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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Dall’Anese, Emiliano | es |
dc.contributor.author | Bazerque, Juan Andrés | es |
dc.contributor.author | Giannakis, Georgios B | es |
dc.date.accessioned | 2023-11-14T17:04:30Z | - |
dc.date.available | 2023-11-14T17:04:30Z | - |
dc.date.issued | 2012 | es |
dc.date.submitted | 20231114 | es |
dc.identifier.citation | Dall’Anese, E, Bazerque, J, Giannakis, G. "Group sparse Lasso for cognitive network sensing robust to model uncertainties and outliers" Physical Communication, 2012, v. 5, n. 2, pp. 161-172. https://doi.org/10.1016/j.phycom.2011.07.005 | es |
dc.identifier.issn | 1874-4907 | es |
dc.identifier.uri | https://hdl.handle.net/20.500.12008/41145 | - |
dc.description | Postprint | es |
dc.description.abstract | To account for variations in the frequency, time, and space dimensions, dynamic re-use of licensed bands under the cognitive radio (CR) paradigm calls for innovative network-level sensing algorithms for multi-dimensional spectrum opportunity awareness. Toward this direction, the present paper develops a collaborative scheme whereby CRs cooperate to localize active primary user (PU) transmitters and reconstruct a power spectral density (PSD) map portraying the spatial distribution of power across the monitored area per frequency band and channel coherence interval. The sensing scheme is based on a parsimonious model that accounts for two forms of sparsity: one due to the narrow-band nature of transmit-PSDs compared to the large portion of spectrum that a CR can sense, and another one emerging when adopting a spatial grid of candidate PU locations. Capitalizing on this dual sparsity, an estimator of the model coefficients is obtained based on the group sparse least-absolute-shrinkage-and-selection operator (GS-Lasso). A novel reduced-complexity GS-Lasso solver is developed by resorting to the alternating direction method of multipliers (ADMoM). Robust versions of this GS-Lasso estimator are also introduced using a GS total least-squares (TLS) approach to cope with both uncertainty in the regression matrices, arising due to inaccurate channel estimation and grid-mismatch effects, and unexpected model outliers. In spite of the non-convexity of the GS-TLS criterion, the novel robust algorithm has guaranteed convergence to (at least) a local optimum. The analytical findings are corroborated by numerical tests | es |
dc.language | en | es |
dc.publisher | Udelar.FI | es |
dc.relation.ispartof | Physical Communication, 2012, v. 5, n. 2 | es |
dc.rights | Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014) | es |
dc.subject | Spectrum sensing | es |
dc.subject | Spectrum cartography | es |
dc.subject | Sparse linear regression | es |
dc.subject | Total least-squares | es |
dc.subject | Outliers | es |
dc.subject.other | Sistemas y Control | es |
dc.title | Group sparse Lasso for cognitive network sensing robust to model uncertainties and outliers | es |
dc.type | Artículo | es |
dc.rights.licence | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) | es |
dc.identifier.doi | https://doi.org/10.1016/j.phycom.2011.07.005 | es |
Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
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