A Robust Signal Recognition Method for Communication System under Time-Varying SNR Environment

Jing-Chao LI  Yi-Bing LI  Shouhei KIDERA  Tetsuo KIRIMOTO  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.12   pp.2814-2819
Publication Date: 2013/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.2814
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Pattern Recognition
recognition method for communication signals,  entropy characteristic,  interval gray relation theory,  time-varying SNR environment,  

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As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.