A Distributed SON-Based User-Centric Backhaul Provisioning Scheme

5G definition and standardization projects are well underway, and governing characteristics and major challenges have been identified. A critical network element impacting the potential performance of 5G networks is the backhaul, which is expected to expand in length and breadth to cater to the exponential growth of small cells while offering high throughput in the order of gigabit per second and less than 1 ms latency with high resilience and energy efficiency. Such performance may only be possible with direct optical fiber connections that are often not available country-wide and are cumbersome and expensive to deploy. On the other hand, a prime 5G characteristic is diversity, which describes the radio access network, the backhaul, and also the types of user applications and devices. Thus, we propose a novel, distributed, self-optimized, end-to-end user-cell-backhaul association scheme that intelligently associates users with candidate cells based on corresponding dynamic radio and backhaul conditions while abiding by users' requirements. Radio cells broadcast multiple bias factors, each reflecting a dynamic performance indicator (DPI) of the end-to-end network performance such as capacity, latency, resilience, energy consumption, and so on. A given user would employ these factors to derive a user-centric cell ranking that motivates it to select the cell with radio and backhaul performance that conforms to the user requirements. Reinforcement learning is used at the radio cells to optimise the bias factors for each DPI in a way that maximise the system throughput while minimising the gap between the users' achievable and required end-to-end quality of experience (QoE). Preliminary results show considerable improvement in users' QoE and cumulative system throughput when compared with the state-of-the-art user-cell association schemes

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

A Review of Wireless Sensor Networks with Cognitive Radio Techniques and Applications

The advent of Wireless Sensor Networks (WSNs) has inspired various sciences and telecommunication with its applications, there is a growing demand for robust methodologies that can ensure extended lifetime. Sensor nodes are small equipment which may hold less electrical energy and preserve it until they reach the destination of the network. The main concern is supposed to carry out sensor routing process along with transferring information. Choosing the best route for transmission in a sensor node is necessary to reach the destination and conserve energy. Clustering in the network is considered to be an effective method for gathering of data and routing through the nodes in wireless sensor networks. The primary requirement is to extend network lifetime by minimizing the consumption of energy. Further integrating cognitive radio technique into sensor networks, that can make smart choices based on knowledge acquisition, reasoning, and information sharing may support the network's complete purposes amid the presence of several limitations and optimal targets. This examination focuses on routing and clustering using metaheuristic techniques and machine learning because these characteristics have a detrimental impact on cognitive radio wireless sensor node lifetime

A survey of self organisation in future cellular networks

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

Cell fault management using machine learning techniques

This paper surveys the literature relating to the application of machine learning to fault management in cellular networks from an operational perspective. We summarise the main issues as 5G networks evolve, and their implications for fault management. We describe the relevant machine learning techniques through to deep learning, and survey the progress which has been made in their application, based on the building blocks of a typical fault management system. We review recent work to develop the abilities of deep learning systems to explain and justify their recommendations to network operators. We discuss forthcoming changes in network architecture which are likely to impact fault management and offer a vision of how fault management systems can exploit deep learning in the future. We identify a series of research topics for further study in order to achieve this

Self organisation for 4G/5G networks

Nowadays, the rapid growth of mobile communications is changing the world towards a fully connected society. Current 4G networks account for almost half of total mobile traffic, and in the forthcoming years, the overall mobile data traffic is expected to dramatically increase. To manage this increase in data traffic, operators adopt network topologies such as Heterogeneous Networks. Thus, operators can de­ ploy hundreds of small cells for each macro cell, allowing them to reduce coverage hales and/or lack of capacity. The advent of this technology is expected to tremendously increase the number of nodes in this new ecosystem, so that traditional network management activities based on, e.g., classic manual and field trial design approaches are just not be viable anymore. As a consequence, the academic J literature has dedicated a significant amount of effort to Self-Organising Network (SON) algorithms. These solutions aim to bring intelligence and autonomous adaptability into cellular networks, thereby reducing capital and operation expenditures (CAPEX/OPEX). Another aspect to take into account is that, these type of networks generate a large amount of data during their normal operation in the form of control, management and data measurements. This data is expected to increase in SG due to different aspects, such as densification, heterogeneity in layers and technologies, additional control and management complexity in Network Functions Virtualisation (NFV) and Software Defined Network (SDN), and the advent of the Internet of Things (loT), among others. In this context, operators face the challenge of de ­ signing efficient technologies, while introducing new services, reaching challenges in terms networks, which are self-aware, self-adaptive, and intelligent. This dissertation provides a contribution to the design, analysis, and evaluation of SON solutions to improve network opera tor performance, expenses, and users' experience, by making the network more self-adaptive and intelligent. It also provides a contribution to the design of a self-aware network planning tool, which allows to predict the Quality of Service (QoS) offered to end-users, based on data al ­ ready available in the network . The main thesis contributions are divided into two parts. The first part presents a novel functional architecture based on an automatic and self-organised Reinforcement Learning (RL) based approach to model SON functionalities, in which the main task is the self-coordination of different actions taken by different SON functions to be automatically executed in a self-organised realistic Long Term Evolution (LTE) network. The proposed approach introduces a new paradigm to deal with the conflicts genera ted by the concurrent execution of multiple SON functions, revealing that the proposed approach is general enough to modelali the SON functions and their derived conflicts. The second part of the thesis is dedicated to the problem of QoS prediction. In particular, we aim at finding patterns of knowledge from physical layer data acquired from heterogeneous LTE networks. We propose an approach that not only is able to verify the QoS level experienced by the users, through physical layer measurements of the UEs, but it is a lso able to predict it based on measurements collected at different time, and from different regions of the heterogeneous network. We propose then to make predictions independently of the physical location, in order to exploit the experience gained in other sectors of the network, to properly dimension and deploy heterogeneous nodes. In this context, we use Machine Learning (ML) as a tool to allow the network to learn from experience, improving performances, and big data analytics to drive the network from reactive to predictive.Hoy en día, el rápido crecimiento de las comunicaciones móviles está cambiando el mundo hacia una sociedad completamente conectada. Las redes 4G actuales representan casi la mitad del tráfico móvil total, y en los próximos años se espera que el tráfico total de los dispositivos móviles aumente drásticamente. Para gestionar este incremento de tráfico de datos, los operadores adoptan tecnologías de redes como las redes heterogéneas. De esta manera, los operadores pueden desplegar centena res de pequeñas celdas por cada macro celda, permitiendo reducir zonas sin cobertura y/o falta de capacidad. Con la introducción de esta tecnología, se espera que incremente de manera sustancia l el número de nodos en el nuevo ecosistema, de manera que las actividades de gestión de las redes tradicionales, basadas en, por ejemplo, el diseño manual, sean inviables. Como consecuencia, la literatura académica ha dedicado un esfuerzo significativo al diseño de algoritmos de redes auto-organizadas (SON). Estas soluciones tienen como objetivo introducir inteligencia y capacidad autónoma a las redes móviles, reduciendo la capacidad y costes operativos. Otro aspecto a tener en cuenta es que este tipo de redes generan una gran cantidad de datos durante su funcionamiento habitual, en forma de medidas de control y gestión de datos. Se espera que estos datos incrementen con la tecnología SG, debido a diferentes aspectos como los son la densificación de redes heterogéneas, la complejidad adicional en el control y la gestión de la virtualización de las funciones de redes (NFV) y las redes definidas por software (SON), así como la llegada del internet de las cosas (loT), entre otros. En este contexto, los operadores se enfrentan al reto de diseñar tecnologías eficientes, mientras introducen nuevos servicios, consiguiendo objetivos en términos de satisfacción del cliente, en donde el objetivo global del operador es la construcción de redes auto-conscientes, auto-adaptables e inteligentes. Esta tesis ofrece una contribución al diseño y evaluación de soluciones SON para mejorar el rendimiento de las redes, los costes y la experiencia de los usuarios, consiguiendo que la red sea auto-adaptable e inteligente. Así mismo, proporciona una contribución al diseño de una herramienta de planificación de red auto-consciente, que permita predecir la calidad de servicio brindada a los usuarios finales, basada en la explotación de datos disponibles en la red.Avui en dia, el ràpid creixement de les comunicacions mòbils està canviant el món cap a una societat completament connectada. Les xarxes 4G actuals representen casi la m trànsit mòbil total, i en els propers anys s’espera que el trànsit total de dades mòbils augmenti dràsticament. Per gestionar aquest increment de trànsit de dades, els operadors adopten topologies de xarxa com ara les xarxes heterogènies (HetNets). D’aquesta manera, els operadors poden desplegar centenars de cel·les petites per a cada cella macro, permetent reduir forats en la cobertura i/o la manca de capacitat. Amb l’arribada d’aquesta tecnologia, s’espera que incrementi enormement el nombre de nodes en el nou ecosistema, de manera que les activitats de gestió de xarxa tradicionals, basades en, per exemple, el disseny manual i els assaigs de camp esdevenen simplement inviables. Com a conseqüència, la literatura acadèmica ha dedicat una quantitat significativa d’esforç als algorismes de xarxa auto organitzada (SON). Aquestes solucions tenen com a objectiu portar la intel·ligència i capacitat d’adaptació autònoma a les xarxes mòbils, reduint el capital i les despeses operatives (CAPES/OPEX). Un altre aspecte a tenir en compte és que aquest tipus de xarxes generen una gran quantitat de dades durant el seu funcionament habitual, en forma de mesuraments de control, gestió i dades. S’espera que aquestes dades incrementin amb la tecnologia 5G, degut a diferents aspectes com ara la densificació, l’heterogeneïtat en capes i tecnologies, la complexitat addicional en el control i la gestió de la virtualització de les funcions de xarxa (NFV) i xarxes definides per software (SDN), i l’adveniment de la internet de les coses (IoT), entre d’altres. En aquest context, els operadors s’enfronten al repte de dissenyar tecnologies eficients, mentre introdueixen nous serveis, aconseguint objectius en termes de satisfacció del client, i on l’objectiu global d’un operador és la construcció de xarxes que són autoconscients, auto-adaptables i intel·ligents. Aquesta tesis ofereix una contribució al disseny, l’anàlisi i l’avaluació de les solucions SON per millorar el rendiment de l’operador de xarxa, les xi despeses i l’experiència dels usuaris, fent que la xarxa sigui més auto-adaptable i intel·ligent. També proporciona una contribució al disseny d’una eina de planificació de xarxa autoconscient, el que permet predir la qualitat de servei (QoS) oferta als usuaris finals, basada en dades ja disponibles a la xarxa. Les contribucions principals d’aquesta tesis es divideixen en dues parts. La primera part presenta una nova arquitectura funcional basada en un aprenentatge per reforç (RL) automàtic i auto-organitzat, enfocat en modelar funcionalitats SON, on la tasca principal és l’auto-coordinació de les diferents accions dutes a terme perles diferents funcions SON a ser executades de forma automàtica en una xarxa Long Term Evolution (LTE) auto-organitzada. L’enfocament proposat introdueix un nou paradigma perfer front als conflictes generats per l’execució simultània de múltiples funcions SON, revelant que l’enfocament proposat és prou general per modelar totes les funcions SON i els seus conflictes derivats. La segona part de la tesis està dedicada al problema de la predicció de la qualitat de servei. En particular, el nostre objectiu és trobar patrons de coneixement a partir de dades de la capa física adquirides de xarxes LTE heterogènies. Proposem un enfocament que no només és capaç de verificar el nivell de QoS experimentat pels usuaris, a través de mesuraments de la capa física dels UEs, sinó que també és capaç de predir-ho basant-se en mesuraments adquirits en diferents instants, i de diferents regions de la xarxa heterogènia. Proposem per tant fer prediccions amb independència de la ubicació física, aprofitant l’experiència adquirida en altres sectors de la xarxa, per dimensionar i desplegar nodes heterogenis correctament. En aquest context, utilitzem l’aprenentatge automàtic (ML) com a eina per permetre que la xarxa aprengui de l’experiència, millorant el rendiment, i l’anàlisi de grans volums de dades per a conduir la xarxa de reactiva a predictiva. Durant l’elaboració d’aquesta tesis, s’han extret dues conclusions principals clau. En primer lloc, destaquem la importància de dissenyar algorismes SON eficients per fer front eficaçment a diversos reptes, com ara la ubicació més adequada de funcions SON i algorismes per resoldre adequadament el problema d’implementació distribuïda o centralitzada, o la solució de conflictes entre funcions SON executades a diferents nodes o xarxes. En segon lloc, en termes d’eines de planificació de xarxes, es poden trobar diferents eines cobrint una àmplia gamma de sistemes i aplicacions orientades a la indústria, així com per a fins d’investigació. En aquest context, les solucions investigades són sotmeses contínuament a canvis importants, on un del principals impulsors és presentar solucions més rentable