Title :
An online parallel algorithm for spectrum sensing in cognitive radio networks
Author :
Yang Yang ; Mengyi Zhang ; Pesavento, Marius ; Palomar, Daniel P.
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;
Conference_Titel :
Signals, Systems and Computers, 2014 48th Asilomar Conference on
Print_ISBN :
978-1-4799-8295-0
DOI :
10.1109/ACSSC.2014.7094778
