Online Bayesian learning for rate selection in millimeter wave cognitive radio networks

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buir.contributor.authorQureshi, Muhammad Anjum
buir.contributor.authorTekin, Cem
dc.citation.epage1458en_US
dc.citation.spage1449en_US
dc.citation.volumeNumber2020-Julyen_US
dc.contributor.authorQureshi, Muhammad Anjum
dc.contributor.authorTekin, Cem
dc.coverage.spatialToronto, Ontario, Canadaen_US
dc.date.accessioned2021-03-04T05:58:23Z
dc.date.available2021-03-04T05:58:23Z
dc.date.issued2020
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.descriptionDate of Conference: 6-9 July 2020en_US
dc.descriptionConference Name: 38th IEEE Conference on Computer Communications, INFOCOM 2020en_US
dc.description.abstractWe consider the problem of dynamic rate selection in a cognitive radio network (CRN) over the millimeter wave (mmWave) spectrum. Specifically, we focus on the scenario when the transmit power is time varying as motivated by the following applications: i) an energy harvesting CRN, in which the system solely relies on the harvested energy source, and ii) an underlay CRN, in which a secondary user (SU) restricts its transmission power based on a dynamically changing interference temperature limit (ITL) such that the primary user (PU) remains unharmed. Since the channel quality fluctuates very rapidly in mmWave networks and costly channel state information (CSI) is not that useful, we consider rate adaptation over an mmWave channel as an online stochastic optimization problem, and propose a Thompson Sampling (TS) based Bayesian method. Our method utilizes the unimodality and monotonicity of the throughput with respect to rates and transmit powers and achieves logarithmic in time regret with a leading term that is independent of the number of available rates. Our regret bound holds for any sequence of transmits powers and captures the dependence of the regret on the arrival pattern. We also show via simulations that the performance of the proposed algorithm is superior than the stateof-the-art algorithms, especially when the arrivals are favorable.en_US
dc.description.provenanceSubmitted by Zeynep Aykut (zeynepay@bilkent.edu.tr) on 2021-03-04T05:58:23Z No. of bitstreams: 1 Online_bayesian_learning_for_rate_selection_in_millimeter_wave_cognitive_radio_networks.pdf: 476490 bytes, checksum: 4f864caa9b38e6cb66b6e32f8f272a05 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-03-04T05:58:23Z (GMT). No. of bitstreams: 1 Online_bayesian_learning_for_rate_selection_in_millimeter_wave_cognitive_radio_networks.pdf: 476490 bytes, checksum: 4f864caa9b38e6cb66b6e32f8f272a05 (MD5) Previous issue date: 2020en
dc.identifier.doi10.1109/INFOCOM41043.2020.9155530en_US
dc.identifier.eisbn9781728164120
dc.identifier.eissn2641-9874
dc.identifier.isbn9781728164137
dc.identifier.issn0743-166X
dc.identifier.urihttp://hdl.handle.net/11693/75749
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/INFOCOM41043.2020.9155530en_US
dc.source.titleProceedings of the 38th IEEE Conference on Computer Communications, INFOCOM 2020en_US
dc.subjectCognitive radio networksen_US
dc.subjectmmWaveen_US
dc.subjectDynamic rate selectionen_US
dc.subjectThompson samplingen_US
dc.subjectContextual unimodal banditsen_US
dc.titleOnline Bayesian learning for rate selection in millimeter wave cognitive radio networksen_US
dc.typeConference Paperen_US

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