Digital modulation signal identifying method under non-gaussian noise in cognitive radio

Abstract

The invention discloses a digital modulation identifying method based on fractional lower order cyclic spectrum related coefficient under the non-gaussian noise in a cognitive radio, and the digital modulation identifying method is capable of solving the problems of bad modulation identifying performance and high calculating complexity under the background of the non-gaussian noise in the cognitive radio. The method comprises the following steps: sampling received signals; calculating relative coefficients rho 1, rho 2, rho 3, rho 4 and rho 5 of projections of fractional lower order cyclic spectrums of the fractional lower order cyclic spectrum calculating signals of the sampled signals at a section of the cycle frequency epsilon=0, a section of frequency f=0, and the cycle frequency epsilon face, and the projection at the frequency f face; and arranging a judgment threshold of a signal set, and identifying the signals in different modulating manners through a classifier based on a judging tree. Under a non-gaussian alpha stably distributed noise, the digital modulation identifying method is relatively high in identification rate, good in stability, and lower in calculation complexity, and is more suitable for a cognitive radio system.

Description

Technical field [0001] The invention belongs to the field of communication technology, and specifically relates to a method for identifying digital modulation signals under non-Gaussian Alpha stable distribution noise, which can be used to identify the modulation mode type of digital modulation signals under Alpha stable distribution noise. Background technique [0002] With the development of communication technology in the direction of wireless and broadband, spectrum resources are becoming increasingly scarce, CR (cognitive radio, cognitive radio) technology has become one of the key technologies to solve this problem. For CR spectrum sensing technology, spectrum sensing must not only accurately detect the appearance of authorized user signals, but also identify its modulation type, and then determine prior information such as authorized users’ business types and business strengths, so as to use these priors. The information enables CR users to discover and use idle spectrum m...

AI Technical Summary

Problems solved by technology

This method uses the Kolmogorov-Smirnov test method to identify MQAM and MPSK signals under Alpha stable distribution noise, but this method has poor recognition performance under low mixed signal-to-noise ratio; see Yang Weichao, Zhao Chunhui, Cheng Baozhi.Alpha Communication signal recognition under stable distribution noise[J].Journal of Applied Science,2010,28(2):111-114. This method recognizes the signal under the Alpha stable distribution noise, using the fractal box dimension as the characteristic parameter , but this method can only be applied in a certain range of mixed signal-to-noise ratio and the recognition performance is poor; because the signal under the Alpha stable distribution noise does not have second-order or higher-order statistics, see He Tao. Digital communication signal modulation recognition Research on some new problems [D]. University of Electronic Science and Technology of China, 2007. This method uses low-order statistics to conduct research on modulation recognition, but the recognition performance of this method is poor; see Zhao Chunhui, Yang Weichao, Du Yu. Using fractional low-order Modulation recognition of cyclic spectral coherence coefficient[J]. Chinese Journal of Applied Science, 2011, 29(6):565-570. (hereinafter referred to as document 4) and Zhao Chunhui, Yang Weichao, Ma Shuang. Communication based on generalized second-order cyclic statistics Research on Signal Modulation Recognition[J]. Journal of Communications, 2011,32(1):144-150. (hereinafter referred to as Document 5). method to identify digitally modulated signals, but these two methods have high computational complexity and poor recognition performance

Therefore, the above methods are not suitable for application in practical cognitive radio systems

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