Stochastic resonance preprocessing-based digital modulation mode automatic identification method

Abstract

The invention discloses a stochastic resonance preprocessing-based digital modulation mode automatic identification method and mainly solves a problem that the prior art has low modulation mode automatic identification performance under a low signal noise condition. The method comprises: sampling received continuous digital modulation signals; normalizing the sampled signals; preprocessing the normalized signals by using a bistable stochastic resonance system to improve the signal-to-noise ratio of the signals; computing the two-order, four-order and six-order cumulants of the signals obtained after stochastic resonance preprocessing by using a statistic mode identification method so as to obtain characteristic vectors; and training a support vector machine by using the characteristic vectors, and identifying the modulation modes of the digital signals by using the trained support vector machine. The method can improve the successful rate of the identification of the modification modes of the digital signals effectively and particularly can ensure accurate identification with a low signal-to-noise ratio. The method can be used for improving the performance of a receiving terminal of a communication system.

Description

technical field [0001] The invention belongs to the technical field of communication, and relates to a digital signal modulation identification method, which is suitable for wireless communication channels with extremely low signal-to-noise ratios. Background technique [0002] The purpose of communication is to transmit information quickly, effectively, safely and accurately through channels. In order to make full use of the channel, the communication signals transmitted in space are modulated before transmission. In the current complex signal environment, the modulation methods adopted by the signal are also various. For the received signal, if you want to correctly demodulate, analyze the received signal, or interfere, you must be able to correctly identify the modulation mode of the signal, and then take the corresponding demodulation method. To demodulate the information content of the intercepted communication signal, the modulation method of the signal must be known...

AI Technical Summary

Problems solved by technology

[0010] The performance of the decision theory recognition method is better, but the calculation is complex and the actual adaptability is poor

For example, for a simple signal form, the complete mathematical expression of the optimal classifier is very complicated. It also needs to construct a correct assumption and analyze it carefully to judge an appropriate threshold, which is very difficult.

Moreover, once the assumption does not match the actual situation, the correct recognition rate will drop dramatically; compared with the judgment theory recognition method, the statistical pattern recognition method has simple theoretical analysis and strong adaptability of the extracted features, which can be used for the recognition of various types of modulation signals. But it is susceptible to noise interference, and the recognition effect is poor in the case of low signal-to-noise ratio

Images