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dc.contributor.authorAlqerm, Ismail
dc.contributor.authorShihada, Basem
dc.date.accessioned2018-05-28T06:13:11Z
dc.date.available2018-05-28T06:13:11Z
dc.date.issued2018-03-19
dc.identifier.citationAlQerm I, Shihada B (2018) Supervised cognitive system: A new vision for cognitive engine design in wireless networks. 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC). Available: http://dx.doi.org/10.1109/CCNC.2018.8319212.
dc.identifier.doi10.1109/CCNC.2018.8319212
dc.identifier.urihttp://hdl.handle.net/10754/627960
dc.description.abstractCognitive radio attracts researchers' attention recently in radio resource management due to its ability to exploit environment awareness in configuring radio system parameters. Cognitive engine (CE) is the structure known for deciding system parameters' adaptation using optimization and machine learning techniques. However, these techniques have strengths and weaknesses depending on the experienced network scenario that make one more appropriate than others. In this paper, we propose a novel design for the cognitive system called supervised cognitive system (SCS), which aims to perform radio parameters adaptation with the most appropriate CE learning technique for the encountered network scenario. To realize SCS, it is required to evaluate the performance of different CEs in different network scenarios and according to certain performance objectives. In addition, the ability to select the most appropriate CE learning technique for adaptation in the current network scenario is also a priority in our design. Therefore, SCS investigates the relationship between learning and performance improvement and it employs online learning to classify scenarios and select the most appropriate CE learning technique. The testbed implementation and evaluation results in terms of goodput, packet error rate, and spectral efficiency show that the proposed SCS achieves more than 50% in performance gain compared to the best standalone CE.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8319212
dc.rights(c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectCognitive engine
dc.subjectOnline Learning
dc.subjectSupervised cognitive system (SCS)
dc.subjectsystem parameters adaptation
dc.titleSupervised cognitive system: A new vision for cognitive engine design in wireless networks
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journal2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)
dc.conference.date2018-01-12 to 2018-01-15
dc.conference.name15th IEEE Annual Consumer Communications and Networking Conference, CCNC 2018
dc.conference.locationLas Vegas, NV, USA
dc.eprint.versionPost-print
kaust.personAlqerm, Ismail
kaust.personShihada, Basem
refterms.dateFOA2018-06-14T04:45:45Z
dc.date.published-online2018-03-19
dc.date.published-print2018-01


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