Compressed spectrum sensing in the presence of interference: Comparison of sparse recovery strategies

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.