Deteriorative effects on feature-based signal detection due to imperfect training

Quang Thai*, Sam Reisenfeld

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

    Abstract

    Feature-based detection techniques have been advocated for robust spectrum sensing in cognitive radios. Cognitive radios must be able to train themselves to identify the features for a specific primary user at a given channel, time or location. However, 'in-the-field' training relies on signal observations where there is uncertainty about whether or not it is truly representative of the primary user. This work considers this uncertainty, how it effects the detector's training time and performance, and identifies a trade-off between these outcomes. A two-stage detector structure is also illustrated to fulfill both the training and operational requirements of such detectors.

    Original languageEnglish
    Title of host publication2012 IEEE Wireless Communications and Networking Conference, WCNC 2012
    Place of PublicationPiscataway, N.J
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages914-919
    Number of pages6
    ISBN (Print)9781467304375
    DOIs
    Publication statusPublished - 2012
    Event2012 IEEE Wireless Communications and Networking Conference, WCNC 2012 - Paris, France
    Duration: 1 Apr 20124 Apr 2012

    Other

    Other2012 IEEE Wireless Communications and Networking Conference, WCNC 2012
    CountryFrance
    CityParis
    Period1/04/124/04/12

    Keywords

    • cognitive radio
    • detection algorithms
    • detectors
    • feature extraction
    • learning systems
    • signal analysis
    • signal detection
    • supervised learning
    • training

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