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Teimouri, M., Rezaei, M. (2019). Blind Classification of Space-Time Codes Using Machine Learning. Electronic and Cyber Defense, 10(1), 1-10.
Mehdi Teimouri; Masoud Rezaei. "Blind Classification of Space-Time Codes Using Machine Learning". Electronic and Cyber Defense, 10, 1, 2019, 1-10.
Teimouri, M., Rezaei, M. (2019). 'Blind Classification of Space-Time Codes Using Machine Learning', Electronic and Cyber Defense, 10(1), pp. 1-10.
Teimouri, M., Rezaei, M. Blind Classification of Space-Time Codes Using Machine Learning. Electronic and Cyber Defense, 2019; 10(1): 1-10.

Blind Classification of Space-Time Codes Using Machine Learning

علوم و فناوریهای پدافند نوین
Article 1, Volume 10, Issue 1 - Serial Number 35, April 2019, Pages 1-10    PDF (971.18 K)
Authors
Mehdi Teimouri* ; Masoud Rezaei
Receive Date: 30 January 2019,  Revise Date: 14 December 2019,  Accept Date: 30 January 2019 
Abstract
For eavesdropping of modern telecommunications systems, which employ multiple transmit and receive antennas, the first step is to recognize the space-time code. In this paper, we propose a method to classify within broad categories of space-time codes. The method is based on machine learning and pattern recognition. Compared to the previous published researches, the novelty of this paper is including quasi-orthogonal space-time codes and also single antenna transmitter in identification set. Moreover, the method described in this paper outperforms the current classification methods. It is shown that the decision tree classifier based on several features extracted from correlation matrices, has practical advantages over the current classification methods, especially when data is very noisy.
Keywords
Blind Identification; space-time codes; correlation matrix; Decision Tree
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