Coding and modulation joint identification method based on one-dimensional deep residual lightweight network

Abstract

The invention discloses a coding and modulation joint recognition method based on a one-dimensional deep residual light network, and its realization steps are: (1) generating 29 kinds of coding and modulation joint signals and 2 kinds of modulation signals; (2) generating training sample sets and Test sample set; (3) Construct a one-dimensional deep residual light weight network model; (4) Train a one-dimensional deep residual light weight network model; (5) Input the test sample set into the trained one-dimensional deep residual light weight network model; Test in the mass network model to obtain the recognition accuracy and evaluate the network performance. The invention is a general radio signal feature extraction method, which has the advantages of good universality, strong robustness, high recognition accuracy, few network parameters, and many types of recognition signals, and can be used for radio signals in actual complex communication environments The joint identification of coding and modulation methods.

Description

technical field [0001] The present invention belongs to the technical field of communication, and further relates to a coding and modulation joint identification method based on a one-dimensional deep residual lightweight network in the technical field of radio signal processing. The present invention is applicable to complex electromagnetic environments, automatically extracts radio signal features through the constructed one-dimensional deep residual lightweight network, and uses the extracted radio signal features to identify radio signal types of different modulation modes and different channel coding modes. Background technique [0002] Radio signal identification plays an important role in both military and civilian applications. In the early days, the number of radio signal sources was small, the system was single, the function was simple, and the frequency domain coverage was small. Artificial feature extraction using expert prior knowledge can complete radio signal i...

AI Technical Summary

Problems solved by technology

Although this method proposes a communication signal modulation recognition method based on generalized S transform, the method still has the disadvantage that the process of extracting signal features is cumbersome when performing generalized S transform, and the energy image of the signal needs to be compared repeatedly In order to complete the identification of the signal, it relies too much on the prior knowledge of experts, and is only suitable for the identification of the modulation mode of the signal.

However, the disadvantage of this method is that it does not make full use of the characteristics of the deeper network that the residual network can achieve, and only constructs an artificial neural network containing two layers of perceptrons, while discarding the original residual network. At the same time of the convolution operation, it also discards the advantages of the convolution operation in extracting data features.

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