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dc.contributor.authorAmuru, S.en_US
dc.contributor.authorTekin, C.en_US
dc.contributor.authorVan Der Schaar, M.en_US
dc.contributor.authorBuehrer, R.M.en_US
dc.date.accessioned2018-04-12T11:48:09Z
dc.date.available2018-04-12T11:48:09Z
dc.date.issued2016en_US
dc.identifier.issn1536-1276
dc.identifier.urihttp://hdl.handle.net/11693/37690
dc.description.abstractCan an intelligent jammer learn and adapt to unknown environments in an electronic warfare-type scenario? In this paper, we answer this question in the positive, by developing a cognitive jammer that adaptively and optimally disrupts the communication between a victim transmitter-receiver pair. We formalize the problem using a multiarmed bandit framework where the jammer can choose various physical layer parameters such as the signaling scheme, power level and the on-off/pulsing duration in an attempt to obtain power efficient jamming strategies. We first present online learning algorithms to maximize the jamming efficacy against static transmitter-receiver pairs and prove that these algorithms converge to the optimal (in terms of the error rate inflicted at the victim and the energy used) jamming strategy. Even more importantly, we prove that the rate of convergence to the optimal jamming strategy is sublinear, i.e., the learning is fast in comparison to existing reinforcement learning algorithms, which is particularly important in dynamically changing wireless environments. Also, we characterize the performance of the proposed bandit-based learning algorithm against multiple static and adaptive transmitter-receiver pairs.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Wireless Communicationsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/TWC.2015.2510643en_US
dc.subjectConvergenceen_US
dc.subjectJammingen_US
dc.subjectLearningen_US
dc.subjectMultiarmed banditsen_US
dc.subjectOptimalen_US
dc.subjectRegreten_US
dc.subjectAlgorithmsen_US
dc.subjectElectronic warfareen_US
dc.subjectNetwork layersen_US
dc.subjectReinforcement learningen_US
dc.subjectTransmittersen_US
dc.subjectMulti armed banditen_US
dc.subjectLearning algorithmsen_US
dc.titleJamming bandits-a novel learning method for optimal jammingen_US
dc.typeArticleen_US
dc.departmentDepartment of Electrical and Electronics Engineering
dc.citation.spage2792en_US
dc.citation.epage2808en_US
dc.citation.volumeNumber15en_US
dc.citation.issueNumber4en_US
dc.identifier.doi10.1109/TWC.2015.2510643en_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US


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