Compressed Sensing for Wireless Communications: Useful Tips and Tricks

Jun Won Choi, Byonghyo Shim, Yacong Ding, Bhaskar Rao, Dong In Kim

Research output: Contribution to journalArticlepeer-review

248 Citations (Scopus)


As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to grasp simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and useful tips and tricks that wireless communication researchers need to know when designing CS-based wireless systems. First, we present an overview of the CS technique, including basic setup, sparse recovery algorithm, and performance guarantee. Then, we describe three distinct subproblems of CS, viz., sparse estimation, support identification, and sparse detection, with various wireless communication applications. We also address main issues encountered in the design of CS-based wireless communication systems. These include potentials and limitations of CS techniques, useful tips that one should be aware of, subtle points that one should pay attention to, and some prior knowledge to achieve better performance. Our hope is that this paper will be a useful guide for wireless communication researchers and even non-experts to get the gist of CS techniques.

Original languageEnglish
Article number7842611
Pages (from-to)1527-1550
Number of pages24
JournalIEEE Communications Surveys and Tutorials
Issue number3
Publication statusPublished - 2017 Jul 1

Bibliographical note

Funding Information:
Manuscript received May 5, 2016; revised December 1, 2016; accepted January 20, 2017. Date of publication February 6, 2017; date of current version August 21, 2017. This work was supported by the National Research Foundation of Korea through the Korean Government (MSIP) under Grant 2016R1A2B3015576 and Grant 2014R1A5A1011478.

Publisher Copyright:
© 1998-2012 IEEE.


  • Compressed sensing
  • greedy algorithm
  • l-norm
  • performance guarantee
  • sparse signal
  • underdetermined systems
  • wireless communication systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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