Name
Abstract | ||
|---|---|---|
This work proposes a spectrum cartography algorithm used for learning the power spectrum distribution over a wide frequency band across a given geographic area. Motivated by low-complexity sensing hardware and stringent communication constraints, compressed and quantized measurements are considered. Setting out from a nonparametric regression framework, it is shown that a sensible approach leads to a support vector machine formulation. The simulated tests verify that accurate spectrum maps can be constructed using a simple sensing architecture with significant savings in the feedback. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP) | Kernel (linear algebra),Wideband,Computer science,Frequency band,Support vector machine,Nonparametric regression,Spectral density,Observational error,Cartography,Cognitive radio |
DocType | ISSN | Citations |
Conference | 1520-6149 | 3 |
PageRank | References | Authors |
0.40 | 19 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Romero | 1 | 73 | 6.77 |
| Seung-Jun Kim | 2 | 1003 | 62.52 |
| Roberto López-Valcarce | 3 | 300 | 41.00 |
| Georgios B. Giannakis | 4 | 3 | 0.40 |