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Compressed spectrum sensing in the presence of interference: comparison of sparse recovery strategies
Lagunas Targarona, Eva; Nájar Martón, Montserrat
Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
Existing approaches to Compressive Sensing (CS) of sparse spectrum has thus far assumed models contaminated with noise (either bounded noise or Gaussian with known power). In practical Cognitive Radio (CR) networks, primary users must be detected even in the presence of low-regulated transmissions from unlicensed systems, which cannot be taken into account in the CS model because of their non-regulated nature. In [1], the authors proposed an overcomplete dictionary that contains tuned spectral shapes of the primary user to sparsely represent the primary users' spectral support, thus allowing all frequency location hypothesis to be jointly evaluated in a global unified optimization framework. Extraction of the primary user frequency locations is then performed based on sparse signal recovery algorithms. Here, we compare different sparse reconstruction strategies and we show through simulation results the link between the interference rejection capabilities and the positive semidefinite character of the residual autocorrelation matrix.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
Cognitive radio
Signal processing
Cognitive radio
Compressed sensing
Interference suppression
Radio spectrum management
Signal detection
Ràdio cognitiva
Tractament del senyal
Attribution-NonCommercial-NoDerivs 3.0 Spain
Institute of Electrical and Electronics Engineers (IEEE)

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