Home > Research > Publications & Outputs > Sparse Representation for Wireless Communications

Electronic data

  • final_version_double_column

    Rights statement: ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

    Accepted author manuscript, 617 KB, PDF document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Sparse Representation for Wireless Communications: A Compressive Sensing Approach

Research output: Contribution to journalJournal article

Published
  • Zhijin Qin
  • Jiancun Fan
  • Yuanwei Liu
  • Yue Gao
  • Geioffrey Ye Li
Close
<mark>Journal publication date</mark>05/2018
<mark>Journal</mark>IEEE Signal Processing Magazine
Issue number3
Volume35
Number of pages19
Pages (from-to)40-58
Publication statusPublished
Early online date26/04/18
Original languageEnglish

Abstract

Sparse representation can efficiently model signals in different applications to facilitate processing. In this article, we will discuss various applications of sparse representation in wireless communications, with a focus on the most recent compressive sensing (CS)-enabled approaches. With the help of the sparsity property, CS is able to enhance the spectrum efficiency (SE) and energy efficiency (EE) of fifth-generation (5G) and Internet of Things (IoT) networks.

Bibliographic note

©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.