Mar 2, 2013

A User's Guide to Compressed Sensing for Communications Systems

A survey paper has been recently published, entitled A User's Guide to Compressed Sensing for Communications Systems, which I co-authored with Kazunori and Toshiyuki.

PDF is available here, and SCILAB codes here.

Compressed sensing (CS) becomes more and more popular in communications engineering.  In this paper, you can find many applications of CS to communications systems.  The summary reads
SUMMARY: This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on ell-1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the ell-1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control.
Enjoy CS with this guide!

Related entries:

Igor has featured our paper on his blog entry. Thank you Igor! (07/Mar/2013)


  1. Interesting figure here - the caption is in Danish.

  2. Thanks for sharing and writing the comprehensive users guide to compressed sensing. The research work helped me in understanding various aspects of compressed sensing.