Sub-nyquist sampling techniques

A number of novel theoretical methods have been developed in an attempt to analyse data produced by sampling a signal at below the Nyquist rate and the limitations of the approaches have been investigated. A technique is developed that allows, under specified conditions, the frequency and amplitude of a band-limited sinusoidal signal (with no harmonics) to be determined when the signal is sampled simultaneously with three uniform samplers at below the Nyquist rate. The three samplers operate at slightly different rates. Each has its output ideally low-pass filtered with a cut-off frequency at half the sampling rate. The frequencies of the signals output from the ideal filters are analysed to determine the input sinusoid parameters. The frequency of the sinusoid can also be found within a calculated tolerance when approximate filter output frequencies are known. Two approaches extending this technique for a band-limited periodic signal consisting of more than just the fundamental, enable the frequencies of the harmonics to be found for the signal, but there is the possibility that other erroneous harmonics may be identified as part of the signal. The probability of this occurring can be reduced by uniformly sampling simultaneously with a greater number of samplers. This probability cannot reach zero. Furthermore, as the number of samplers increases or the number of signal harmonics increases, the computational workload imposed in determining the harmonic frequencies rises dramatically. The approaches are rendered impractical and sampling at irregular intervals is suggested as an alternative to using a very large number of uniform samplers. A modified discrete Fourier transform and its inverse are developed to allow an estimated spectral analysis of a continuous periodic signal sampled at irregular intervals. Additive pseudo-random sampling and periodic sampling with dither are rigorously defined as two proposed irregular sampling schemes. The periodicity and symmetrical properties of the modified transform are derived for the two schemes. Consistently alias-free spectral analysis of a band-limited periodic signal is demonstrated using additive pseudo-random sampling with a maximum sampling rate below the Nyquist rate. This does not apply when using periodic sampling with dither

Sub-Nyquist Sampling: Bridging Theory and Practice

Sampling theory encompasses all aspects related to the conversion of continuous-time signals to discrete streams of numbers. The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. In modern applications, an increasingly number of functions is being pushed forward to sophisticated software algorithms, leaving only those delicate finely-tuned tasks for the circuit level. In this paper, we review sampling strategies which target reduction of the ADC rate below Nyquist. Our survey covers classic works from the early 50's of the previous century through recent publications from the past several years. The prime focus is bridging theory and practice, that is to pinpoint the potential of sub-Nyquist strategies to emerge from the math to the hardware. In that spirit, we integrate contemporary theoretical viewpoints, which study signal modeling in a union of subspaces, together with a taste of practical aspects, namely how the avant-garde modalities boil down to concrete signal processing systems. Our hope is that this presentation style will attract the interest of both researchers and engineers in the hope of promoting the sub-Nyquist premise into practical applications, and encouraging further research into this exciting new frontier.Comment: 48 pages, 18 figures, to appear in IEEE Signal Processing Magazin

Channel Capacity under General Nonuniform Sampling

This paper develops the fundamental capacity limits of a sampled analog channel under a sub-Nyquist sampling rate constraint. In particular, we derive the capacity of sampled analog channels over a general class of time-preserving sampling methods including irregular nonuniform sampling. Our results indicate that the optimal sampling structures extract out the set of frequencies that exhibits the highest SNR among all spectral sets of support size equal to the sampling rate. The capacity under sub-Nyquist sampling can be attained through filter-bank sampling, or through a single branch of modulation and filtering followed by uniform sampling. The capacity under sub-Nyquist sampling is a monotone function of the sampling rate. These results indicate that the optimal sampling schemes suppress aliasing, and that employing irregular nonuniform sampling does not provide capacity gain over uniform sampling sets with appropriate preprocessing for a large class of channels.Comment: 5 pages, to appear in IEEE International Symposium on Information Theory (ISIT), 201

Special Considerations in Estate Planning for Same-Sex and Unmarried Couples

Sub-Nyquist sampling makes use of sparsities in analog signals to sample them at a rate lower than the Nyquist rate. The reduction in sampling rate, however, comes at the cost of additional digital signal processing (DSP) which is required to reconstruct the uniformly sampled sequence at the output of the sub-Nyquist sampling analog-to-digital converter. At present, this additional processing is computationally intensive and time consuming and offsets the gains obtained from the reduced sampling rate. This paper focuses on sparse multi-band signals where the user band locations can change from time to time and the reconstructor requires real-time redesign. We propose a technique that can reduce the computational complexity of the reconstructor. At the same time, the proposed scheme simplifies the online reconfigurability of the reconstructor

From Theory to Practice: Sub-Nyquist Sampling of Sparse Wideband Analog Signals

Conventional sub-Nyquist sampling methods for analog signals exploit prior information about the spectral support. In this paper, we consider the challenging problem of blind sub-Nyquist sampling of multiband signals, whose unknown frequency support occupies only a small portion of a wide spectrum. Our primary design goals are efficient hardware implementation and low computational load on the supporting digital processing. We propose a system, named the modulated wideband converter, which first multiplies the analog signal by a bank of periodic waveforms. The product is then lowpass filtered and sampled uniformly at a low rate, which is orders of magnitude smaller than Nyquist. Perfect recovery from the proposed samples is achieved under certain necessary and sufficient conditions. We also develop a digital architecture, which allows either reconstruction of the analog input, or processing of any band of interest at a low rate, that is, without interpolating to the high Nyquist rate. Numerical simulations demonstrate many engineering aspects: robustness to noise and mismodeling, potential hardware simplifications, realtime performance for signals with time-varying support and stability to quantization effects. We compare our system with two previous approaches: periodic nonuniform sampling, which is bandwidth limited by existing hardware devices, and the random demodulator, which is restricted to discrete multitone signals and has a high computational load. In the broader context of Nyquist sampling, our scheme has the potential to break through the bandwidth barrier of state-of-the-art analog conversion technologies such as interleaved converters.Comment: 17 pages, 12 figures, to appear in IEEE Journal of Selected Topics in Signal Processing, the special issue on Compressed Sensin