren in LTE durchgeführt. Darüber hinaus wird die Eignung zusätzlicher Systeminformationen oder Messungen zur weiteren Verbesserung der Zellwechsel-Entscheidung untersucht. Durch die Einführung neuer Zelltypen (z.B. Small Cells), moderner Übertragungstechniken wie adaptive Antennensysteme oder die Einführung neuer Technologien wie LTE Advanced nimmt die Komplexität der zukünftigen Mobilfunknetze stetig zu. Das in dieser Arbeit entwickelte Optimierungsverfahren ermöglicht eine schnelle und zuverlässige Anpassung der Zellwechselparameter an die veränderlichen Bedingungen in den Mobilfunknetzen und kann daher auch in komplexeren Systemen eingesetzt werden