Name
Abstract | ||
|---|---|---|
In this paper, we propose a feature-based method for spectrum sensing of OFDM signals from sub-Nyquist samples over a single band. We exploit the structure of the covariance matrix of OFDM signals to convert an underdetermined set of covariance-based equations to an overdetermined one. The statistical properties of sample covariance matrix are analyzed and then based on that an approximate Generalized Likelihood Ratio Test (GLRT) for detection of OFDM signals from sub-Nyquist samples is derived. The method is also extended to the frequency-selective channels. HighlightsA method for covariance-based spectrum sensing of OFDM signals from sub-Nyquist samples is introduced.The structure of the covariance matrix of OFDM signals is exploited for sub-Nyquist detection.The covariances between elements of sample covariance matrix are used to formulate an approximate Generalized Likelihood Ratio Test (GLRT).The method is extended to the frequency-selective channel case. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.sigpro.2014.11.017 | Signal Processing |
Keywords | DocType | Volume |
compressive sensing,ofdm,cognitive radio | Journal | abs/1501.02405 |
Issue | ISSN | Citations |
C | Signal Processing, Volume 109, April 2015, Pages 261-268 | 2 |
PageRank | References | Authors |
0.36 | 25 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seyed Alireza Razavi | 1 | 42 | 7.77 |
| Mikko Valkama | 2 | 1567 | 175.51 |
| Danijela Cabric | 3 | 795 | 101.37 |