A User's Guide to Compressed Sensing for Communications Systems

Masaaki just sent me the following today:

Dear Igor,

How's everything going with you? Today I would like to introduce our new survey paper on CS for communications: K. Hayashi, M. Nagahara, and T. Tanaka, A User's Guide to Compressed Sensing for Communications Systems, IEICE Trans. on Communications, Vol. E96-B, No. 3, pp. 685-712, Mar. 2013.

The paper can be downloaded from

http://search.ieice.org/bin/pdf_link.php?category=B&lang=E&year=2013&fname=e96-b_3_685&abst=

or from my blog entry

http://sparseland.blogspot.jp/2013/03/a-users-guide-to-compressed-sensing-for.html

You can also get SCILAB codes for executing the algorithms given in the paper from

http://www.msys.sys.i.kyoto-u.ac.jp/csdemo.html

In the paper, we mentioned your web pages (see [208], [209]). I am happy if you cover our paper on Nuit Blanche.

Best regards,

Masaaki Nagahara

nagahara@ieee.org

http://www-ics.acs.i.kyoto-u.ac.jp/~nagahara/

Sure Masaaki ! Here it is: A User's Guide to Compressed Sensing for Communications Systems by 

Kazunori Kazunori , Masaaki Nagahara and Tanaka Toshiyuki. The summary reads:

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.

SCILAB codes here.

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