Automatic Modulation Recognition of Digital Communication Signals

2010 ◽  
Author(s):  
Wu Juan-ping ◽  
Han Ying-zheng ◽  
Zhang Jin-mei ◽  
Wang Hua-kui
Keyword(s):  
Digital Communication ◽  
Modulation Recognition ◽  
Communication Signals ◽  
Automatic Modulation Recognition

scholarly journals Research on signal modulation based on machine learning intelligent algorithm and computer automatic identification

2021 ◽  
Vol 2083 (4) ◽  
pp. 042092
Author(s):  
Zixi Li
Keyword(s):  
Neural Network ◽  
Signal To Noise Ratio ◽  
Feature Representation ◽  
Automatic Identification ◽  
Signal Recognition ◽  
Modulation Recognition ◽  
Data Set ◽  
Communication Signals ◽  
Communication Signal ◽  
Automatic Modulation Recognition

Abstract In the process of communication, modulation signal recognition and classification are an important part of non-cooperative communication. Automatic modulation recognition technology of communication signals based on feature extraction and pattern recognition is a key research object in the radio field. The use of neural network can achieve automatic recognition of a variety of modulation signals and achieve good results. In this method, the received signal is preprocessed to obtain the complex baseband signal including in-phase component and orthogonal component. As the data set of the input convolution neural network model, the signal further optimizes the traditional method of manual extraction of expert features for communication signal recognition, which has great limitations and low accuracy under low signal-to-noise ratio, and the simulation results are verified. The results show that the proposed method has stronger feature representation ability and competitiveness in automatic modulation recognition, and is helpful to promote the application of deep learning in the field of automatic modulation recognition.

Automatic Recognition of M-Nary Digital Modulation Signals

2013 ◽  
Vol 336-338 ◽  
pp. 1665-1669 ◽  
Author(s):  
Li Rong
Keyword(s):  
Success Rate ◽  
Simple Structure ◽  
Automatic Recognition ◽  
Modulation Recognition ◽  
Communication Signals ◽  
Automatic Modulation Recognition ◽  
Modulation Signal ◽  
Digital Modulations ◽  
Key Features ◽  
Simulation Results

For the using of multi-modulation, the precondition of receiving and demodulating signal is to decide the type of the modulation. So automatic recognition of modulation signal has significant influences on the analysis of communication signals. In this paper, nine types of M-nary digital modulations are recognized by using four effective key features and utilizing the decision-theoretic approachThe simulation results shows that overall success rate is over 99% at SNR4dBThis algorithm is verified its good performance. It has simple structure, less calculation and good performance of real time. It can realize automatic modulation recognition.

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