Paper Info
Title
Embedded Primary Users Identification And Channel Estimation For Underlay Cognitive Radio Network Based On Compressive Sensing
Abstract
Because of its spectrum-sharing nature, a CR network inevitably operates in interference-intensive environments. One challenge is how to maintain the interferences generated by the cognitive transmissions to the primary network below an acceptable threshold level. In this paper, we firstly propose an efficient primary users identification, using compressive sensing (CS). Our focus is on the angular sparsity of the received signal given an unknown number of primary user source signals impinging upon the antenna array from different directions of arrival (DOA). Given multiple snapshots, multiple measurement vectors (MMV) are available at the secondary base station and considered for primary channel detection over the angular domain using the regularized M-FOCUSS algorithm. Then, we develop novel methods for paths separation and primary channels estimation based on their autocorrelation matrix properties. Through simulations, we show that the recovery performance of the proposed approach in terms of false alarm and average minimum square error (MES) between the true and the estimated primary channel, is better than the conventional maximum to minimum eigenvalue (MME) detector.
Year
Venue
Keywords
2016
2016 IEEE 27TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC)
Cognitive underlay network, compressive sensing (CS), Non Line Of Sight (NLOS), Multiple-Input-Single-Output (MISO), channel estimation, M-FOCUSS
Field
DocType
Citations 
Base station,Primary channel,False alarm,Computer science,Autocorrelation matrix,Communication channel,Antenna array,Real-time computing,Detector,Compressed sensing
Conference
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Imen Sahnoun151.15
Inès Kammoun21614.53
Mohamed Siala37337.81