SoC implementation of a modulation classification module for cognitive radios

2016 ◽  
Author(s):  
K A Arun Kumar
Keyword(s):  
Cognitive Radios ◽  
Modulation Classification

Evaluation of Machine Learning-Driven Automatic Modulation Classifiers Under Various Signal Models

2020 ◽  
Author(s):  
Pejman Ghasemzadeh ◽  
Subharthi Banerjee ◽  
Michael Hempel ◽  
Hamid Sharif ◽  
Tarek Omar
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Communication Systems ◽  
Short Term Memory ◽  
Cognitive Radios ◽  
Wireless Channel ◽  
Support Vector ◽  
Modulation Scheme ◽  
Modulation Classification ◽  
Non Linear

Abstract Automatic Modulation Classification (AMC) is becoming an essential component in receiver designs for next-generation communication systems, such as Cognitive Radios (CR). AMC enables receivers to classify an intercepted signal’s modulation scheme without any prior information about the signal. This is becoming increasingly vital due to the combination of congested frequency bands and geographically disparate frequency licensing for the railroad industry across North America. Thus, a radio technology is needed that allows train systems to adapt automatically and intelligently to changing locations and corresponding RF environment fluctuations. Three AMC approaches have been proposed in the scientific literature. The performance of these approaches depends especially on the particular environment where the classifiers are employed. In this work, the authors present a performance evaluation of the Feature-based AMC approach, as this is the most promising approach for railroads in real-time AMC operations under various different wireless channel environments. This study is done as the first one for railroads application where it considers different environments models including Non-Gaussian Class A noise, Multipath fast fading, and their combination. The evaluation is conducted for signals using a series of QAM modulation schemes. The authors selected the signal’s Cumulant statistical features for the feature extraction stage in this study, coupled with three different machine learning classifiers: Support Vector Machine (SVM), Deep Neural Network (DNN) and Recurrent Neural Network (RNN) utilizing long-short term memory (LSTM), in order to maintain control over the classifiers’ accuracy and computational complexity, especially for the non-linear cases. Our results indicate that when the signal model noise shows higher non-linear behavior, the RNN classifier on average achieves higher classification accuracy than the other classifiers.

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