Non-Uniform Sampling

Non-uniform sampling allows to reduce the number of acquired samples compared to traditional Nyquist sampling when a signal is sparse in the Fourier domain and compressed sensing reconstruction algorithms are used. A new concept of such a sampler called Random Sampling Slope ADC (RSS-ADC) is introduced here.

Authors: Patrick Maechler, Norbert Felber, Hubert Kaeslin, and Andreas Burg.

Abstract: Spectrum sensing, i.e. the identification of occupied frequencies within a large bandwidth, requires complex sampling hardware. Measurements suggest that only a small fraction of the available spectrum is actually used at any time and place, which allows a sparse characterization of the frequency domain signal. Compressed sensing (CS) can exploit this sparsity and simplify measurements. We investigate the performance of a very simple hardware architecture based on the slope analog-to- digital converter (ADC), which allows to sample signals at unevenly spaced points in time. CS algorithms are used to identify the occupied frequencies, which can be continuously distributed across a large bandwidth.

Full paper:

P. Maechler, N. Felber, H. Kaeslin, and A. Burg, "Hardware-Efficient Random Sampling of Fourier-Sparse Signals", Proc. ISCAS, May 2012

First paper on RSS-ADC with basic concept:

P. Maechler, N. Felber, and A. Burg, "Random Sampling ADC for Sparse Spectrum Sensing", Proc. Eusipco, pp.1200-1204, Sept. 2011