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Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
University of Luxembourg, Luxembourg. (Signal Processing)ORCID iD: 0000-0003-2298-6774
2018 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 12, no 1, p. 6-19Article in journal (Refereed) Published
Abstract [en]

In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) accessing the frequency band of a primary user (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU, and subsequently, eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the secondary users that aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the active learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate, and fast Bayesian learning method, the expectation propagation. The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. Vol. 12, no 1, p. 6-19
Keywords [en]
Bayes methods;Cognitive radio;Interference channels;Interference constraints;Receivers;Sensors;Cognitive radio networks (CRNs);active learning (AL);constrained dynamic programming;expectation propagation (EP)
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-258977DOI: 10.1109/JSTSP.2017.2785826ISI: 000426010500002Scopus ID: 2-s2.0-85039786364OAI: oai:DiVA.org:kth-258977DiVA, id: diva2:1350500
Note

QC 20190912

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-12Bibliographically approved

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CiteExportLink to record
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