Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

Maximum-Likelihood Classification for MPSK with Compressive Samplings

Authors
Nian Tong, Lichun Li, Xun Lu
Corresponding Author
Nian Tong
Available Online July 2015.
DOI
10.2991/icismme-15.2015.411How to use a DOI?
Keywords
modulation classification; MPSK; compressive sensing
Abstract

This paper focuses on the classification of the MPSK modulations using compressive measurements in additional Gaussian white noise (AWGN). Under the compressive sensing (CS) frame, the compressive maximum-likelihood (CML) classifier provided in this paper tries to recognize the MPSK signals using far fewer samplings than traditional maximum-likelihood (TML) classifier needs. This paper presents the criterion of classification and the classification performance analysis. Finally, several numerical simulations are provided and the results indicate that the CML classifier have a satisfied performance in higher SNR with far lower complexity. It’s an effective approach to promote the real-time property of communication system

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
Series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-62520-67-7
ISSN
1951-6851
DOI
10.2991/icismme-15.2015.411How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Nian Tong
AU  - Lichun Li
AU  - Xun Lu
PY  - 2015/07
DA  - 2015/07
TI  - Maximum-Likelihood Classification for MPSK with Compressive Samplings
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 1996
EP  - 1999
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.411
DO  - 10.2991/icismme-15.2015.411
ID  - Tong2015/07
ER  -