Blind spectrum sensing in cognitive radio is being addressed in this thesis. Particular emphasis is put on performance in the low signal to noise range. It is shown how methods relying on traditional sample based estimation methods, such as the energy detector and autocorrelation based detectors, suffer at low SNRs. This problem is attempted to be solved by investigating how higher order statistics and information theoretic distance measures can be applied to do spectrum sensing. Results from a thorough literature survey indicate that the information theoretic distance gls{kl} divergence is promising when trying to devise a novel cognitive radio spectrum sensing scheme. Two novel detection algorithms based on Kullback-Leibler divergence estimation are proposed. However, unfortunately only one of them has a fully proven theoretical foundation. The other has a partial theoretical framework, supported by empirical results. Detection performance of the two proposed detectors in comparison with two reference detectors is assessed. The two reference detectors are the energy detector, and an autocorrelation based detector. Through simulations, it is shown that the proposed KL divergence based algorithms perform worse than the energy detector for all the considered scenarios, while one of them performs better than the autocorrelation based detector for certain signals. The reason why the detectors perform worse than the energy detector, despite the good properties of the estimators at low signal to noise ratios, is that the KL divergence between signal and noise is small. The low divergence stems from the fact that both signal and noise have very similar probability density distributions. Detection performance is also assessed by applying the detectors to raw data of a downconverted UMTS signal. It is shown that the noise distribution deviates from the standard assumption (circularly symmetric complex white Gaussian). Due to this deviation, the autocorrelation based reference detector and the two proposed Kullback-Leibler divergence based detectors are challenged. These detectors rely heavily on the aforementioned assumption, and fail to function properly when applied to signals with deviating characteristics.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ntnu-10844 |
| Date | January 2010 |
| Creators | Christiansen, Jørgen Berle |
| Publisher | Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, Institutt for elektronikk og telekommunikasjon |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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