A sequential sampling algorithm that adapts to the uncertain sparsity in signal environment

Compressive sensing (CS) achieves efficiencies in signal collection, particularly in scenarios where the monitored bandwidth is large and the signal of interest is sparse. In this paper we present a survey of published hardware prototypes by assessing their architecture and comparing their performance to conventional analog-to-digital converters (ADCs). We also present an algorithm which adapts to the changing sparsity of signal environment by dynamically assigning sampling rate in order to improve the applicability of CS ADCs in environment with uncertain input signal sparsity. Our results provide practical guidelines in signal monitoring of wideband spectrum.