Automatic modulation classification of digital modulation signals with stacked autoencoders

2017 ◽  
Vol 71 ◽  
pp. 108-116 ◽  
Author(s):  
Afan Ali ◽  
Fan Yangyu ◽  
Shu Liu
Keyword(s):  
Modulation Classification ◽  
Digital Modulation ◽  
Automatic Modulation Classification ◽  
Stacked Autoencoders

Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants

2017 ◽  
Vol 66 (7) ◽  
pp. 6089-6101 ◽  
Author(s):  
Sai Huang ◽  
Yuanyuan Yao ◽  
Zhiqing Wei ◽  
Zhiyong Feng ◽  
Ping Zhang
Keyword(s):  
Modulation Classification ◽  
Automatic Modulation Classification

scholarly journals Performance Analysis of Automatic Modulation Classification using Time Frequency Transforms under Non-Ideal Channel Conditions

2019 ◽  
Vol 8 (12) ◽  
pp. 1685-1691
Keyword(s):  
Performance Analysis ◽  
Fading Channels ◽  
Classification Accuracy ◽  
Modulation Classification ◽  
Time Frequency ◽  
Automatic Modulation Classification ◽  
Performance Metric ◽  
S Transform ◽  
Channel Conditions

Classification of different analog and digital modulation classes using Time-Frequency Transforms (TFTs) through MST and MFSWT under ideal channel conditions is presented in this paper. It also deals with performance analysis of proposed Modified S- Transform (MST) and Modified Frequency Slice Wavelet Transform (MFSWT) based Automatic Modulation Classification (AMC) methods under different channel conditions such as Gaussian and fading channels. The performance of the proposed TFT based AMC methods under AWGN (with SNR values varied from -10 dB to 20 dB) and fading channels is examined through simulation. Moreover, the performance of the proposed TFT based AMC is compared with that of the existing techniques in terms of performance metric namely classification accuracy which is also discussed in this paper.

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