Spatial Modulation Based on Supervised Learning

Ping Yang; Jing Zhu; Yue Xiao; Yong Liang Guan

doi:10.1109/ICDSP.2018.8631879

Spatial Modulation Based on Supervised Learning

Ping Yang, Jing Zhu, Yue Xiao, Yong Liang Guan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

In this paper, we propose a semi-blind super-vised learning(SL)-based detector for spatial modulation (SM) multiple-input multiple-output (MIMO) systems, which has low cost and low complexity. Specifically, the proposed scheme exploits the the rotation characteristic of constellation symbols for reducing the the length of the training sequence required for accurate signal classification. We proved that this reduction causes no performance loss compared with the conventional SL detector that uses the full length of training sequence. Moreover, we found that the ratio of the reduction depends on the specific constellation type. Our simulation results show that the proposed SL detector is capable of reducing up to 81% training load and complexity compared to the conventional SL detector.

Original language	English
Title of host publication	2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538668115
DOIs	https://doi.org/10.1109/ICDSP.2018.8631879
Publication status	Published - Jul 2 2018
Externally published	Yes
Event	23rd IEEE International Conference on Digital Signal Processing, DSP 2018 - Shanghai, China Duration: Nov 19 2018 → Nov 21 2018

Publication series

Name	International Conference on Digital Signal Processing, DSP
Volume	2018-November

Conference

Conference	23rd IEEE International Conference on Digital Signal Processing, DSP 2018
Country/Territory	China
City	Shanghai
Period	11/19/18 → 11/21/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

ASJC Scopus Subject Areas

Signal Processing

Access to Document

10.1109/ICDSP.2018.8631879

Cite this

Yang, P., Zhu, J., Xiao, Y., & Guan, Y. L. (2018). Spatial Modulation Based on Supervised Learning. In 2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018 Article 8631879 (International Conference on Digital Signal Processing, DSP; Vol. 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDSP.2018.8631879

@inproceedings{9ea5f93b14f448989c999847ad635187,

title = "Spatial Modulation Based on Supervised Learning",

abstract = "In this paper, we propose a semi-blind super-vised learning(SL)-based detector for spatial modulation (SM) multiple-input multiple-output (MIMO) systems, which has low cost and low complexity. Specifically, the proposed scheme exploits the the rotation characteristic of constellation symbols for reducing the the length of the training sequence required for accurate signal classification. We proved that this reduction causes no performance loss compared with the conventional SL detector that uses the full length of training sequence. Moreover, we found that the ratio of the reduction depends on the specific constellation type. Our simulation results show that the proposed SL detector is capable of reducing up to 81% training load and complexity compared to the conventional SL detector.",

author = "Ping Yang and Jing Zhu and Yue Xiao and Guan, {Yong Liang}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 23rd IEEE International Conference on Digital Signal Processing, DSP 2018 ; Conference date: 19-11-2018 Through 21-11-2018",

year = "2018",

month = jul,

day = "2",

doi = "10.1109/ICDSP.2018.8631879",

language = "English",

series = "International Conference on Digital Signal Processing, DSP",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018",

address = "United States",

}

Yang, P, Zhu, J, Xiao, Y & Guan, YL 2018, Spatial Modulation Based on Supervised Learning. in 2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018., 8631879, International Conference on Digital Signal Processing, DSP, vol. 2018-November, Institute of Electrical and Electronics Engineers Inc., 23rd IEEE International Conference on Digital Signal Processing, DSP 2018, Shanghai, China, 11/19/18. https://doi.org/10.1109/ICDSP.2018.8631879

Spatial Modulation Based on Supervised Learning. / Yang, Ping; Zhu, Jing; Xiao, Yue et al.
2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8631879 (International Conference on Digital Signal Processing, DSP; Vol. 2018-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Spatial Modulation Based on Supervised Learning

AU - Yang, Ping

AU - Zhu, Jing

AU - Xiao, Yue

AU - Guan, Yong Liang

PY - 2018/7/2

Y1 - 2018/7/2

N2 - In this paper, we propose a semi-blind super-vised learning(SL)-based detector for spatial modulation (SM) multiple-input multiple-output (MIMO) systems, which has low cost and low complexity. Specifically, the proposed scheme exploits the the rotation characteristic of constellation symbols for reducing the the length of the training sequence required for accurate signal classification. We proved that this reduction causes no performance loss compared with the conventional SL detector that uses the full length of training sequence. Moreover, we found that the ratio of the reduction depends on the specific constellation type. Our simulation results show that the proposed SL detector is capable of reducing up to 81% training load and complexity compared to the conventional SL detector.

AB - In this paper, we propose a semi-blind super-vised learning(SL)-based detector for spatial modulation (SM) multiple-input multiple-output (MIMO) systems, which has low cost and low complexity. Specifically, the proposed scheme exploits the the rotation characteristic of constellation symbols for reducing the the length of the training sequence required for accurate signal classification. We proved that this reduction causes no performance loss compared with the conventional SL detector that uses the full length of training sequence. Moreover, we found that the ratio of the reduction depends on the specific constellation type. Our simulation results show that the proposed SL detector is capable of reducing up to 81% training load and complexity compared to the conventional SL detector.

UR - http://www.scopus.com/inward/record.url?scp=85062794675&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062794675&partnerID=8YFLogxK

U2 - 10.1109/ICDSP.2018.8631879

DO - 10.1109/ICDSP.2018.8631879

M3 - Conference contribution

AN - SCOPUS:85062794675

T3 - International Conference on Digital Signal Processing, DSP

BT - 2018 IEEE 23rd International Conference on Digital Signal Processing, DSP 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 23rd IEEE International Conference on Digital Signal Processing, DSP 2018

Y2 - 19 November 2018 through 21 November 2018

ER -

Spatial Modulation Based on Supervised Learning

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

Access to Document

Other files and links

Fingerprint

Cite this