Título:
Group sparse Lasso for cognitive network sensing robust to model uncertainties and outliers
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Autor:
Dall’Anese, Emiliano
Bazerque, Juan Andrés
Giannakis, Georgios B
Bazerque, Juan Andrés
Giannakis, Georgios B
Tutor:
Tipo de documento:
Artículo
Editor:
Palabras clave:
Spectrum sensing
Spectrum cartography
Sparse linear regression
Total least-squares
Outliers
Spectrum cartography
Sparse linear regression
Total least-squares
Outliers
Descriptores:
Sistemas y Control
Año de publicación:
2012
Contenido:
Resumen:
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
Descripción:
Postprint
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Editorial:
Udelar.FI
EN:
Physical Communication, 2012, v. 5, n. 2
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Citación:
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
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ISSN:
1874-4907
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Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| DBG12.pdf | — | 904.88 KB | Adobe PDF |