Robust communication signal modulation and recognition method

Abstract

The invention discloses a robust communication signal modulation and recognition method and relates to communication signal modulation and recognition methods. The method aims at solving the problem that in order to guarantee validity within a large SNR range, a plurality of recognizers need to be trained through a traditional ARM algorithm, and in other words, the recognizers need to be trained individually for different SNR environments at a training stage and accordingly the workload is huge. The method includes the steps that Wigner-Ville conversion is performed on a communication signal sample s(t) to obtain WVD of the s(t); second-order three-dimensional autocorrelation characteristics are extracted, and a second-order three-dimensional autocorrelation characteristic set is established; selection is performed on the second-order three-dimensional autocorrelation characteristics to form a robust feature set; a first-class support vector unit is established through training, and an output function Yi(x) of the first-class support vector unit is calculated; the possibilities that a communication signal sample to be recognized sx(t) belongs to the various modulation modes included in the communication signal sample s(t) are calculated, and a modulation category with the largest possibility is selected as the final modulation and recognition result. The method is suitable for communication signal modulation and recognition.

Description

technical field [0001] The invention relates to a communication signal modulation identification method. Background technique [0002] With the development of software radio and cognitive radio technology, the research on automatic modulation recognition (Automation Modulation Recognition, AMR) of multi-system communication signals based on feature extraction and pattern recognition has made a lot of progress and achievements, but it still cannot meet the needs of communication signals. There are still many challenges for the practical application of modulation recognition. In particular, the poor promotion ability of AMR has always been an important bottleneck hindering its practical application. In order to solve the problem of poor generalization ability of AMR method and the need for real-time signal-to-noise ratio estimation, this project studies the multi-system communication signal modulation recognition mechanism and method with generalization ability from two aspec...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the traditional AMR algorithm needs to train multiple recognizers to ensure the effectiveness in a larger SNR range, that is, in the training stage, the recognizers need to be trained separately for different SNR environments, resulting in a huge workload The problem

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