Sequential learning for optimal monitoring of multi-channel wireless networks

We consider the problem of optimally assigning p sniffers to K channels to monitor the transmission activities in a multi-channel wireless network. The activity of users is initially unknown to the sniffers and is to be learned along with channel assignment decisions while maximizing the benefits of...

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Bibliographic Details
Published in2011 Proceedings IEEE INFOCOM pp. 1152 - 1160
Main Authors Arora, P., Szepesvari, C., Rong Zheng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2011
Subjects
Joints
Monitoring
Performance evaluation
Resource management
Uncertainty
Wireless networks
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Summary:We consider the problem of optimally assigning p sniffers to K channels to monitor the transmission activities in a multi-channel wireless network. The activity of users is initially unknown to the sniffers and is to be learned along with channel assignment decisions while maximizing the benefits of this assignment, resulting in the fundamental trade-off between exploration versus exploitation. We formulate it as the linear partial monitoring problem, a super-class of multi-armed bandits. As the number of arms (sniffer-channel assignments) is exponential, novel techniques are called for, to allow efficient learning. We use the linear bandit model to capture the dependency amongst the arms and develop two policies that take advantage of this dependency. Both policies enjoy logarithmic regret bound of time-slots with a term that is sub-linear in the number of arms.
ISBN:1424499194
9781424499199
ISSN:0743-166X
2641-9874
DOI:10.1109/INFCOM.2011.5934892