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.

Digital Modulation Recognition in Different Environments

2014 ◽  
Vol 1049-1050 ◽  
pp. 2084-2087 ◽  
Author(s):  
Rong Li
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Automatic Recognition ◽  
Modulation Recognition ◽  
Signal To Noise ◽  
Digital Modulation ◽  
Multipath Interference ◽  
Modulation Signal ◽  
Simulation Results ◽  
Recognition Success

For the using of multi-modulation, the precondition of receiving and demodulating signal is to determine the type of the modulation, so automatic recognition of modulation signal has significant influence on the analysis of the signals. In this paper, digital modulation recognition is studied respectively in different environment of White Gaussian Noise (WGN), stationary interference and multipath interference. The simulation results show that the recognition success rate is the highest in stationary interference environment and the lowest in multipath interference environment with the same signal to noise ratio (SNR).

scholarly journals An Algorithm for Automatic Recogniton of Digital QAM Modulations

2019 ◽  
Vol 25 (3) ◽  
pp. 36-41
Author(s):  
Alexandru-Daniel Luţă ◽  
Paul Bechet
Keyword(s):  
Lower Order ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Automatic Recognition ◽  
Signal To Noise ◽  
Small Influence ◽  
Qam Modulation ◽  
Digital Modulations ◽  
Simulation Results ◽  
Noise Ratio

Abstract This paper proposes a new Matlab-developed algorithm for automatic recognition of digital modulations using the constellation of states. Using this technique the automatic distinction between four digital modulation schemes (8-QAM, 16-QAM, 32-QAM and 64-QAM) was made. It has been seen that the efficiency of the algorithm is influenced by the type of modulation, the value of the signal-to-noise ratio and the number of samples. In the case of an AWGN noise channel the simulation results indicated that the value of SNR (signal-to-noise ratio) has a small influence on the recognition rate for lower-order QAM (8-QAM and 16-QAM). The length of the signal may change essentially the recognition rate of this algorithm especially for modulations with a high number of bits per symbol. Consequently, for the 64-QAM modulation in a case of 25dB signal-to-noise ratio the recognition rate is doubled if the sample rate is incresed from 5400 to 80640.

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.

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