Communication Signal Modulation Recognition Method of Evolutionary BP Neural Network of Quantum Elephant Group Mechanism

Abstract

The present invention provides a communication signal modulation recognition method for evolution of BP neural network by quantum elephant group mechanism, and designs a weighted Myriad filter combined with the data set of designed characteristic parameters, and then utilizes quantum elephant group mechanism to evolve BP neural network to obtain neural network The optimal system parameters of , using the BP neural network with optimal weights and thresholds as a classifier to efficiently identify the modulation mode of communication signals under the background of impact noise. The designed method can obtain the optimal network parameters and classification recognition effect in the impact noise environment, so as to obtain a higher recognition rate in the harsh environment such as impact noise and low mixed signal-to-noise ratio, and break through the application of the existing neural network modulation recognition limit.

Description

technical field [0001] The invention relates to a communication signal modulation mode identification method based on a quantum elephant group mechanism evolution BP neural network under impact noise, and belongs to the field of communication signal processing. Background technique [0002] Modulation identification technology is a very critical technology in the field of wireless communication. The modulation method of identifying wireless communication signals is the basic technology in the fields of electronic countermeasures, electronic reconnaissance, non-cooperative communication, smart antennas and wireless spectrum management. Or the civilian field has a very wide range of applications and very important value. In recent years, with the rapid development of wireless communication technology, electronic technology, signal processing and other technologies, the modulation methods of wireless communication signals have become more and more complex, and the types of modu...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that the performance of the existing communication signal modulation recognition method deteriorates seriously or even fails in the environment of impact noise or strong shock noise, and it is difficult to determine the optimal parameters of the BP neural network used as a modulation recognition classifier. The weighted Myriad filter combines the data set of the designed characteristic parameters, and then uses the quantum elephant swarm mechanism to evolve the BP neural network, obtains the optimal system parameters of the neural network, and uses the BP neural network with the optimal weight and threshold as the classifier pair Efficient identification of communication signal modulation modes in the background of impact noise

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