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Wireless Standard Classification in Cognitive Radio Networks Using Self Organising Maps

Research output: Chapter in Book/Report/Conference proceedingConference paper

Shaswar Baban, Oliver Holland, Abdol-Hamid Aghvami, Daniel Denkovski, Liljana Gavrilovska

Original languageEnglish
Title of host publicationProceedings of the Tenth International Symposium on Wireless Communication Systems (ISWCS 2013)
Place of PublicationIlmenau, Germany
PublisherIEEE
Pages115-119
Number of pages5
ISBN (Print)9783800735297
Publication statusPublished - Aug 2013

King's Authors

Abstract

Radio access technology (RAT) recognition can be an important technique to facilitate spectrum sharing, interference avoidance, and cooperation among cognitive radios. As one example among its very many possible uses and benefits, RAT recognition might allow Secondary Users (SUs) to differentiate between the transmissions of Primary Users (PUs) and other SUs, such that SUs might contend for a spectrum band fairly, only not transmitting when they detect the PUs as having started to transmit in the same band. In this work, a practical testbed made up of software defined radio transceivers and computing units has been assembled, and used to transmit and receive extensive samples of representative RATs. A Self-Organizing Map (SOM) with Support Vector Machine (SVM) clustering and classification technique has been developed via a semisupervised learning, to operate on these received samples. Finally, performance metrics have been presented showing almost 100 % classification performance at -20dB Signal-to- Noise Ratio (SNR). This demonstrates the efficiency of this technique.

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