Compressed Sensing of Analog Signals in Shift-Invariant Spaces

TL;DR

This paper introduces a novel method for low-rate sampling of sparse continuous-time signals in shift-invariant spaces, leveraging compressed sensing techniques to significantly reduce sampling rates without prior knowledge of active generators.

Contribution

It develops a framework that extends compressed sensing to analog signals, enabling sub-Nyquist sampling of sparse signals in shift-invariant spaces without finite-dimensional models.

Findings

Sampling rate can be reduced below the Nyquist rate for sparse signals.

The method effectively reconstructs signals using compressed sensing algorithms.

Framework bridges the gap between analog sampling and compressed sensing literature.

Abstract

A traditional assumption underlying most data converters is that the signal should be sampled at a rate exceeding twice the highest frequency. This statement is based on a worst-case scenario in which the signal occupies the entire available bandwidth. In practice, many signals are sparse so that only part of the bandwidth is used. In this paper, we develop methods for low-rate sampling of continuous-time sparse signals in shift-invariant (SI) spaces, generated by m kernels with period T. We model sparsity by treating the case in which only k out of the m generators are active, however, we do not know which k are chosen. We show how to sample such signals at a rate much lower than m/T, which is the minimal sampling rate without exploiting sparsity. Our approach combines ideas from analog sampling in a subspace with a recently developed block diagram that converts an infinite set of sparse equations to a finite counterpart. Using these two components we formulate our problem within the framework of finite compressed sensing (CS) and then rely on algorithms developed in that context. The distinguishing feature of our results is that in contrast to standard CS, which treats finite-length vectors, we consider sampling of analog signals for which no underlying finite-dimensional model exists. The proposed framework allows to extend much of the recent literature on CS to the analog domain.