Hybrid cognitive engine for radio systems adaptation

Handle URI:
http://hdl.handle.net/10754/625990
Title:
Hybrid cognitive engine for radio systems adaptation
Authors:
Alqerm, Ismail ( 0000-0002-5960-0663 ) ; Shihada, Basem ( 0000-0003-4434-4334 )
Abstract:
Network 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Citation:
AlQerm 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.
Publisher:
IEEE
Journal:
2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)
Conference/Event name:
14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017
Issue Date:
20-Jul-2017
DOI:
10.1109/CCNC.2017.7983233
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/document/7983233/
Appears in Collections:
Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorAlqerm, Ismailen
dc.contributor.authorShihada, Basemen
dc.date.accessioned2017-10-30T08:39:49Z-
dc.date.available2017-10-30T08:39:49Z-
dc.date.issued2017-07-20en
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.en
dc.identifier.doi10.1109/CCNC.2017.7983233en
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.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7983233/en
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.en
dc.subjectCase-based reasoning Software-defined radio (SDR)en
dc.subjectCognitive engineen
dc.subjectDecision-treesen
dc.titleHybrid cognitive engine for radio systems adaptationen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journal2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)en
dc.conference.date2017-01-08 to 2017-01-11en
dc.conference.name14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017en
dc.conference.locationLas Vegas, NV, USAen
dc.eprint.versionPost-printen
kaust.authorAlqerm, Ismailen
kaust.authorShihada, Basemen
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