An online parallel algorithm for spectrum sensing in cognitive radio networks

Author

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

fYear

2014

Firstpage

1801

Lastpage

1805

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.

Keywords

cognitive radio; convergence; least mean squares methods; parallel algorithms; radio spectrum management; telecommunication computing; ℓ₁-regularized least square problem; cognitive radio network; convergence property; online parallel algorithm; position estimation; primary users; spectrum sensing; transmit power estimation; unknown vector variable; Approximation algorithms; Cognitive radio; Complexity theory; Convergence; Estimation; Optimization; Parallel algorithms;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2014 48th Asilomar Conference on

Print_ISBN

978-1-4799-8295-0

Type

conf

DOI

10.1109/ACSSC.2014.7094778

Filename

7094778