Learning Based Proactive Handovers in Heterogeneous Networks

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


    Today, the number of versatile real-time mobile applications is vast, each requiring different data rate, Quality of Service (QoS) and connection availability requirements. There have been strong demands for pervasive communication with advances in wireless technologies. Real-time applications experience significant performance bottlenecks in heterogeneous networks. A critical time for a real-time application is when a vertical handover is done between different radio access technologies. It requires a lot of signalling causing unwanted interruptions to real-time applications. This work presents a utilization of learning algorithms to give time for applications to prepare itself for vertical handovers in the heterogeneous network environment. A testbed has been implemented, which collects PHY (Physical layer), application level QoS and users context information from a terminal and combines these Key Performance Indicators (KPI) with network planning information in order to anticipate vertical handovers by taking into account the preparation time required by a specific real-time application
    Original languageEnglish
    Title of host publicationMobile Networks and Management
    Subtitle of host publicationMONAMI 2013
    ISBN (Print)978-3-319-04277-0, 978-3-319-04276-3
    Publication statusPublished - 2013
    MoE publication typeA4 Article in a conference publication
    Event5th International Conference, MONAMI 2013 - Cork, Ireland
    Duration: 23 Sept 201325 Sept 2013

    Publication series

    SeriesLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering


    Conference5th International Conference, MONAMI 2013
    Abbreviated titleMONAMI 2013


    • Vertical Handover
    • Heterogeneous Network
    • Key Performance Indicator
    • Machine Learning
    • Quality of Experience


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