Hybrid Experiential-Heuristic Cognitive Radio Engine Architecture and Implementation

Simple item page
dc.contributor.authorAmanna, Ashwin E.en
dc.contributor.authorAli, Danielen
dc.contributor.authorFitch, David Gonzalezen
dc.contributor.authorReed, Jeffrey H.en
dc.contributor.departmentElectrical and Computer Engineeringen
dc.date.accessioned2017-09-18T09:49:51Zen
dc.date.available2017-09-18T09:49:51Zen
dc.date.issued2012-05-16en
dc.date.updated2017-09-18T09:49:51Zen
dc.description.abstractThe concept of cognitive radio (CR) focuses on devices that can sense their environment, adapt configuration parameters, and learn from past behaviors. Architectures tend towards simplified decision-making algorithms inspired by human cognition. Initial works defined cognitive engines (CEs) founded on heuristics, such as genetic algorithms (GAs), and case-based reasoning (CBR) experiential learning algorithms. This hybrid architecture enables both long-term learning, faster decisions based on past experience, and capability to still adapt to new environments. This paper details an autonomous implementation of a hybrid CBR-GA CE architecture on a universal serial radio peripheral (USRP) software-defined radio focused on link adaptation. Details include overall process flow, case base structure/retrieval method, estimation approach within the GA, and hardware-software lessons learned. Unique solutions to realizing the concept include mechanisms for combining vector distance and past fitness into an aggregate quantification of similarity. Over-the-air performance under several interference conditions is measured using signal-to-noise ratio, packet error rate, spectral efficiency, and throughput as observable metrics. Results indicate that the CE is successfully able to autonomously change transmit power, modulation/coding, and packet size to maintain the link while a non-cognitive approach loses connectivity. Solutions to existing shortcomings are proposed for improving case-base searching and performance estimation methods.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAshwin Amanna, Daniel Ali, David Gonzalez Fitch, and Jeffrey H. Reed, “Hybrid Experiential-Heuristic Cognitive Radio Engine Architecture and Implementation,” Journal of Computer Networks and Communications, vol. 2012, Article ID 549106, 15 pages, 2012. doi:10.1155/2012/549106en
dc.identifier.doihttps://doi.org/10.1155/2012/549106en
dc.identifier.urihttp://hdl.handle.net/10919/79010en
dc.language.isoenen
dc.publisherHindawien
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.holderCopyright © 2012 Ashwin Amanna et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleHybrid Experiential-Heuristic Cognitive Radio Engine Architecture and Implementationen
dc.title.serialJournal of Computer Networks and Communicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

Files

Original bundle
Now showing 1 - 2 of 2
Name:
JCNC.2012.549106.xml
Size:
6.76 KB
Format:
Extensible Markup Language
Download
Thumbnail Image
Name:
JCNC.2012.549106.pdf
Size:
1.28 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Journal Articles, Hindawi Press
Scholarly Works, Electrical and Computer Engineering