Cognitive Management of Self -Organized Radio Networks Based on Multi Armed Bandit

Abstract : Many tasks in current mobile networks are automated through Self-Organizing Networks (SON) functions. The actual implementation consists in a network with several SON functions deployed and operating independently. A Policy Based SON Manager (PBSM) has been introduced to configure these functions in a manner that makes the overall network fulfill the operator objectives. Given the large number of possible configurations (for each SON function instance in the network), we propose to empower the PBSM with learning capability. This Cognitive PBSM (C-PBSM) learns the most appropriate mapping between SON configurations and operator objectives based on past experience and network feedback. The proposed learning algorithm is a stochastic multi-armed bandit, namely the UCB1. We evaluate the performances of the proposed C-PBSM on an LTE-A simulator. We show that it is able to learn the optimal SON configuration and quickly adapts to objective changes.
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Communication dans un congrès
PIMRC 2017 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications , 2017, Montreal, Canada. 2017, IEEE PIMRC 2017
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Contributeur : Laurent Decreusefond <>
Soumis le : samedi 12 août 2017 - 21:23:29
Dernière modification le : jeudi 24 août 2017 - 01:10:55

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  • HAL Id : hal-01574283, version 1

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Tony Daher, Sana Jemaa, Laurent Decreusefond. Cognitive Management of Self -Organized Radio Networks Based on Multi Armed Bandit. PIMRC 2017 28th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications , 2017, Montreal, Canada. 2017, IEEE PIMRC 2017. 〈hal-01574283〉

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