| Performance of Spiked Population Models for Spectrum Sensing |
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Tan-Thanh Le, Hyung-Yun Kong |
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Dept. of Electrical. Engineering, University of Ulsan |
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| Abstract |
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In order to improve sensing performance when the noise variance is not known, this paper considers a so-called blind spectrum sensing technique that is based on eigenvalue models. In this paper, we employed the spiked population models in order to identify the miss detection probability. At first, we try to estimate the unknown noise variance based on the blind measurements at a secondary location. We then investigate the performance of detection, in terms of both theoretical and empirical aspects, after applying this estimated noise variance result. In addition, we study the effects of the number of SUs and the number of samples on the spectrum sensing performance. |
| Key words:
Random Matrix Theory, Cognitive Radio Networks, Spiked Population Model, Spectrum Sensing Techniques, Eigenvalue-Based Spectrum Sensing, Noise Estimation |
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