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dc.contributor.authorAlqerm, Ismail
dc.contributor.authorShihada, Basem
dc.date.accessioned2017-10-30T08:39:49Z
dc.date.available2017-10-30T08:39:49Z
dc.date.issued2017-07-20
dc.identifier.citationAlQerm I, Shihada B (2017) Hybrid cognitive engine for radio systems adaptation. 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC). Available: http://dx.doi.org/10.1109/CCNC.2017.7983233.
dc.identifier.doi10.1109/CCNC.2017.7983233
dc.identifier.urihttp://hdl.handle.net/10754/625990
dc.description.abstractNetwork efficiency and proper utilization of its resources are essential requirements to operate wireless networks in an optimal fashion. Cognitive radio aims to fulfill these requirements by exploiting artificial intelligence techniques to create an entity called cognitive engine. Cognitive engine exploits awareness about the surrounding radio environment to optimize the use of radio resources and adapt relevant transmission parameters. In this paper, we propose a hybrid cognitive engine that employs Case Based Reasoning (CBR) and Decision Trees (DTs) to perform radio adaptation in multi-carriers wireless networks. The engine complexity is reduced by employing DTs to improve the indexing methodology used in CBR cases retrieval. The performance of our hybrid engine is validated using software defined radios implementation and simulation in multi-carrier environment. The system throughput, signal to noise and interference ratio, and packet error rate are obtained and compared with other schemes in different scenarios.
dc.publisherIEEE
dc.relation.urlhttp://ieeexplore.ieee.org/document/7983233/
dc.rights(c) 2017 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.subjectCase-based reasoning Software-defined radio (SDR)
dc.subjectCognitive engine
dc.subjectDecision-trees
dc.titleHybrid cognitive engine for radio systems adaptation
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputer Science Program
dc.identifier.journal2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)
dc.conference.date2017-01-08 to 2017-01-11
dc.conference.name14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017
dc.conference.locationLas Vegas, NV, USA
dc.eprint.versionPost-print
kaust.personAlqerm, Ismail
kaust.personShihada, Basem
refterms.dateFOA2018-06-14T03:52:29Z


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