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Proceedings Paper

A new approach for high order MQAM signal modulation recognition
Author(s): Mohammed Tag Elsir Awad Elsoufi; Xiong Ying; Wang Jun; Tang Bin
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Paper Abstract

In this paper, a new modulation recognition algorithm is proposed. Communication Signals are recognized and classified based on Clustering techniques. Proposed algorithm uses Clustering Validity Measures as a key features extracted from MQAM signals. Fuzzy C-mean Clustering (FCM) is applied on received MQAM signal to produce a membership matrix of different clusters. Clustering Validity Measures are applied on the membership function. Different MQAM signals have different values of Validity Measures. This feature recognizes most MQAM signals with high confidentiality. At low SNR cases, a neural network with a conjugate gradient Learning approach is utilized to enhance algorithm performance. Fletcher-Reeves learning approach can improve the speed and rate of convergence. Simulation results prove the validity of proposed algorithm. No prior information is needed using proposed algorithm. Misclassification rate is less for low order MQAM signals.

Paper Details

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003358 (29 August 2016); doi: 10.1117/12.2245162
Show Author Affiliations
Mohammed Tag Elsir Awad Elsoufi, Univ. of Electronic Science and Technology of China (China)
Xiong Ying, Univ. of Electronic Science and Technology of China (China)
Wang Jun, Univ. of Electronic Science and Technology of China (China)
Tang Bin, Univ. of Electronic Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)

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