Application of reinforcement learning to admission control in CDMA network

Author

Makarevitch, Boris

Author_Institution

Commun. Lab., Helsinki Univ. of Technol., Espoo, Finland

Volume

2

fYear

2000

fDate

2000

Firstpage

1353

Abstract

The paper describes an admission control algorithm for the CDMA networks which is able to adapt to the operating environment. The algorithm is based on the principle of reinforcement learning and it achieves near-optimal performance for various radio propagation conditions and network operator´s objectives. The performance evaluation results for different state space alternatives and algorithm parameters are presented and compared with the conventional admission control based on the power thresholds

Keywords

Markov processes; cellular radio; code division multiple access; learning (artificial intelligence); multiuser channels; radio networks; telecommunication congestion control; CDMA network; Markov decision process; admission control algorithm; algorithm parameters; cellular radio; near-optimal performance; network operator objectives; performance evaluation results; power thresholds; radio propagation conditions; reinforcement learning; state space alternatives; Admission control; Base stations; Communication system control; Degradation; Downlink; Intelligent networks; Interference; Learning; Multiaccess communication; Paper technology;

fLanguage

English

Publisher

ieee

Conference_Titel

Personal, Indoor and Mobile Radio Communications, 2000. PIMRC 2000. The 11th IEEE International Symposium on

Conference_Location

London

Print_ISBN

0-7803-6463-5

Type

conf

DOI

10.1109/PIMRC.2000.881639

Filename

881639