Maximum-Likelihood Classification for MPSK with Compressive Samplings

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/).

Download article (PDF)

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  -