Random Sampling ADC for Sparse Spectrum Sensing
Scanning large bandwidths (spectrum sensing) pushes today's analog hardware to its limits since periodic sampling at Nyquist rate with sufficient resolution is often prohibitively complex. In this paper, the authors consider a scenario where the signal to be acquired is sparse in the frequency domain (e.g., spectrum sensing in cognitive radio applications) and they are interested in identifying the sparse support of the signal. For this type of applications, they describe a new Analog-to-Digital Converter (ADC) architecture that acquires unequally spaced samples based on a slope ADC, which is one of the least complex ADC architectures available. For the signal reconstruction, they employ algorithms from compressed sensing for the recovery of the dominant spectral components.