Name
Abstract | ||
|---|---|---|
Spectrum sensing is a fundamental component in cognitive radio. A major challenge in this area is the requirement of a high sampling rate in the sensing of a wideband signal. In this paper a wideband spectrum sensing model is presented that utilizes a sub-Nyquist sampling scheme to bring substantial savings in terms of the sampling rate. The correlation matrix of a finite number of noisy samples is computed and used by a subspace estimator to detect the occupied and vacant channels of the spectrum. In contrast with common methods, the proposed method does not need the knowledge of signal properties that mitigates the uncertainty problem. We evaluate the performance of this method by computing the probability of detecting signal occupancy in terms of the number of samples and the SNR of randomly generated signals. The results show a reliable detection even in low SNR and small number of samples. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/DSP-SPE.2011.5739182 | 2011 IEEE DIGITAL SIGNAL PROCESSING WORKSHOP AND IEEE SIGNAL PROCESSING EDUCATION WORKSHOP (DSP/SPE) |
Keywords | DocType | Volume |
Wideband spectrum sensing, Sub-Nyquist sampling, Cognitive radio, Correlation matrix, Subspace methods | Journal | abs/1010.2 |
Citations | PageRank | References |
14 | 0.73 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Moslem Rashidi | 1 | 14 | 1.40 |
| Kasra Haghighi | 2 | 53 | 4.47 |
| Arash Owrang | 3 | 20 | 2.93 |
| Mats Viberg | 4 | 1043 | 126.67 |