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

Adaptive Neuro-Fuzzy Inference System for Dynamic Load Balancing in 3GPP LTE

ANFIS is applicable in modeling of key parameters when investigating the performance and functionality of wireless networks. The need to save both capital and operational expenditure in the management of wireless networks cannot be over-emphasized. Automation of network operations is a veritable means of achieving the necessary reduction in CAPEX and OPEX. To this end, next generations networks such WiMAX and 3GPP LTE and LTE-Advanced provide support for self-optimization, self-configuration and self-healing to minimize human-to-system interaction and hence reap the attendant benefits of automation. One of the most important optimization tasks is load balancing as it affects network operation right from planning through the lifespan of the network. Several methods for load balancing have been proposed. While some of them have a very buoyant theoretical basis, they are not practically implementable at the current state of technology. Furthermore, most of the techniques proposed employ iterative algorithm, which in itself is not computationally efficient. This paper proposes the use of soft computing, precisely adaptive neuro-fuzzy inference system for dynamic QoS-aware load balancing in 3GPP LTE. Three key performance indicators (i.e. number of satisfied user, virtual load and fairness distribution index) are used to adjust hysteresis task of load balancing

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

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-Healing in LTE networks with unsupervised learning techniques

Recently the cellular networks are getting more complex in maintenance and network management, and rapidly growing in the number of users so that repairing and maintenance of the system are becoming more challenging and expensive. To solve the problems and maintain the system, operators depend on their experience but by increasing in type and density of the networks, this way will not operate as before. So Self-organizing network (SON) has been used in this study to solve these issues

Self-Healing in LTE networks with unsupervised learning techniques

Recently the cellular networks are getting more complex in maintenance and network management, and rapidly growing in the number of users so that repairing and maintenance of the system are becoming more challenging and expensive. To solve the problems and maintain the system, operators depend on their experience but by increasing in type and density of the networks, this way will not operate as before. So Self-organizing network (SON) has been used in this study to solve these issues

LTE-verkon suorituskyvyn parantaminen CDMA2000:sta LTE:hen tehdyn muutoksen jälkeen

CDMA2000 technology has been widely used on 450 MHz band. Recently the equipment availability and improved performance offered by LTE has started driving the operators to migrate their networks from CDMA2000 to LTE. The migration may cause the network performance to be in suboptimal state. This thesis presents four methods to positively influence LTE network performance after CDMA2000 to LTE migration, especially on 450 MHz band. Furthermore, three of the four presented methods are evaluated in a live network. The measured three methods were cyclic prefix length, handover parameter optimization and uplink coordinated multipoint (CoMP) transmission. The objective was to determine the effectiveness of each method. The research methods included field measurements and network KPI collection. The results show that normal cyclic prefix length is enough for LTE450 although the cell radius may be up to 50km. Only special cases exist where cyclic prefix should be extended. Operators should consider solving such problems individually instead of widely implementing extended cyclic prefix. Handover parameter optimization turned out to be an important point of attention after CDMA2000 to LTE migration. It was observed that if the handover parameters are not concerned, significant amount of unnecessary handovers may happen. It was evaluated that about 50% of the handovers in the network were unnecessary in the initial situation. By adjusting the handover parameter values 47,28 % of the handovers per user were removed and no negative effects were detected. Coordinated multipoint transmission has been widely discussed to be an effective way to improve LTE network performance, especially at the cell edges. Many challenges must be overcome before it can be applied to downlink. Also, implementing it to function between cells in different eNBs involve challenges. Thus, only intra-site uplink CoMP transmission was tested. The results show that the performance improvements were significant at the cell edges as theory predicted.CDMA2000 teknologiaa on laajalti käytetty 450 MHz:n taajuusalueella. Viime aikoina LTE:n tarjoamat halvemmat laitteistot ja parempi suorituskyky ovat kannustaneet operaattoreita muuttamaan verkkoaan CDMA2000:sta LTE:hen. Kyseinen muutos saattaa johtaa epäoptimaaliseen tilaan verkon suorituskyvyn kannalta. Tämä työ esittelee neljä menetelmää, joilla voidaan positiivisesti vaikuttaa LTE-verkon suorituskykyyn CDMA2000:ste LTE:hen tehdyn muutoksen jälkeen erityisesti 450 MHz:n taajuusalueella. Kolmea näistä menetelmistä arvioidaan tuotantoverkossa. Nämä kolme menetelmää ovat suojavälin pituus, solunvaihtoparametrien optimointi ja ylälinkin koordinoitu monipistetiedonsiirto. Tavoite oli määrittää kunkin menetelmän vaikutus. Tutkimusmenetelmiin kuului kenttämittaukset ja verkon suorituskykymittareiden analyysi. Tutkimustulosten perusteella voidaan sanoa, että normaali suojaväli on riittävän pitkä LTE450:lle vaikka solujen säde on jopa 50km. Vain erikoistapauksissa tarvitaan pidennettyä suojaväliä. Operaattoreiden tulisi ratkaista tällaiset tapaukset yksilöllisesti sen sijaan, että koko verkossa käytettäisiin pidennettyä suojaväliä. Solunvaihtoparametrien optimointi osoittautui tärkeäksi huomion aiheeksi CDMA2000:sta LTE:hen tehdyn muutoksen jälkeen. Turhia solunvaihtoja saattaa tapahtua merkittäviä määriä, mikäli parametreihin ei kiinnitetä huomiota. Lähtötilanteessa noin 50 % testiverkon solunvaihdoista arvioitiin olevan turhia. Solunvaihtoparametreja muuttamalla 47,28 % solunvaihdoista per käyttäjä saatiin poistettua ilman, että mitään haittavaikutuksia olisi huomattu. Koordinoidun monipistetiedonsiirron on laajalti sanottu olevan tehokas tapa parantaa LTE-verkon suorituskykyä, etenkin solujen reunoilla. Monia haasteita pitää ratkaista, enne kuin sitä voidaan käyttää alalinkin tiedonsiirtoon. Lisäksi sen käyttöön eri tukiasemien solujen välillä liittyy haasteita. Tästä syystä monipistetiedonsiirtoa voitiin testata vain ylälinkin suuntaan ja vain yhden tukiaseman välisten solujen kesken. Tulokset osoittivat, että suorituskyky parani merkittävästi solun reunalla

Graph-based prediction of missing KPIs through optimization and random forests for KPI systems

Key performance indicators (KPIs) are widely used to monitor and control the production in industry. On an aggregated level, often represented as graphs or interrelated KPI systems, a comprehensive overview is given. However, missing or inaccurate sensor data and KPIs, as well inconsistencies in KPI based management are a major hurdle disturbing operations. To counter the impact of such missing KPIs, we propose a value optimization based approach to reconstruct the values of missing KPIs within a KPI system. While the approach shows successful reconstruction in the case study, the value optimization can be sped up through a random forest prediction of the initial optimization set. Thus, the inclusion of previous knowledge about the system behavior proves beneficial and superior to the pure optimization based approach, as validated by both randomized and simulation-based measurement data

Interoperability and Quality Assurance for Multi-Vendor LTE Network

The deployment of the LTE is picking up pace in many countries and these networks are deployed alongside the existing 2G/3G services. LTE/LTE-A networks offer higher data rates and reduced delay to the subscribers. Today's mobile networks consist of equipment from multiple vendors and they are called multiple vendor networks. Interoperability testing is important at initial network launch and during network expansion. This paper discusses a typical problem related to interoperability testing along with the test results and the issues faced during the testing. The test results discussed in the paper are obtained from three scenarios - before testing, during testing and after testing. The test results are used to study the impact on network performance. Apart from the interoperability testing, an outline of testing that focus on general network stability, the interworking capability of LTE with other technologies such as 2G and 3G and taxonomy for the generation of key performance indicators (KPIs) are also discussed

Accessibility Degradation Prediction on LTE/SAE Network Using Discrete Time Markov Chain (DTMC) Model

In this paper, an algorithm for predicting accessibility performance on an LTE/SAE network based on relevant historical key performance indicator (KPI) data is proposed. Since there are three KPIs related to accessibility, each representing different segments, a method to map these three KPI values onto the status of accessibility performance is proposed. The network conditions are categorized as high, acceptable or low for each time interval of observation. The first state shows that the system is running optimally, while the second state shows that the system has deteriorated and needs full attention, and the third state indicates that the system has gone into degraded conditions that cannot be tolerated. After the state sequence has been obtained, a transition probability matrix can be derived, which can be used to predict future conditions using a DTMC model. The results obtained are system predictions in terms of probability values for each state for a specific future time. These prediction values are required for proactive health monitoring and fault management. Accessibility degradation prediction is then conducted by using measurement data derived from an eNodeB in the LTE network for a period of one month