1,237 research outputs found

    A survey of self organisation in future cellular networks

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    This article surveys the literature over the period of the last decade on the emerging field of self organisation as applied to wireless cellular communication networks. Self organisation has been extensively studied and applied in adhoc networks, wireless sensor networks and autonomic computer networks; however in the context of wireless cellular networks, this is the first attempt to put in perspective the various efforts in form of a tutorial/survey. We provide a comprehensive survey of the existing literature, projects and standards in self organising cellular networks. Additionally, we also aim to present a clear understanding of this active research area, identifying a clear taxonomy and guidelines for design of self organising mechanisms. We compare strength and weakness of existing solutions and highlight the key research areas for further development. This paper serves as a guide and a starting point for anyone willing to delve into research on self organisation in wireless cellular communication networks

    A survey of machine learning techniques applied to self organizing cellular networks

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    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

    A case study: Failure prediction in a real LTE network

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    Mobile traffic and number of connected devices have been increasing exponentially nowadays, with customer expectation from mobile operators in term of quality and reliability is higher and higher. This places pressure on operators to invest as well as to operate their growing infrastructures. As such, telecom network management becomes an essential problem. To reduce cost and maintain network performance, operators need to bring more automation and intelligence into their management system. Self-Organizing Networks function (SON) is an automation technology aiming to maximize performance in mobility networks by bringing autonomous adaptability and reducing human intervention in network management and operations. Three main areas of SON include self-configuration (auto-configuration when new element enter the network), self-optimization (optimization of the network parameters during operation) and self-healing (maintenance). The main purpose of the thesis is to illustrate how anomaly detection methods can be applied to SON functions, in particularly self-healing functions such as fault detection and cell outage management. The thesis is illustrated by a case study, in which the anomalies - in this case, the failure alarms, are predicted in advance using performance measurement data (PM data) collected from a real LTE network within a certain timeframe. Failures prediction or anomalies detection can help reduce cost and maintenance time in mobile network base stations. The author aims to answer the research questions: what anomaly detection models could detect the anomalies in advance, and what type of anomalies can be well-detected using those models. Using cross-validation, the thesis shows that random forest method is the best performing model out of the chosen ones, with F1-score of 0.58, 0.96 and 0.52 for the anomalies: Failure in Optical Interface, Temperature alarm, and VSWR minor alarm respectively. Those are also the anomalies can be well-detected by the model

    Self-Organizing Networks use cases in commercial deployments

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    These measurements can be obtained from different sources, but these sources are either expensive or not applicable to any network. To solve this problem, this thesis proposes a method that uses information available in any network so that the calibration of predictive maps is converted into universal without losing accuracy with respect to current methods. Furthermore, the complexity of today's networks makes them prone to failure. To save costs, operators employ network self-healing techniques so that networks are able to self-diagnose and even self-fix when possible. Among the various failures that can occur in mobile communication networks, a common case is the existence of sectors whose radiated signal has been exchanged. This issue appears during the network roll-out when engineers accidentally cross feeders of several antennas. Currently, manual methodology is used to identify this problem. Therefore, this thesis presents an automatic system to detect these cases. Finally, special attention has been paid to the computational efficiency of the algorithms developed in this thesis since they have finally been integrated into commercial tools.Ince their origins, mobile communication networks have undergone major changes imposed by the need for networks to adapt to user demand. To do this, networks have had to increase in complexity. In turn, complexity has made networks increasingly difficult to design and maintain. To mitigate the impact of network complexity, the concept of self-organizing networks (SON) emerged. Self-organized networks aim at reducing the complexity in the design and maintenance of mobile communication networks by automating processes. Thus, three major blocks in the automation of networks are identified: self-configuration, self-optimization and self-healing. This thesis contributes to the state of the art of self-organized networks through the identification and subsequent resolution of a problem in each of the three blocks into which they are divided. With the advent of 5G networks and the speeds they promise to deliver to users, new use cases have emerged. One of these use cases is known as Fixed Wireless Access. In this type of network, the last mile of fiber is replaced by broadband radio access of mobile technologies. Until now, regarding self-configuration, greenfield design methodologies for wireless networks based on mobile communication technologies are based on the premise that users have mobility characteristics. However, in fixed wireless access networks, the antennas of the users are in fixed locations. Therefore, this thesis proposes a novel methodology for finding the optimal locations were to deploy network equipment as well as the configuration of their radio parameters in Fixed Wireless Access networks. Regarding self-optimization of networks, current algorithms make use of signal maps of the cells in the network so that the changes that these maps would experience after modifying any network parameter can be estimated. In order to obtain these maps, operators use predictive models calibrated through real network measurements

    Opportunistic Third-Party Backhaul for Cellular Wireless Networks

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    With high capacity air interfaces and large numbers of small cells, backhaul -- the wired connectivity to base stations -- is increasingly becoming the cost driver in cellular wireless networks. One reason for the high cost of backhaul is that capacity is often purchased on leased lines with guaranteed rates provisioned to peak loads. In this paper, we present an alternate \emph{opportunistic backhaul} model where third parties provide base stations and backhaul connections and lease out excess capacity in their networks to the cellular provider when available, presumably at significantly lower costs than guaranteed connections. We describe a scalable architecture for such deployments using open access femtocells, which are small plug-and-play base stations that operate in the carrier's spectrum but can connect directly into the third party provider's wired network. Within the proposed architecture, we present a general user association optimization algorithm that enables the cellular provider to dynamically determine which mobiles should be assigned to the third-party femtocells based on the traffic demands, interference and channel conditions and third-party access pricing. Although the optimization is non-convex, the algorithm uses a computationally efficient method for finding approximate solutions via dual decomposition. Simulations of the deployment model based on actual base station locations are presented that show that large capacity gains are achievable if adoption of third-party, open access femtocells can reach even a small fraction of the current market penetration of WiFi access points.Comment: 9 pages, 6 figure

    Detection and compensation methods for self-healing in self-organizing networks

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    Uno de los elementos clave en la definición de los recientes estándares de comunicaciones móviles del 3rd Generation Partnership Project (3GPP), LTE (Long Term Evolution) y LTEAdvanced, es la consideración de funciones que se puedan ejecutar de manera automática. Este tipo de redes se conocen como redes Auto-Organizadas (Self-Organizing Networks, SON). Las funciones SON permiten hacer frente al importante incremento en tamaño y complejidad que han experimentado las redes de comunicaciones móviles en los últimos años. El número de usuarios es cada vez mayor y los servicios requieren gran cantidad de recursos y altas tasas de transmisión por lo que la gestión de estas redes se está convirtiendo en una tarea cada vez más compleja. Además, cuando las redes de quinta generación (5G) se implanten, la complejidad y el coste asociado a estas nuevas redes será todavía mayor. En este contexto, las funciones SON resultan imprescindibles para llevar a cabo la gestión de estas redes tan complejas. El objetivo de SON es definir un conjunto de funcionalidades que permitan automatizar la gestión de las redes móviles. Mediante la automatización de las tareas de gestión y optimización es posible reducir los gastos de operación y capital (OPEX y CAPEX). Las funciones SON se clasifican en tres grupos: Auto- Configuración, Auto-Optimización y Auto-Curación. Las funciones de Auto-Configuración tienen como objetivo la definición de los distintos parámetros de configuración durante la fase de planificación de una red o después de la introducción de un nuevo elemento en una red ya desplegada. Las funciones de Auto-Optimización pretenden modificar los parámetros de configuración de una red para maximizar el rendimiento de la misma y adaptarse a distintos escenarios. Las funciones de Auto- Curación tienen como objetivo detectar y diagnosticar posibles fallos en la red que afecten al funcionamiento de la misma de manera automática. Cuando un fallo es detectado en una celda este puede ser recuperado (función de recuperación) o compensado (función de compensación). Uno de los principales desafíos relacionado con las funciones SON es el desarrollo de métodos eficientes para la automatización de las tareas de optimización y mantenimiento de una red móvil. En este sentido, la comunidad científica ha centrado su interés en la definición de métodos de Auto-Configuración y Auto-Optimización siendo las funciones de Auto-Curación las menos exploradas. Por esta razón, no es fácil encontrar algoritmos de detección y compensación realmente eficientes. Muchos estudios presentan métodos de detección y compensación que producen buenos resultados pero a costa de una gran complejidad. Además, en muchos casos, los algoritmos de detección y compensación se presentan como solución general para distintos tipos de fallo lo que hace que disminuya la efectividad. Por otro lado, la investigación ha estado tradicionalmente enfocada a la búsqueda de soluciones SON basadas en modelos analíticos o simulados. Sin embargo, el principal desafío ahora está relacionado con la explotación de datos reales disponibles con el objetivo de crear una base del conocimiento útil que maximice el funcionamiento de las actuales soluciones SON. Esto es especialmente interesante en el área de las funciones de Auto-Curación. En este contexto, la disponibilidad de un histórico de datos es crucial para entender cómo funciona la red en condiciones normales o cuando se producen fallos y como estos fallos afectan a la calidad de servicio experimentada por los usuarios. El principal objetivo de esta tesis es el desarrollo de algoritmos eficientes de detección y compensación de fallos en redes móviles. En primer lugar, se propone un método de detección de celdas caídas basado en estadísticas de traspasos. Una de las principales características de este algoritmo es que su simplicidad permite detectar celdas caídas en cualquier red inmediatamente después de acceder a los indicadores de funcionamiento de la misma. En segundo lugar, una parte importante de la tesis está centrada en la función de compensación. Por un lado, se propone una novedosa metodología de compensación de celdas caídas. Este nuevo método permite adaptar la compensación a la degradación específica provocada por la celda caída. Una vez que se detecta un problema de celda caída, se realiza un análisis de la degradación producida por este fallo en las celdas vecinas. A continuación, diferentes algoritmos de compensación se aplican a las distintas celdas vecinas en función del tipo de degradación detectado. En esta tesis se ha llevado a cabo un estudio de esta fase de análisis utilizando datos de una red real actualmente en uso. Por otro lado, en esta tesis también se propone un método de compensación que considera un fallo diferente al de celda caída. En concreto, se propone un método de compensación para un fallo de cobertura débil basado en modificaciones del margen de traspaso. Por último, aunque es interesante evaluar los métodos propuestos en redes reales, no siempre es posible. Los operadores suelen ser reacios a probar métodos que impliquen cambios en los parámetros de configuración de los elementos de la red. Por esta razón, una parte de esta tesis ha estado centrada en la implementación de un simulador dinámico de nivel de sistema que permita la evaluación de los métodos propuestos

    Self organising cloud cells: a resource efficient network densification strategy

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    Network densification is envisioned as the key enabler for 2020 vision that requires cellular systems to grow in capacity by hundreds of times to cope with unprecedented traffic growth trends being witnessed since advent of broadband on the move. However, increased energy consumption and complex mobility management associated with network densifications remain as the two main challenges to be addressed before further network densification can be exploited on a wide scale. In the wake of these challenges, this paper proposes and evaluates a novel dense network deployment strategy for increasing the capacity of future cellular systems without sacrificing energy efficiency and compromising mobility performance. Our deployment architecture consists of smart small cells, called cloud nodes, which provide data coverage to individual users on a demand bases while taking into account the spatial and temporal dynamics of user mobility and traffic. The decision to activate the cloud nodes, such that certain performance objectives at system level are targeted, is carried out by the overlaying macrocell based on a fuzzy-logic framework. We also compare the proposed architecture with conventional macrocell only deployment and pure microcell-based dense deployment in terms of blocking probability, handover probability and energy efficiency and discuss and quantify the trade-offs therein
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