Signals Modulation Recognition Based on Efficient Attribute Reduction with Neighborhood Rough Set

Abstract:

The purpose of communication signals automatic modulation recognition is to judge signal modulation styles and estimate signal modulation parameters on the precondition of unknown modulation information. According to the seven kinds communication modulation signals studied in this paper, select a group of feature parameters based on the time-frequency characteristics of communication signals. The fast algorithm for attribute reduction based on neighborhood rough set using feature selection is introduced in detail. Then, using back propagation network as classification instruments to identify signals. The simulation shows that the method can not only reduce the number of feature parameters, but also improve the recognition rate.

• Periodical: Applied Mechanics and Materials (Volumes 220-223)
• Pages: 2301-2307
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.220-223.2301
• Online since: November 2012
• Authors: Ying Zheng Han, Juan Ping Wu, Xiao Fang Liang
• Keywords: BP Neural Network (BPNN), Fast Attribute Reduction, Feature Selection, Modulation Recognition, Neighborhood Rough Set

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