Online cross-layer learning in heterogeneous cognitive radio networks without CSI

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dc.contributor.authorQureshi, Muhammad Anjumen_US
dc.contributor.authorTekin, Cemen_US
dc.coverage.spatialIzmir, Turkeyen_US
dc.date.accessioned2019-02-21T16:05:14Z
dc.date.available2019-02-21T16:05:14Z
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 2-5 May 2018en_US
dc.description.abstractWe propose a contextual multi-armed bandit (CMAB) model for cross-layer learning in heterogeneous cognitive radio networks (CRNs). We consider the scenario where application adaptive modulation (AAM) is implemented in the physical (PHY) layer for heterogeneous applications in the application (APP) layer, each having dynamic packet error rate (PER) requirement. We consider the bit error rate (BER) constraint as the context to mode selector determined by the PHY layer based on the PER requirement, and propose a learning algorithm that learns the modulation with the highest expected reward online over an unknown dynamic wireless channel without channel state information (CSI), where the reward is taken as the Quality of Service (QoS) provided by the PHY layer to upper layers. We show numerically that the proposed algorithm's expected cumulative loss with respect to an oracle which knows the channel distribution perfectly grows sublinearly in time, and hence, the average loss asymptotically approaches to zero, which in turn yields optimal performance.
dc.description.provenanceMade available in DSpace on 2019-02-21T16:05:14Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under 3501 Program Grant No. 116E229.
dc.identifier.doi10.1109/SIU.2018.8404793
dc.identifier.isbn9781538615010
dc.identifier.urihttp://hdl.handle.net/11693/50240
dc.language.isoEnglish
dc.publisherIEEE
dc.relation.isversionofhttps://doi.org/10.1109/SIU.2018.8404793
dc.relation.project116E229 - Türkiye Bilimsel ve Teknolojik Araştirma Kurumu, TÜBITAK
dc.source.title2018 26th Signal Processing and Communications Applications Conference (SIU)en_US
dc.subjectAAMen_US
dc.subjectBERen_US
dc.subjectFeedbacken_US
dc.subjectMode selectoren_US
dc.subjectNo CSIen_US
dc.subjectPHY layeren_US
dc.subjectRegreten_US
dc.subjectSNRen_US
dc.titleOnline cross-layer learning in heterogeneous cognitive radio networks without CSIen_US
dc.typeConference Paperen_US

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