Radio frequency (RF) technology enables the modulation of narrowband signals
by high carrier frequencies. As a result, many important and emerging applications,
such as cognitive radios, have to sense and sample wideband signals with
extremely high Nyquist rates, leading to a large number of samples that need
to be transmitted, stored and processed. Those required sampling rates
may even exceed today's best analog-to-digital (ADCs) front-end bandwidths
by orders of magnitude. Therefore we need to exploit the structure of the input
signal in order to acquire it. Such wideband signals are typically sparse, that is,
consist of a relatively small number of narrowband transmissions spread across
a wide spectrum. We present a sampling system, the modulated wideband
converter (MWC), that performs preprocessing on a sparse analog signal before
sampling it at a low rate, namely twice the Landau rate. We then develop a digital
architecture that reconstructs the analog signal, showing that we can recover
the original signal from the sub-Nyquist samples.

?What is Sub-Nyquist Sampling

We live in an analog world, but data processing is usually performed by digital computers.
The transition from the analog (continuous time) to the digital world is called sampling.

In most analog-to-digital converters (ADCs) today, sampling is based on the Shannon-Nyquist theorem, which requires sampling at a rate that is at least twice the highest signal frequency.

As the bandwidth of the signal increases, it demands the increase in sampling frequency, which raises a number of critical issues that affect system design:

There is a need for expensive wideband ADCs which require excessive hardware solutions and consume a lot of power.

Computer systems need more memory and more computing power in order to process the sampled data. In many cases, much of the sampled information is compressed and reduced in later stages of the processing.

Sub-Nyquist sampling offers a new way of smart and effective sampling of wideband signals by performing analog preprocessing prior to sampling. The idea is to exploit the same structure that is used in the digital chain in order to drastically reduce the sampling rate and only sample the information in the signal that is actually needed. Thus, instead of sampling at a high rate and then compressing the data, it is possible to sample the signal at a low rate to begin with. Low sampling rate also enables low-rate digital processing and reduces required system memory and power.

This technology has many potential applications in a large variety of fields such as communications, radar systems, medical imaging, optical systems, super-resolution microscopy and more.

At the event we will present algorithms and systems developed in the area of sub-Nyquist sampling.

M. Mishali and Y. C. Eldar, "Xampling: Compressed Sensing for Analog Signals", Compressed Sensing: Theory and Applications, Edited by Y. C. Eldar and G. Kutyniok, Cambridge University Press, 2012.

Additional information on Xampling can be found at Prof. Yonina Eldar’s website.

For further information on Xampling demo systems click here.

We look forward to seeing you at the event.

Please feel free to forward this invitation within your organization.