Cooperative spectrum sensing for cognitive radios using kriged kalman filtering

Seung Jun Kim; Emiliano Dall'Anese; Georgios B. Giannakis

doi:10.1109/JSTSP.2010.2053016

Cooperative spectrum sensing for cognitive radios using kriged kalman filtering

Seung Jun Kim, Emiliano Dall'Anese, Georgios B. Giannakis

Electrical and Computer Engineering

Research output: Contribution to journal › Article › peer-review

126 Scopus citations

Abstract

A cooperative cognitive radio (CR) sensing problem is considered, where a number of CRs collaboratively detect the presence of primary users (PUs) by exploiting the novel notion of channel gain (CG) maps. The CG maps capture the propagation medium per frequency from any point in space and time to each CR user. They are updated in real-time using Kriged Kalman filtering (KKF), a tool with well-appreciated merits in geostatistics. In addition, the CG maps enable tracking the transmit-power and location of an unknown number of PUs, via a sparse regression technique. The latter exploits the sparsity inherent to the PU activities in a geographical area, using an ℓ₁-norm regularized, sparsity-promoting weighted least-squares formulation. The resulting sparsity-cognizant tracker is developed in both centralized and distributed formats, to reduce computational complexity and memory requirements of a batch alternative. Numerical tests demonstrate considerable performance gains achieved by the proposed algorithms.

Original language	English (US)
Article number	5484600
Pages (from-to)	24-36
Number of pages	13
Journal	IEEE Journal on Selected Topics in Signal Processing
Volume	5
Issue number	1
DOIs	https://doi.org/10.1109/JSTSP.2010.2053016
State	Published - Feb 2011

Bibliographical note

Funding Information:
Manuscript received October 12, 2009; revised March 29, 2010; accepted June 04, 2010. Date of publication June 14, 2010; date of current version January 19, 2011. This work was supported by the National Science Foundation under Grants CCF 0830480 and CON 0824007 and also in part through collaborative participation in the Communications and Networks Consortium sponsored by the U.S. Army Research Laboratory under the Collaborative Technology Alliance Program, Cooperative Agreement DAAD19-01-2-0011. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon. Part of this work appeared in the Proceedings of the 43rd Asilomar Conference on Signal, Systems, and Computers, Pacific Grove, CA, November 2009, and in the Proceedings of the 3rd International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Aruba, Dutch Antilles, December 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Andres Kwasinski.

Keywords

Channel estimation
Kalman filters
cognitive radio
compressed sampling
distributed algorithms

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10.1109/JSTSP.2010.2053016

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abstract = "A cooperative cognitive radio (CR) sensing problem is considered, where a number of CRs collaboratively detect the presence of primary users (PUs) by exploiting the novel notion of channel gain (CG) maps. The CG maps capture the propagation medium per frequency from any point in space and time to each CR user. They are updated in real-time using Kriged Kalman filtering (KKF), a tool with well-appreciated merits in geostatistics. In addition, the CG maps enable tracking the transmit-power and location of an unknown number of PUs, via a sparse regression technique. The latter exploits the sparsity inherent to the PU activities in a geographical area, using an ℓ1-norm regularized, sparsity-promoting weighted least-squares formulation. The resulting sparsity-cognizant tracker is developed in both centralized and distributed formats, to reduce computational complexity and memory requirements of a batch alternative. Numerical tests demonstrate considerable performance gains achieved by the proposed algorithms.",

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Cooperative spectrum sensing for cognitive radios using kriged kalman filtering

Abstract

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Keywords

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