Name
Abstract | ||
|---|---|---|
Spectrum sensing is the major challenge in the cognitive radio (CR). We
propose to learn local feature and use it as the prior knowledge to improve the
detection performance. We define the local feature as the leading eigenvector
derived from the received signal samples. A feature learning algorithm (FLA) is
proposed to learn the feature blindly. Then, with local feature as the prior
knowledge, we propose the feature template matching algorithm (FTM) for
spectrum sensing. We use the discrete Karhunen--Lo{\`e}ve transform (DKLT) to
show that such a feature is robust against noise and has maximum effective
signal-to-noise ratio (SNR). Captured real-world data shows that the learned
feature is very stable over time. It is almost unchanged in 25 seconds. Then,
we test the detection performance of the FTM in very low SNR. Simulation
results show that the FTM is about 2 dB better than the blind algorithms, and
the FTM does not have the noise uncertainty problem. |
Year | Venue | Keywords |
|---|---|---|
2011 | Clinical Orthopaedics and Related Research | cognitive radio,eigenvectors,signal to noise ratio,information theory,template matching |
Field | DocType | Volume |
Template matching,Pattern recognition,Feature (computer vision),Computer science,Speech recognition,Artificial intelligence,Eigenvalues and eigenvectors,Feature learning,Cognitive radio | Journal | abs/1102.2 |
Citations | PageRank | References |
2 | 0.41 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Zhang | 1 | 61 | 6.79 |
| Robert Caiming Qiu | 2 | 857 | 88.17 |