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
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.
