An online parallel algorithm for spectrum sensing in cognitive radio networks

Yang Yang; Mengyi Zhang; Marius Pesavento; Daniel P. Palomar

doi:10.1109/ACSSC.2014.7094778

An online parallel algorithm for spectrum sensing in cognitive radio networks

Yang Yang, Mengyi Zhang, Marius Pesavento, Daniel P. Palomar

Department of Electronic and Computer Engineering

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

4 Citations (Scopus)

Abstract

We consider the estimation of the position and transmit power of primary users in cognitive radio networks based on solving a sequence of ℓ₁-regularized least-square problems, in which the unknown vector is sparse and the measurements are only sequentially available. We propose an online parallel algorithm that is novel in three aspects: i) all elements of the unknown vector variable are updated in parallel; ii) the update of each element has a closed-form expression; and iii) the stepsize is designed to accelerate the convergence yet it still has a closed-form expression. The convergence property is both theoretically analyzed and numerically consolidated.

Original language	English
Title of host publication	Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	1801-1805
Number of pages	5
ISBN (Electronic)	9781479982974
DOIs	https://doi.org/10.1109/ACSSC.2014.7094778
Publication status	Published - 24 Apr 2015
Event	48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: 2 Nov 2014 → 5 Nov 2014

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2015-April
ISSN (Print)	1058-6393

Conference

Conference	48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	2/11/14 → 5/11/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Access to Document

10.1109/ACSSC.2014.7094778

Cite this

Yang, Y., Zhang, M., Pesavento, M., & Palomar, D. P. (2015). An online parallel algorithm for spectrum sensing in cognitive radio networks. In M. B. Matthews (Ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers (pp. 1801-1805). Article 7094778 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2014.7094778

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abstract = "We consider the estimation of the position and transmit power of primary users in cognitive radio networks based on solving a sequence of ℓ1-regularized least-square problems, in which the unknown vector is sparse and the measurements are only sequentially available. We propose an online parallel algorithm that is novel in three aspects: i) all elements of the unknown vector variable are updated in parallel; ii) the update of each element has a closed-form expression; and iii) the stepsize is designed to accelerate the convergence yet it still has a closed-form expression. The convergence property is both theoretically analyzed and numerically consolidated.",

author = "Yang Yang and Mengyi Zhang and Marius Pesavento and Palomar, \{Daniel P.\}",

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doi = "10.1109/ACSSC.2014.7094778",

language = "English",

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Yang, Y, Zhang, M, Pesavento, M & Palomar, DP 2015, An online parallel algorithm for spectrum sensing in cognitive radio networks. in MB Matthews (ed.), Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers., 7094778, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2015-April, IEEE Computer Society, pp. 1801-1805, 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 2/11/14. https://doi.org/10.1109/ACSSC.2014.7094778

An online parallel algorithm for spectrum sensing in cognitive radio networks. / Yang, Yang; Zhang, Mengyi; Pesavento, Marius et al.
Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. ed. / Michael B. Matthews. IEEE Computer Society, 2015. p. 1801-1805 7094778 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2015-April).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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N2 - We consider the estimation of the position and transmit power of primary users in cognitive radio networks based on solving a sequence of ℓ1-regularized least-square problems, in which the unknown vector is sparse and the measurements are only sequentially available. We propose an online parallel algorithm that is novel in three aspects: i) all elements of the unknown vector variable are updated in parallel; ii) the update of each element has a closed-form expression; and iii) the stepsize is designed to accelerate the convergence yet it still has a closed-form expression. The convergence property is both theoretically analyzed and numerically consolidated.

AB - We consider the estimation of the position and transmit power of primary users in cognitive radio networks based on solving a sequence of ℓ1-regularized least-square problems, in which the unknown vector is sparse and the measurements are only sequentially available. We propose an online parallel algorithm that is novel in three aspects: i) all elements of the unknown vector variable are updated in parallel; ii) the update of each element has a closed-form expression; and iii) the stepsize is designed to accelerate the convergence yet it still has a closed-form expression. The convergence property is both theoretically analyzed and numerically consolidated.

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Yang Y, Zhang M, Pesavento M, Palomar DP. An online parallel algorithm for spectrum sensing in cognitive radio networks. In Matthews MB, editor, Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers. IEEE Computer Society. 2015. p. 1801-1805. 7094778. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2014.7094778

An online parallel algorithm for spectrum sensing in cognitive radio networks

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