Performance analysis of digital wideband receiver based on reconstruction of compressed sensing data

Compressed sensing has been applied in various areas of signal processing, image processing, and RF. It is attractive due to wideband coverage with reduced sampling rate. Nyquist sampling data can be approximately restored from the compressed sensing reduced sampling data using reconstruction algorithms. These algorithms usually require several optimizations to control accuracy and convergent speed in Nyquist sampling data reconstruction. Foreseeing the future hardware implementation performing the reconstruction, one needs to identify and optimize the relevant parameters in reconstruction algorithms for the best performance. In this paper, we present performance analysis of a compressed-sensing based digital wideband receiver. The compressed sensing scheme is non-uniform sampling and the subsequent reconstruction algorithm is NESTA. The restored Nyquist sampling data is analyzed with conventional frame-based fast Fourier transform FFT) to calculate sensitivity and spurious-free dynamic range. The signal detection threshold is determined from probability density function and false alarm rate. The effect of parameters controlling convergence and accuracy in Nyquist sampling data reconstruction on the receiver performance is presented. Comparison of sensitivity and dynamic range with the conventional Nyquist based FFT receiver is also presented.