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The role of the tutorials is to provide a platform for a more intensive scientific exchange amongst researchers interested in a particular topic and as a meeting point for the community. Tutorials complement the depth-oriented technical sessions by providing participants with broad overviews of emerging fields. A tutorial can be scheduled for 1.5 or 3 hours.

Introduction to Compressed Sensing with Applications in Communications Using Matlab



Luis F. Abanto-Leon
Eindhoven University of Technology

Compressed sensing (CS) is a relatively recent foundational theory with pragmatical sense and intuitive understanding that has found applications in several areas. It has become a sound and well-founded technique that promises to facilitate solutions to complex optimization problems when elemental necessary conditions can be fulfilled. Even when a signal is not inherently sparse, such state can be promoted by means of sparsifying procedures. And thus the convenient characteristics of CS can be exploited. Comprehending the underlying principles of CS and the mechanisms that explain why CS works is a most important asset for the WINSYS community as it reveals a new perspective to approach problems in a less conventional fashion, possibly with a simplified procedure and improved results. The objective of this tutorial is to provide a general overview of CS and demystify its concept by means of examples and applications accompanied with a programming session in order to shorten the gap between theory and practice. 


compressed sensing, sparsity, reconstruction, angle of arrival estimation, signal denoising, channel estimation

Target Audience

Mainly aimed at graduate students although this would not prevent undergraduate students and early-stage researchers from attending.

Detailed Outline

1) Nyquist sampling vs compressed sensing; 30 min

2) Compressed recovery, necessary conditions and norms; 30 min

3) How to formulate optimization problems exploiting inherent / enforced sparsity; 30 min

4) Installation of CVX toolbox in Matlab; 5 min

5) Signal denoising; 20 min

6) Audio denoising; 5 min

7) Image denoising; 5 min

8) Angle of arrival estimation; 30 min

9) Channel estimation; 25 min

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