Communication Signal Modulation Identification Method Based on Evolutionary BP Neural Network

Abstract

The invention provides a communication signal modulation mode identification method based on evolutionary BP neural network, which performs preprocessing and feature extraction on acquired communication signals with known different modulation modes, and uses the extraction result as an input feature parameter of the neural network. Using the cat group evolution mechanism of the compound search mode to optimize the initial weight and threshold of the BP neural network with the recognition rate as the objective function, the optimal parameters are obtained as the initial parameters of the neural network for subsequent recognition, and then the input characteristic parameters and the optimal The initial parameters train the BP neural network to obtain the BP neural network with optimal system parameters. The communication signal of unknown modulation mode is obtained, and the BP neural network with optimal system parameters is used to identify the communication signal of unknown modulation mode to obtain the recognition result. Compared with the traditional BP neural network for modulation recognition, this method has a higher recognition rate under the same signal-to-noise ratio, and avoids falling into the local optimal solution during the training process 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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