Name
Abstract | ||
|---|---|---|
In this paper we develop the complex-valued version of the Gaussian processes for regression (GPR) for proper complex signals. This tool has proved to be useful in the nonlinear detection in digital communications in real valued models. GPRs can be cast as nonlinear MMSE where hyperparameters can be tuned optimizing a marginal likelihood (ML). This feature allows for a flexible kernel that can easily adapt either to a linear or nonlinear solution. We introduce the complex-valued form of the GPR, and develop it for the proper complex case. We also deal with the optimization of the ML. Some experiments included illustrate the good performance of the proposal. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/SAM.2014.6882359 | Sensor Array and Multichannel Signal Processing Workshop |
Keywords | Field | DocType |
Gaussian processes,digital communication,least mean squares methods,optimisation,regression analysis,GPR,Gaussian processes for regression,Gaussian processes regressors,complex proper signals,digital communications,marginal likelihood,nonlinear MMSE,nonlinear detection,nonlinear solution,solution | Signal processing,Nonlinear system,Ground-penetrating radar,Computer science,Computer network,Gaussian process,Artificial intelligence,Detector,Kernel (linear algebra),Hyperparameter,Marginal likelihood,Algorithm,Machine learning | Conference |
ISSN | Citations | PageRank |
1551-2282 | 4 | 0.48 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafael Boloix-Tortosa | 1 | 42 | 7.20 |
| F. Javier Payan-Somet | 2 | 9 | 1.94 |
| Juan José Murillo-Fuentes | 3 | 182 | 23.93 |
| Boloix-Tortosa, R. | 4 | 4 | 0.48 |
| Payan-Somet, F.J. | 5 | 4 | 0.48 |
| Murillo-Fuentes, J.J. | 6 | 37 | 6.55 |