Artificial intelligent power prediction for efficient resource management of WCDMA mobile network

Y. K. Tee, S. K. Tinng, Johnny Siaw Paw Koh, Y. David

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a method of predicting changes of power consumption at Node B of a wideband code division multiple access (WCDMA) mobile network due to dynamic resource allocation such as movement of unit equipment (UE), handover call from adjacent cell and accommodation of new service request. The method learns the mapping of power consumption at Node B by monitoring power changes that response to previous performed resource allocation. Estimation of the unknown function is implemented with support vector regression (SVR). The output of SVR will be used by WCDMA mobile network to decide on new service admission. Genetic algorithm (GA) is then applied to form optimal beams to cover all UEs in a cell with minimum power. This artificial intelligent call admission control (CAC) was validated using a dynamic WCDMA mobile network simulator. A few comparative results in downlink have shown that our integrated support vector regression assists genetic algorithm (SVRaGA) is capable of predicting next interval power consumption at Node B with low prediction error and improving the quality of service (QoS) by reducing dropped calls.

Original languageEnglish
Title of host publication2008 14th Asia-Pacific Conference on Communications, APCC 2008
Publication statusPublished - 01 Dec 2008
Event2008 14th Asia-Pacific Conference on Communications, APCC 2008 - Akihabara, Tokyo, United States
Duration: 14 Oct 200816 Oct 2008

Publication series

Name2008 14th Asia-Pacific Conference on Communications, APCC 2008

Other

Other2008 14th Asia-Pacific Conference on Communications, APCC 2008
CountryUnited States
CityAkihabara, Tokyo
Period14/10/0816/10/08

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Communication

Cite this

Tee, Y. K., Tinng, S. K., Koh, J. S. P., & David, Y. (2008). Artificial intelligent power prediction for efficient resource management of WCDMA mobile network. In 2008 14th Asia-Pacific Conference on Communications, APCC 2008 [4773836] (2008 14th Asia-Pacific Conference on Communications, APCC 2008).