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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Sparse Representation for Wireless Communications
T2 - A Compressive Sensing Approach
AU - Qin, Zhijin
AU - Fan, Jiancun
AU - Liu, Yuanwei
AU - Gao, Yue
AU - Li, Geioffrey Ye
N1 - ©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.
PY - 2018/5
Y1 - 2018/5
N2 - 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.
AB - 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.
KW - Wireless communications
KW - compressive sensing
KW - sparsity property
KW - 5G
KW - Internet of Things
U2 - 10.1109/MSP.2018.2789521
DO - 10.1109/MSP.2018.2789521
M3 - Journal article
VL - 35
SP - 40
EP - 58
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
SN - 1053-5888
IS - 3
ER -