IEEE Signal Processing - May 2018 - 56
either the sparsity model or the CS-enabled approach needs
to be confirmed by some measured results under different
propagation conditions.
Channel estimation and feedback with joint support
In the aforementioned channel estimation and feedback
schemes, one or two of the three types of channel sparsity are
used to improve the channel estimation performance and
reduce the feedback overhead. Future research needs to
explore whether performance can be further improved if all
three types of channel sparsity are used and, if so, how much
performance gain might be achieved.
Channel estimation and feedback
with unknown support
Channel sparsity directly affects CS-enabled channel estima-
tion performance. However, it is still a challenging problem
how to get the information on channel sparsity support. At the
same time, it is also worth studying how channel estimation
and feedback approaches should be designed without channel
sparsity support.
Other CS applications
CS-aided localization
In a multiple target localization network, the multiple target
locations can be formulated as a sparse matrix in the discrete
spatial domain. By exploiting the spatial sparsity, instead of
recording all received signal strengths (RSSs) over the spa-
tial grid to construct a radio map from the targets, far fewer
numbers of RSS measurements need to be collected at run-
time. Subsequently, the target locations can be recovered
from the collected measurements through solving an , 1 min-
imization problem. As a result, the multiple target locations
can be recovered.
CS-aided impulse noise cancelation
In certain applications, such as OFDM, impulsive noise will
severely degrade the system performance. The OFDM signal
is often processed in the frequency domain. Even if the
impulse noise lasts only a short time, it affects a wide frequen-
cy range. By regarding impulse noise as a sparse vector, one
study exploited CS to mitigate such noise [67].
CS-aided cloud radio access networks
Cloud radio access networks (C-RANs) have been proposed as
a promising technology to support massive connectivity in 5G
networks. In C-RANs, the BSs are replaced by remote radio
heads (RRHs) and connected to a central processor via digital
backhaul links. Thanks to the spatial and temporal variation of
the mobile traffic, it is feasible to switch off some RRHs in
green C-RANs, provided the quality of service is guaranteed.
More specifically, one RRH will be switched off only when all
the coefficients in its beamformer are set to zero. Such a group
sparsity property inspires us to apply CS to active RRH selec-
tion in green C-RANs to minimize network power consump-
56
tion [68], [69]. Additionally, in C-RANs' uplink, the channel
estimation from the active users to the RRHs is the key to
achieving the spatial multiplexing gain. Generally, the number
of active users is low in C-RANs, which makes it possible to
apply CS to reduce the uplink training overhead for channel
estimation. Moreover, the correlation among active users at
different RRHs exhibits a joint sparsity property, which can
further facilitate the active user detection and channel estima-
tion in C-RANs [70].
Conclusions
This article provided a comprehensive overview of sparse rep-
resentation, with applications in wireless communications.
Specifically, after introducing the basic CS principles, we iden-
tified the common sparse domains in 5G and IoT networks.
Subsequently, we discussed the exploitation of different sparsi-
ty properties in three CS-enabled networks: wide-band spec-
trum sensing in CRNs, data collection in IoT networks, and
channel estimation and feedback in massive MIMO systems.
In the previous discussion, we concluded that, by invoking CS,
the SE and EE of 5G and IoT networks can be enhanced from
different perspectives. Furthermore, we identified potential
research challenges to provide a guide for researchers interest-
ed in sparse representation in 5G and IoT networks.
Acknowledgments
Jiancun Fan is supported by the National Natural Science
Foundation of China under grant 61671367, and by the China
Postdoctoral Science Foundations under grants 2014M560780
and 2015T81031. Yue Gao is supported by funding from the
Physical Sciences Research Council in the United Kingdom
through grant EP/R00711X/1.
Authors
Zhijin Qin (zhijin.qin@lancaster.ac.uk) received her B.S.
degrees from Queen Mary University of London (QMUL) and
Beijing University of Posts and Telecommunications in 2012,
and her Ph.D. degree in electronic engineering from QMUL in
2016. She was with Imperial College London as a research
associate. She has been a lecturer (assistant professor) with
Lancaster University, United Kingdom, since August 2017.
Her research interests include compressive sensing, the Inter-
net of Things, and nonorthogonal multiple access. She
received the Best Paper Award at the 2012 Wireless Technolo-
gy Symposium and the 2017 IEEE Global Communica-
tions Conference.
Jiancun Fan (fanjc@xjtu.edu.cn) received his B.S. and
Ph.D. degrees in electrical engineering from Xi'an Jiaotong
University, China, in 2004 and 2012, respectively. From 2009
to 2011, he was a visiting scholar at the Georgia Institute of
Technology, Atlanta. He is currently an associate professor
at Xi'an Jiaotong University. His general research interests
include signal processing and wireless communications,
with an emphasis on multiple-input, multiple-output com-
munication systems, cross-layer optimization for spectral-
and energy-efficient networks, practical issues in long-term
IEEE Signal Processing Magazine
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May 2018
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