Show simple item record

dc.contributor.advisorTekin, Cem
dc.contributor.authorJavanmardi, Alireza
dc.date.accessioned2020-12-23T07:55:26Z
dc.date.available2020-12-23T07:55:26Z
dc.date.copyright2020-12
dc.date.issued2020-12
dc.date.submitted2020-12-22
dc.identifier.urihttp://hdl.handle.net/11693/54853
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2020.en_US
dc.descriptionIncludes bibliographical references (leaves 54-60).en_US
dc.description.abstractWe consider the problem of the distributed sequential channel and rate selection in cognitive radio networks where multiple users choose channels from the same set of available wireless channels and pick modulation and coding schemes (corresponds to transmission rates). In order to maximize the network throughput, users need to be cooperative while communication among them is not allowed. Also, if multiple users select the same channel simultaneously, they collide, and none of them would be able to use the channel for transmission. We rigorously formulate this resource allocation problem as a multi-player multi-armed bandit problem and propose a decentralized learning algorithm called Game of Thrones with Sequential Halving Orthogonal Exploration (GoT-SHOE). The proposed algorithm keeps the number of collisions in the network as low as possible and performs almost optimal exploration of the transmission rates to speed up the learning process. We prove our learning algorithm achieves a regret with respect to the optimal allocation that grows logarithmically over rounds with a leading term that is logarithmic in the number of transmission rates. We also propose an extension of our algorithm which works when the number of users is greater than the number of channels. Moreover, we discuss that Sequential Halving Orthogonal Exploration can indeed be used with any distributed channel assignment algorithm and enhance its performance. Finally, we provide extensive simulations and compare the performance of our learning algorithm with the state-of-the-art which demonstrates the superiority of the proposed algorithm in terms of better system throughput and lower number of collisions.en_US
dc.description.statementofresponsibilityby Alireza Javanmardien_US
dc.format.extentxii, 60 leaves : charts (some color) ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCognitive radioen_US
dc.subjectMulti-armed banditsen_US
dc.subjectDecentralized algorithmsen_US
dc.subjectRegret boundsen_US
dc.titleFully distributed bandit algorithm for the joint channel and rate selection problem in heterogeneous cognitive radio networksen_US
dc.title.alternativeHeterojen bilişsel radyo ağlarında müşterek kanal ve oran seçimi problemi için tümüyle merkezi olmayan haydut algoritmasıen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB125813
dc.embargo.release2021-06-30


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record