Abstract
A precise positioning of transmitting nodes enhances the performance of Cognitive Radio (CR), by enabling more efficient dynamic allocation of channels and transmit powers for unlicensed users. Most localization techniques rely on random positioning of sensor nodes where, few sensor nodes may have a
Highlights
Received on 19 June, 2015; accepted on 26 November, 2015; published on 05 April, 2016 Keywords: Cognitive Radio, Compressive Sensing, Radio Environment Map, Localization, Power Measurements
The localization accuracy of the Compressive Sensing (CS) algorithm can be effected by certain external factors such as Signal to Noise Ratio (SNR), shadowing, density of Secondary User (SU) and distribution of SUs
The paper discusses the formulation of a novel algorithm to jointly deduce the location and transmit power of Primary Users (PUs) in a cognitive radio network
Summary
The CS technique is an approach for the solution of an under-determined set of equations for which the solution vector is known to be sparse. The complex Fourier Transform basis functions can be used to represent the time domain signal with few non-zero coefficients. In such case the CS algorithm can be used to obtain a sparsest solution vector to a set of underdetermined equations. This formulation is valid for a noiseless scenario but when external noise is considered the algorithm is modified to a Second-Order Cone Program for an optimized solution for a defined threshold [10]. This can be stated as, min x 1 = min |xi|. SU the CS algorithm is effective because the same solution vector usually has minimum l0 norm and minimum l1 norm [10]
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