Abstract
Now a days Compressed Sensing (CS) is one of the crucial task in signal processing. To reconstruct the signal efficiently, we can follow the Nyquist criteria by finding solutions to undetermined linear systems. These systems have less no of equations with more unknowns. This paper concentrates on the use of sub-Nyquist technique such as CS which detect the spectrum usage without the prior information of sparsity. Narrowband QPSK signal is recovered by using OMP (Orthogonal Matching Pursuit), CoSaMP (Compressive Sampling Matching Pursuit) and l-1 minimization algorithms. Experimental results show that the comparative analysis of the above algorithms in terms of MSE(Mean Square Error) and SNR(Signal to Noise Ratio). All simulations are carried out using MATLAB.
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