Communication signal modulation mode identification method based on evolved BP neural network

Abstract

The invention provides a communication signal modulation mode identification method based on an evolution BP neural network, and the method comprises the steps: carrying out the preprocessing and feature extraction of obtained communication signals with different known modulation modes, and enabling an extraction result to serve as an input feature parameter of the neural network; optimizing the initial weight value and threshold value of the BP neural network by using a cat swarm evolution mechanism of a composite search mode and using the recognition rate as an objective function; and obtaining an optimal parameter as an initial parameter of the neural network for later recognition, and then training the BP neural network by using the input characteristic parameter and the optimal initial parameter to obtain the BP neural network with the optimal system parameter; acquiring a communication signal with an unknown modulation mode, and identifying the communication signal with the unknown modulation mode by using the BP neural network with the optimal system parameter to obtain an identification result. Compared with a traditional BP neural network for modulation recognition, the method has higher recognition rate under the same signal-to-noise ratio, and the situation that a local optimal solution is caught in the training process is avoided as much as possible.

Description

technical field [0001] The invention relates to a communication signal modulation mode identification method based on an evolutionary BP neural network, which belongs to the field of communication signal processing. Background technique [0002] Modulation recognition is a prerequisite for obtaining the information content of communication signals. Modulation recognition technology is a hot topic in the field of signal processing in recent years, and has broad application prospects in radio spectrum resource monitoring and management, electronic reconnaissance, and interference identification. With the rapid development of communication technology, the system and modulation patterns of communication signals have become more complex and diverse, which makes it impossible for conventional identification methods and theories to effectively identify communication signals, which also puts forward higher challenges for the identification research of communication signals. Require...

AI Technical Summary

Problems solved by technology

Compared with the traditional method, the optimized neural network can obtain better network parameters and training results, and more effectively solve the problem that the BP neural network is difficult to obtain the optimal system parameters, and obtain a higher recognition rate of communication signal modulation

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