TY - GEN
T1 - Proactive call drop avoidance in UMTS networks
AU - Zhou, Shan
AU - Yang, Jie
AU - Xu, Dahai
AU - Li, Guangzhi
AU - Jin, Yu
AU - Ge, Zihui
AU - Kosseifi, Mario B.
AU - Doverspike, Robert
AU - Chen, Yingying
AU - Ying, Lei
PY - 2013
Y1 - 2013
N2 - The rapid advancement of smartphones has instigated tremendous data applications for cell phones. Supporting simultaneous voice and data services in a cellular network is not only desirable but also becoming indispensable. However, if the voice and data are serviced through the same antenna (like the 3G UMTS network), a voice call with data sessions requires better radio connection than a voice-only call. In this paper, we systematically study the coordination between the voice and data transmissions in UMTS networks. From analyzing a large carrier's UMTS network recording data, we first identify the most relevant network measurements/features indicating a potential call drop, then propose a drop-call predictor based on AdaBoost. Moreover, we develop an intelligent call management strategy to voluntarily block data sessions when the voice is predicted to be dropped. Our analysis utilizing real service provider's data sets shows that our proposed scheme can not only predict drop calls with a very high accuracy but also achieve the highest user satisfaction compared to the other existing call management strategies.
AB - The rapid advancement of smartphones has instigated tremendous data applications for cell phones. Supporting simultaneous voice and data services in a cellular network is not only desirable but also becoming indispensable. However, if the voice and data are serviced through the same antenna (like the 3G UMTS network), a voice call with data sessions requires better radio connection than a voice-only call. In this paper, we systematically study the coordination between the voice and data transmissions in UMTS networks. From analyzing a large carrier's UMTS network recording data, we first identify the most relevant network measurements/features indicating a potential call drop, then propose a drop-call predictor based on AdaBoost. Moreover, we develop an intelligent call management strategy to voluntarily block data sessions when the voice is predicted to be dropped. Our analysis utilizing real service provider's data sets shows that our proposed scheme can not only predict drop calls with a very high accuracy but also achieve the highest user satisfaction compared to the other existing call management strategies.
UR - http://www.scopus.com/inward/record.url?scp=84883089819&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883089819&partnerID=8YFLogxK
U2 - 10.1109/INFCOM.2013.6566808
DO - 10.1109/INFCOM.2013.6566808
M3 - Conference contribution
AN - SCOPUS:84883089819
SN - 9781467359467
T3 - Proceedings - IEEE INFOCOM
SP - 425
EP - 429
BT - 2013 Proceedings IEEE INFOCOM 2013
T2 - 32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013
Y2 - 14 April 2013 through 19 April 2013
ER -