3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications

Research Article

Blind Spectrum Sensing for Cognitive Radio Based on Model Selection

  • @INPROCEEDINGS{10.1109/CROWNCOM.2008.4562448,
        author={Bassem Zayen and Aawatif Hayar and Dominique Nussbaum},
        title={Blind Spectrum Sensing for Cognitive Radio Based on Model Selection},
        proceedings={3rd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications},
        publisher={IEEE},
        proceedings_a={CROWNCOM},
        year={2010},
        month={5},
        keywords={Cognitive radio Spectrum Sensing Model Selection Akaike weights.},
        doi={10.1109/CROWNCOM.2008.4562448}
    }
    
  • Bassem Zayen
    Aawatif Hayar
    Dominique Nussbaum
    Year: 2010
    Blind Spectrum Sensing for Cognitive Radio Based on Model Selection
    CROWNCOM
    IEEE
    DOI: 10.1109/CROWNCOM.2008.4562448
Bassem Zayen1,*, Aawatif Hayar1,*, Dominique Nussbaum1,*
  • 1: Mobile Communications Group, Institut Eurecom, 2229 Route des Cretes, P.O. Box 193, 06904 Sophia Antipolis, France
*Contact email: zayen@eurecom.fr, hayar@eurecom.fr, nussbaum@eurecom.fr

Abstract

Cognitive radio devices will be able to seek and dynamically use frequency bands for network access. This will be done by autonomous detection of vacant sub-bands in the radio spectrum. In this paper, we propose a new method for blind detection of vacant sub-bands over the spectrum band. The proposed method exploits model selection tools like Akaike information criterion (AIC) and Akaike weights to sense holes in the spectrum band. Specifically, we assume that the noise of the radio spectrum band can still be adequately modeled using Gaussian distribution. We then compute and analyze Akaike weights in order to decide if the distribution of the received signal fits the noise distribution or not. Our theoretical result are validated using experimental measurements captured by Eurecom RF Agile Platform. Simulations show promising performance results of the proposed technique in terms of sensing vacant sub-bands in the spectrum.