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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00223318%21RIV15-GA0-21230___
rdf:type
n9:Vysledek skos:Concept
dcterms:description
To avoid call drops after handover due to unavailability of radio resources at a target handover cell, call admission control procedure reserves a specific amount of resources for users performing handover to this cell. If a high amount of resources is reserved, the available capacity for users served by the cell is lowered. Contrary, if a low amount of resources is booked for users entering the new cell, handover cannot be performed and user's connection is dropped. To optimize the amount of reserved resources, we propose an algorithm for prediction of channel quality between the user and the target cell after completing handover to the target cell. The algorithm is based on the knowledge of handover hysteresis and on decomposition of overall interference caused by other cells in the network. The prediction accuracy is tuned by correction parameter, which is dynamically set based on Q-learning approach. As the results show, the proposed algorithm with learning improves the efficiency of channel quality prediction up to twice comparing to conventional solution. To avoid call drops after handover due to unavailability of radio resources at a target handover cell, call admission control procedure reserves a specific amount of resources for users performing handover to this cell. If a high amount of resources is reserved, the available capacity for users served by the cell is lowered. Contrary, if a low amount of resources is booked for users entering the new cell, handover cannot be performed and user's connection is dropped. To optimize the amount of reserved resources, we propose an algorithm for prediction of channel quality between the user and the target cell after completing handover to the target cell. The algorithm is based on the knowledge of handover hysteresis and on decomposition of overall interference caused by other cells in the network. The prediction accuracy is tuned by correction parameter, which is dynamically set based on Q-learning approach. As the results show, the proposed algorithm with learning improves the efficiency of channel quality prediction up to twice comparing to conventional solution.
dcterms:title
Q-Learning-based Prediction of Channel Quality after Handover in Mobile Networks Q-Learning-based Prediction of Channel Quality after Handover in Mobile Networks
skos:prefLabel
Q-Learning-based Prediction of Channel Quality after Handover in Mobile Networks Q-Learning-based Prediction of Channel Quality after Handover in Mobile Networks
skos:notation
RIV/68407700:21230/14:00223318!RIV15-GA0-21230___
n3:aktivita
n11:P
n3:aktivity
P(GPP102/12/P613)
n3:dodaniDat
n6:2015
n3:domaciTvurceVysledku
n17:4588568 n17:2734796
n3:druhVysledku
n5:D
n3:duvernostUdaju
n14:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
40982
n3:idVysledku
RIV/68407700:21230/14:00223318
n3:jazykVysledku
n20:eng
n3:klicovaSlova
handover; prediction; channel quality; small cells
n3:klicoveSlovo
n4:prediction n4:channel%20quality n4:small%20cells n4:handover
n3:kontrolniKodProRIV
[D23880EAA6C4]
n3:mistoKonaniAkce
Washington D.C.
n3:mistoVydani
Piscataway
n3:nazevZdroje
IEEE 25th Annual International Symposium on Personal, Indoor and Mobile Radio Communications
n3:obor
n8:JA
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
3
n3:projekt
n7:GPP102%2F12%2FP613
n3:rokUplatneniVysledku
n6:2014
n3:tvurceVysledku
Mach, Pavel Calvanese Strinati, E. Bečvář, Zdeněk
n3:typAkce
n18:WRD
n3:zahajeniAkce
2014-09-02+02:00
s:issn
2166-9589
s:numberOfPages
6
n19:hasPublisher
IEEE Conference Publications
n21:isbn
978-1-4799-4912-0
n12:organizacniJednotka
21230