Name
Abstract | ||
|---|---|---|
The detection of random signals in noisy measurements is a problem of interest in several scientific applications that has been studied extensively. Recently, sample eigenvalue-based procedures for spectrum sensing and signal detection have received a lot of attention due their computational simplicity, their robustness, and their performance, which is claimed to exceed the performance of the classical Neyman-Pearson detectors. In this paper, through a theoretical analysis and Monte Carlo simulations, we investigate the detection performance of the different eigenvalue-based detection strategies that have been proposed while utilizing the performances of the energy detector and the estimator-correlator as benchmarks. Our results indicate that eigenvalue-based methods are not better than the classical energy detector and estimator-correlator. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICC.2014.6884014 | Communications |
Keywords | Field | DocType |
Monte Carlo methods,eigenvalues and eigenfunctions,signal detection,wireless sensor networks,Monte Carlo simulations,Neyman-Pearson detectors,eigenvalue-based detection,energy detector,estimator-correlator,performance evaluation,random signal detection,spectrum sensing,wireless sensor network,eigenvalue-based signal detection,energy detection,estimator correlator,hypothesis testing,wireless sensor networks | Computer science,Theoretical computer science,Real-time computing,Wireless sensor network,Computer engineering,Eigenvalues and eigenvectors | Conference |
ISSN | Citations | PageRank |
1550-3607 | 1 | 0.39 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric Ayeh | 1 | 1 | 0.39 |
| Kamesh Namuduri | 2 | 121 | 15.86 |
| Xinrong Li | 3 | 1 | 0.39 |