Adaptive Network Based Fuzzy Inference System Model For Minimizing Handover Failure in Mobile Networks

For seamless connection between mobile users on the same and different mobile technologies there is need for the deployment of a more complex algorithm for a successful switching of mobile users. Signal to interference ratio, speed of the mobile users and traffic distance are the three input used in the Adaptive network based Fuzzy inference system (ANFIS) which is an hybrid of two techniques of artificial intelligence which make it suitable to handle complexities such as ping-pong effect and interference which impair on the quality of service (QoS) during call handover process as the mobile users move from one coverage area (cell) to anothe

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

Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed

Unsupervised Machine Learning for Networking:Techniques, Applications and Research Challenges

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances

Proposal of an adaptive infotainment system depending on driving scenario complexity

The PhD research project is framed within the plan of industrial doctorates of the “Generalitat de Catalunya”. During the investigation, most of the work was carried out at the facilities of the vehicle manufacturer SEAT, specifically at the information and entertainment (infotainment) department. In the same way, there was a continuous cooperation with the telematics department of the UPC. The main objective of the project consisted in the design and validation of an adaptive infotainment system dependent on the driving complexity. The system was created with the purpose of increasing driver’ experience while guaranteeing a proper level of road safety. Given the increasing number of application and services available in current infotainment systems, it becomes necessary to devise a system capable of balancing these two counterparts. The most relevant parameters that can be used for balancing these metrics while driving are: type of services offered, interfaces available for interacting with the services, the complexity of driving and the profile of the driver. The present study can be divided into two main development phases, each phase had as outcome a real physical block that came to be part of the final system. The final system was integrated in a vehicle and validated in real driving conditions. The first phase consisted in the creation of a model capable of estimating the driving complexity based on a set of variables related to driving. The model was built by employing machine learning methods and the dataset necessary to create it was collected from several driving routes carried out by different participants. This phase allowed to create a model capable of estimating, with a satisfactory accuracy, the complexity of the road using easily extractable variables in any modern vehicle. This approach simplify the implementation of this algorithm in current vehicles. The second phase consisted in the classification of a set of principles that allow the design of the adaptive infotainment system based on the complexity of the road. These principles are defined based on previous researches undertaken in the field of usability and user experience of graphical interfaces. According to these of principles, a real adaptive infotainment system with the most commonly used functionalities; navigation, radio and media was designed and integrated in a real vehicle. The developed system was able to adapt the presentation of the content according to the estimation of the driving complexity given by the block developed in phase one. The adaptive system was validated in real driving scenarios by several participants and results showed a high level of acceptance and satisfaction towards this adaptive infotainment. As a starting point for future research, a proof of concept was carried out to integrate new interfaces into a vehicle. The interface used as reference was a Head Mounted screen that offered redundant information in relation to the instrument cluster. Tests with participants served to understand how users perceive the introduction of new technologies and how objective benefits could be blurred by initial biases.El proyecto de investigación de doctorado se enmarca dentro del plan de doctorados industriales de la Generalitat de Catalunya. Durante la investigación, la mayor parte del trabajo se llevó a cabo en las instalaciones del fabricante de vehículos SEAT, específicamente en el departamento de información y entretenimiento (infotainment). Del mismo modo, hubo una cooperación continua con el departamento de telemática de la UPC. El objetivo principal del proyecto consistió en el diseño y la validación de un sistema de información y entretenimiento adaptativo que se ajustaba de acuerdo a la complejidad de la conducción. El sistema fue creado con el propósito de aumentar la experiencia del conductor y garantizar un nivel adecuado en la seguridad vial. El proyecto surge dado el número creciente de aplicaciones y servicios disponibles en los sistemas actuales de información y entretenimiento; es por ello que se hace necesario contar con un sistema capaz de equilibrar estas dos contrapartes. Los parámetros más relevantes que se pueden usar para equilibrar estas métricas durante la conducción son: el tipo de servicios ofrecidos, las interfaces disponibles para interactuar con los servicios, la complejidad de la conducción y el perfil del conductor. El presente estudio se puede dividir en dos fases principales de desarrollo, cada fase tuvo como resultado un componente que se convirtió en parte del sistema final. El sistema final fue integrado en un vehículo y validado en condiciones reales de conducción. La primera fase consistió en la creación de un modelo capaz de estimar la complejidad de la conducción en base a un conjunto de variables relacionadas con la conducción. El modelo se construyó empleando "Machine Learning Methods" y el conjunto de datos necesario para crearlo se recopiló a partir de varias rutas de conducción realizadas por diferentes participantes. Esta fase permitió crear un modelo capaz de estimar, con una precisión satisfactoria, la complejidad de la carretera utilizando variables fácilmente extraíbles en cualquier vehículo moderno. Este enfoque simplifica la implementación de este algoritmo en los vehículos actuales. La segunda fase consistió en la clasificación de un conjunto de principios que permiten el diseño del sistema de información y entretenimiento adaptativo basado en la complejidad de la carretera. Estos principios se definen en base a investigaciones anteriores realizadas en el campo de usabilidad y experiencia del usuario con interfaces gráficas. De acuerdo con estos principios, un sistema de entretenimiento y entretenimiento real integrando las funcionalidades más utilizadas; navegación, radio y audio fue diseñado e integrado en un vehículo real. El sistema desarrollado pudo adaptar la presentación del contenido según la estimación de la complejidad de conducción dada por el bloque desarrollado en la primera fase. El sistema adaptativo fue validado en escenarios de conducción reales por varios participantes y los resultados mostraron un alto nivel de aceptación y satisfacción hacia este entretenimiento informativo adaptativo. Como punto de partida para futuras investigaciones, se llevó a cabo una prueba de concepto para integrar nuevas interfaces en un vehículo. La interfaz utilizada como referencia era una pantalla a la altura de los ojos (Head Mounted Display) que ofrecía información redundante en relación con el grupo de instrumentos. Las pruebas con los participantes sirvieron para comprender cómo perciben los usuarios la introducción de nuevas tecnologías y cómo los sesgos iniciales podrían difuminar los beneficios