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

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 New Paradigm for Proactive Self-Healing in Future Self-Organizing Mobile Cellular Networks

Mobile cellular network operators spend nearly a quarter of their revenue on network management and maintenance. Remarkably, a significant proportion of that budget is spent on resolving outages that degrade or disrupt cellular services. Historically, operators have mainly relied on human expertise to identify, diagnose and resolve such outages while also compensating for them in the short-term. However, with ambitious quality of experience expectations from 5th generation and beyond mobile cellular networks spurring research towards technologies such as ultra-dense heterogeneous networks and millimeter wave spectrum utilization, discovering and compensating coverage lapses in future networks will be a major challenge. Numerous studies have explored heuristic, analytical and machine learning-based solutions to autonomously detect, diagnose and compensate cell outages in legacy mobile cellular networks, a branch of research known as self-healing. This dissertation focuses on self-healing techniques for future mobile cellular networks, with special focus on outage detection and avoidance components of self-healing. Network outages can be classified into two primary types: 1) full and 2) partial. Full outages result from failed soft or hard components of network entities while partial outages are generally a consequence of parametric misconfiguration. To this end, chapter 2 of this dissertation is dedicated to a detailed survey of research on detecting, diagnosing and compensating full outages as well as a detailed analysis of studies on proactive outage avoidance schemes and their challenges. A key observation from the analysis of the state-of-the-art outage detection techniques is their dependence on full network coverage data, susceptibility to noise or randomness in the data and inability to characterize outages in both spacial domain and temporal domain. To overcome these limitations, chapters 3 and 4 present two unique and novel outage detection techniques. Chapter 3 presents an outage detection technique based on entropy field decomposition which combines information field theory and entropy spectrum pathways theory and is robust to noise variance. Chapter 4 presents a deep learning neural network algorithm which is robust to data sparsity and compares it with entropy field decomposition and other state-of-the-art machine learning-based outage detection algorithms including support vector machines, K-means clustering, independent component analysis and deep auto-encoders. Based on the insights obtained regarding the impact of partial outages, chapter 5 presents a complete framework for 5th generation and beyond mobile cellular networks that is designed to avoid partial outages caused by parametric misconfiguration. The power of the proposed framework is demonstrated by leveraging it to design a solution that tackles one of the most common problems associated with ultra-dense heterogeneous networks, namely imbalanced load among small and macro cells, and poor resource utilization as a consequence. The optimization problem is formulated as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization and load balancing self-organizing network functions

Mobilfunknetzmanagement im Kontext von Realistischen Heterogenen Szenarien

Every generation of mobile radio communication standards leads to a new level of complexity in the cellular systems. Moreover, due to the ever-increasing data traffic demands of mobile users as well as declining revenues in recent years, the operators of such networks have to deal with all those network administration difficulties in the most efficient manner. One promising approach that shall relieve the operator from time-consuming manual tasks is to use so-called Self-Organising Network (SON) functionalities. SON functions monitor the performance of the network and change the (radio) parameters accordingly, based on internal algorithms that focus on dedicated optimisation goals. This work investigates whether SON functions can be used to enforce Key Performance Indicator (KPI) targets demanded by the operators. Therefore, the impact of SON on the network manageability and performance is studied by using SON functions that consider multiple technologies (i.e. LTE and WLAN) and different cell layers (macro and small cells). The evaluations are based on sophisticated system-level simulations that rely on an in-house developed platform called ``SiMoNe'' (Simulator for Mobile Networks). Moreover, the foundations of the scenarios used are realistically planned mobile networks on the one hand, and advanced mobility models with a particular emphasis on realistic movements and behaviours, on the other hand. As a preparatory step, the newly introduced mobility models are investigated regarding the handover performance. The results show that the behaviour and nature of the movements have a profound impact on the overall network performance. After that, three well-known SON functions are tested that operate in the domain of self-optimisation. This is done by varying SON algorithm parameterisation values in three distinct network environments. The insights gained into the behaviour of the SON functions are then used to manage a complex heterogeneous cellular network by setting appropriate SON parametrisation values that alter the behaviour of SON functions accordingly. By that, the formulated KPI goals can be achieved. However, the evaluations show that the implementations of the objectives are only doable to some extent in realistic settings due to the compound and inhomogeneous nature of the network scenarios.Jede neue Mobilfunk-Generation sorgt dafür, dass die Komplexität in den Netzen zunimmt. Außerdem führt die immer weiter steigende Nachfrage nach mobilem Datenverkehr sowie sinkende Einnahmen dazu, dass die Betreiber solcher Netze mit administrativen Aufgaben in möglichst effizienter Weise umgehen müssen. Eine Möglichkeit stellen sogenannte Selbst-Organisierende Netze (engl. Self-Organising Network (SON)) dar, um den Betreiber von zeitaufwendigen manuellen Arbeiten zu befreien. SON Funktionen überwachen Kenngrößen im Netz und ändern, je nach Zielfunktion des Algorithmus, entsprechende (Radio-)Parameter im Netz. Diese Dissertation untersucht, ob SON Funktionen geeignet sind um ein Mobilfunknetz zu steuern und somit vorgegebene Zielvorgaben der Netzbetreiber umzusetzen. Die verwendeten SON Funktionen arbeiten hierbei mit unterschiedlichen Technologien (z.B. LTE und WLAN) und auf mehreren Zellschichten (Makro- bis Femtozellen). Als Simulationsumgebung wird auf die leistungsfähige Plattform ``SiMoNe'' (engl. Simulator for Mobile Networks) zurückgegriffen. Die Simulationsgrundlagen bilden einerseits realistisch geplante Mobilfunknetze und anderseits fortschrittliche Mobilitätsmodelle, wobei eine besondere Betonung auf die realistische Umsetzung von Bewegung und Verhalten der Nutzer gelegt wird. In einem vorbereitenden Schritt werden neuartige Mobilitätsmodelle auf ihr Handover-Verhalten untersucht. Die Ergebnisse zeigen hierbei, dass das Verhalten und die Bewegung einen entscheidenden Einfluss auf die Netzperformance haben können. Im Anschluss werden drei bekannte SON Funktionen in drei unterschiedlichen Netzumgebungen getestet. Dies geschieht durch eine Variation der Parameterwerte der SON Algorithmen, welche das Verhalten der Funktionen verändern und somit auch die Netzperformances entscheidend beeinflussen kann. Die über das Verhalten der SON Funktionen gesammelten Erkenntnisse werden letztendlich genutzt, um Zielvorgaben an ein komplexes heterogenes Mobilfunknetzwerk zu realisieren. Die Auswertungen zeigen, dass dies nur in einem gewissen Maße geschehen kann. Die hohe Komplexität und die inhomogene Topologie der Netze beeinträchtigen eine zielgenaue Veränderung der Netzperformance entscheidend

Self-organization for 5G and beyond mobile networks using reinforcement learning

The next generations of mobile networks 5G and beyond, must overcome current networks limitations as well as improve network performance. Some of the requirements envisioned for future mobile networks are: addressing the massive growth required in coverage, capacity and traffic; providing better quality of service and experience to end users; supporting ultra high data rates and reliability; ensuring latency as low as one millisecond, among others. Thus, in order for future networks to enable all of these stringent requirements, a promising concept has emerged, self organising networks (SONs). SONs consist of making mobile networks more adaptive and autonomous and are divided in three main branches, depending on their use-cases, namely: self-configuration, self-optimisation, and self-healing. SON is a very promising and broad concept, and in order to enable it, more intelligence needs to be embedded in the mobile network. As such, one possible solution is the utilisation of machine learning (ML) algorithms. ML has many branches, such as supervised, unsupervised and Reinforcement Learning (RL), and all can be used in different SON use-cases. The objectives of this thesis are to explore different RL techniques in the context of SONs, more specifically in self-optimization use-cases. First, the use-case of user-cell association in future heterogeneous networks is analysed and optimised. This scenario considers not only Radio Access Network (RAN) constraints, but also in terms of the backhaul. Based on this, a distributed solution utilizing RL is proposed and compared with other state-of-the-art methods. Results show that the proposed RL algorithm outperforms current ones and is able to achieve better user satisfaction, while minimizing the number of users in outage. Another objective of this thesis is the evaluation of Unmanned Aerial vehicles (UAVs) to optimize cellular networks. It is envisioned that UAVs can be utilized in different SON use-cases and integrated with RL algorithms to determine their optimal 3D positions in space according to network constraints. As such, two different mobile network scenarios are analysed, one emergency and a pop-up network. The emergency scenario considers that a major natural disaster destroyed most of the ground network infrastructure and the goal is to provide coverage to the highest number of users possible using UAVs as access points. The second scenario simulates an event happening in a city and, because of the ground network congestion, network capacity needs to be enhanced by the deployment of aerial base stations. For both scenarios different types of RL algorithms are considered and their complexity and convergence are analysed. In both cases it is shown that UAVs coupled with RL are capable of solving network issues in an efficient and quick manner. Thus, due to its ability to learn from interaction with an environment and from previous experience, without knowing the dynamics of the environment, or relying on previously collected data, RL is considered as a promising solution to enable SON