IEEE Signal Processing - May 2018 - 40
Zhijin Qin, Jiancun Fan, Yuanwei Liu, Yue Gao, and Geoffrey Ye Li
Sparse
Representation
for Wireless
Communications
©iStockphoto.com/naqiewei
A compressive sensing approach
S
parse representation can efficiently model signals in differ-
ent applications to facilitate processing. In this article, we
will discuss various applications of sparse representation
in wireless communications, with a focus on the most re-
cent 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.
Digital Object Identifier 10.1109/MSP.2018.2789521
Date of publication: 26 April 2018
40
This article starts from a comprehensive overview of CS
principles and different sparse domains potentially used in
5G and IoT networks. Then we introduce recent research
progress in applying CS to address the major opportunities
and challenges in these networks, including wide-band spec-
trum sensing in cognitive radio networks (CRNs), data col-
lection in IoT networks, and channel estimation and feedback
in massive multiple-input, multiple-output (MIMO) systems.
Moreover, we identify other potential applications and re-
search challenges for sparse representation for 5G and IoT
networks. This article will provide readers a clear picture of
IEEE Signal Processing Magazine
|
May 2018
|
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Table of Contents for the Digital Edition of IEEE Signal Processing - May 2018
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