72 research outputs found

    Mobility management in 5G for high-speed trains

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    High-speed trains (HST) are nowadays more present in our lives currently, some of them can reach speeds up to 500 km/h and futuristic concepts such as hyperloop tunnels could make trains travel at speeds up to 1000 km/h. Dealing with such high speeds arises many communication problems, for example, in mobility management, with many handovers or high Doppler frequency shifts. You might be thinking how it is possible to provide a good QoS to the users inside the train, when traveling at such elevated velocities. In the thesis, we rely on the development of 5G New Radio and the benefits associated, such as a new handover protocol introduced by 3GPP called conditional handover (CHO). By simulating with Simu5G a HST scenario we have proved that CHO can provide a better service to the users by improving the SINR levels and being more efficient than common handover.Los trenes de alta velocidad están cada vez más presentes en nuestro día a día, algunos ya alcanzan velocidades de 500 km/h, mientras que otros conceptos futuristas como los túneles hyperloop podrían hacer que alcanzaran velocidades de hasta 1000 km/h. En el ámbito de las telecomunicaciones, trabajar a tan altas velocidades conlleva algunos problemas, como por ejemplo un elevado número de handovers. Seguramente, os estéis preguntando cómo es posible establecer un servicio que cumpla unos mínimos de calidad para el usuario, cuando este viaja a tan altas velocidades. Para ello, nos hemos apoyado en la tecnología 5G i un nuevo concepto de handover llamado conditional handover (CHO), introducido por el 3GPP. A través del simulador Simu5G, hemos conseguido demostrar que el CHO no solo es un protocolo más eficiente, sino que además conlleva una mejora en los niveles de SINR, en condiciones parecidas a las de un tren de alta velocidad.Els trens d'alta velocitat estan cada vegada més presents en el nostre dia a dia, alguns ja son capaços d'arribar a velocitats pròximes als 500 km/h, mentre que altres conceptes futuristes com els túnels hyperloop podrien fer que els trens arribessin a velocitats de 1000 km/h. En l'àmbit de les comunicacions, treballar amb velocitats tan elevades comporta alguns problemes, com per exemple un ampli número de handovers. Segurament, estareu pensant com es possible establir un servei que compleixi uns mínims de qualitat de cara a l'usuari, al estar treballant amb velocitats tant elevades. Per fer-ho ens hem recolzat en la tecnologia 5G i un nou concepte de handover presentat pel 3GPP, el conditional handover (CHO). Simulant a través de Simu5G un escenari similar al d'un tren d'alta velocitat, hem pogut demostrar que el CHO no es només un protocol més eficient que el handover normal, sinó que a més a més millora els nivells de SINR

    A survey of machine learning techniques applied to self organizing cellular networks

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    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
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