Sparse Representation for Wireless Communications : A Compressive Sensing Approach

Qin, Zhijin and Fan, Jiancun and Liu, Yuanwei and Gao, Yue and Li, Geioffrey Ye (2018) Sparse Representation for Wireless Communications : A Compressive Sensing Approach. IEEE Signal Processing Magazine, 35 (3). pp. 40-58. ISSN 1053-5888

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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.

Item Type:

Journal Article

Journal or Publication Title:

IEEE Signal Processing Magazine

Additional Information:

©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.

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/1700/1711

Subjects:

?? wireless communicationscompressive sensingsparsity property5ginternet of thingssignal processingapplied mathematicselectrical and electronic engineering ??

Departments:

Faculty of Science and Technology > School of Computing & Communications

ID Code:

89465

Deposited By:

ep_importer_pure

Deposited On:

05 Jan 2018 13:40

Refereed?:

Yes

Published?:

Published

Last Modified:

31 Dec 2023 00:53

URI:

https://eprints.lancs.ac.uk/id/eprint/89465