Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network

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

Service oriented networking for multimedia applications in broadband wireless networks

Extensive efforts have been focused on deploying broadband wireless networks. Providing mobile users with high speed network connectivity will let them run various multimedia applications on their wireless devices. In order to successfully deploy and operate broadband wireless networks, it is crucial to design efficient methods for supporting various services and applications in broadband wireless networks. Moreover, the existing access-oriented networking solutions are not able to fully address all the issues of supporting various applications with different quality of service requirements. Thus, service-oriented networking has been recently proposed and has gained much attention. This dissertation discusses the challenges and possible solutions for supporting multimedia applications in broadband wireless networks. The service requirements of different multimedia applications such as video streaming and Voice over IP (VoIP) are studied and some novel service-oriented networking solutions for supporting these applications in broadband wireless networks are proposed. The performance of these solutions is examined in WiMAX networks which are the promising technology for broadband wireless access in the near future. WiMAX networks are based on the IEEE 802.16 standards which have defined different Quality of Service (QoS) classes to support a broad range of applications with varying service requirements to mobile and stationary users. The growth of multimedia traffic that requires special quality of service from the network will impose new constraints on network designers who should wisely allocate the limited resources to users based on their required quality of service. An efficient resource management and network design depends upon gaining accurate information about the traffic profile of user applications. In this dissertation, the access level traffic profile of VoIP applications are studied first, and then a realistic distribution model for VoIP traffic is proposed. Based on this model, an algorithm to allocate resources for VoIP applications in WiMAX networks is investigated. Later, the challenges and possible solutions for transmitting MPEG video streams in wireless networks are discussed. The MPEG traffic model adopted by the WiMAX Forum is introduced and different application-oriented solutions for enhancing the performance of wireless networks with respect to MPEG video streaming applications are explained. An analytical framework to verify the performance of the proposed solutions is discoursed, and it is shown that the proposed solutions will improve the efficiency of VoIP applications and the quality of streaming applications over wireless networks. Finally, conclusions are drawn and future works are discussed

A methodology for obtaining More Realistic Cross-Layer QoS Measurements in mobile networks: A VoIP over LTE Use Case

Los servicios de voz han sido durante mucho tiempo la primera fuente de ingresos para los operadores móviles. Incluso con el protagonismo creciente del tráfico de datos, los servicios de voz seguirán jugando un papel importante y no desaparecerán con la transición a redes basadas en el protocolo IP. Por otra parte, hace años que los principales actores en la industria móvil detectaron claramente que los usuarios no aceptarían una degradación en la calidad de los servicios de voz. Es por esto que resulta crítico garantizar la experiencia de usuario (QoE) en la transición a redes de nueva generación basadas en conmutación de paquetes. El trabajo realizado durante esta tesis ha buscado analizar el comportamiento y las dependencias de los diferentes servicios de Voz sobre IP (VoIP), así como identificar configuraciones óptimas, mejoras potenciales y metodologías que permitan asegurar niveles de calidad aceptables al mismo tiempo que se trate de minimizar los costes. La caracterización del rendimiento del tráfico de datos en redes móviles desde el punto de vista de los usuarios finales es un proceso costoso que implica la monitorización y análisis de un amplio rango de protocolos y parámetros con complejas dependencias. Para abordar desde la raíz este problema, se requiere realizar medidas que relacionen y correlen el comportamiento de las diferentes capas. La metodología de caracterización propuesta en esta tesis proporciona la posibilidad de recoger información clave para la resolución de problemas en las comunicaciones IP, relaciolándola con efectos asociados a la propagación radio, como cambios de celda o pérdida de enlaces, o con carga de la red y limitaciones de recursos en zonas geográficas específicas. Dicha metodología se sustenta en la utilización de herramientas nativas de monitorización y registro de información en smartphones, y la aplicación de cadenas de herramientas para la experimentación extensiva tanto en redes reales y como en entornos de prueba controlados. Con los resultados proporcionados por esta serie de herramientas, tanto operadores móviles y proveedores de servicio como desarrolladores móviles podrían ganar acceso a información sobre la experiencia real del usuario y sobre cómo mejorar la cobertura, optimizar los servicios y adaptar el funcionamiento de las aplicaciones y el uso de protocolos móviles basados en IP en este contexto. Las principales contribuciones de las herramientas y métodos introducidos en esta tesis son los siguientes: - Una herramienta de monitorización multicapa para smartphones Android, llamada TestelDroid, que permite la captura de indicadores clave de rendimiento desde el propio equipo de usuario. Asimismo proporciona la capacidad de generar tráfico de forma activa y de verificar el estado de alcanzabilidad del terminal, realizando pruebas de conectividad. - Una metodología de post-procesado para correlar la información presente en las diferentes capas de las medidas realizadas. De igual forma, se proporciona la opción a los usuarios de acceder directamente a la información sobre el tráfico IP y las medidas radio y de aplicar metodologías propias para la obtención de métricas. - Se ha realizado la aplicación de la metodología y de las herramientas usando como caso de uso el estudio y evaluación del rendimiento de las comunicaciones basadas en IP a bordo de trenes de alta velocidad. - Se ha contribuido a la creación de un entorno de prueba realista y altamente configurable para la realización de experimentos avanzados sobre LTE. - Se han detectado posibles sinergias en la utilización de instrumentación avanzada de I+D en el campo de las comunicaciones móviles, tanto para la enseñanza como para la investigación en un entorno universitario