Automatic Modulation Classification of Digital Communication Signals Using SVM Based on Hybrid Features, Cyclostationary, and Information Entropy

Abstract

Since digital communication signals are widely used in radio and underwater acoustic systems, the modulation classification of these signals has become increasingly significant in various military and civilian applications. However, due to the adverse channel transmission characteristics and low signal to noise ratio (SNR), the modulation classification of communication signals is extremely challenging. In this paper, a novel method for automatic modulation classification of digital communication signals using a support vector machine (SVM) based on hybrid features, cyclostationary, and information entropy is proposed. In this proposed method, by combining the theory of the cyclostationary and entropy, based on the existing signal features, we propose three other new features to assist the classification of digital communication signals, which are the maximum value of the normalized cyclic spectrum when the cyclic frequency is not zero, the Shannon entropy of the cyclic spectrum, and Renyi entropy of the cyclic spectrum respectively. Because these new features do not require any prior information and have a strong anti-noise ability, they are very suitable for the identification of communication signals. Finally, a one against one SVM is designed as a classifier. Simulation results show that the proposed method outperforms the existing methods in terms of classification performance and noise tolerance.

Ensemble of Deep Learning Enabled Modulation Signal

“…Lee-Leon et al [9] proposed a receiver technique by exploring the ML-DBN method -to combat the signal distortion created by the multi-path propagation and Doppler effect. Firstly, the received signals are segmented into frames beforehand this frame is preprocessed individually by a pixelization method.…”

“…It could accommodate the variable-length signal data for matching the fixed-length input requested from the shared NN, as well as it has the capacity to appropriately handle the zero data from the signal order for improving the resultant loss. Wei et al [9] proposed a technique for automated modulation classifier of digital transmission signals utilizing SVM based hybrid feature, cyclostationary, and data entropy. During the presented approach, with integrating the concept of entropy and cyclostationary dependent upon the current signal feature, it can be present 3 novel features for supporting the classifier of digital transmission signals.…”

AMC2-Pyramid: Intelligent Pyramidal Feature Engineering and Multi-Distance Decision Making for Automatic Multi-Carrier Modulation Classification

“…Each approach is dedicated to different works, and the final classification is performed by CNN. However, the CNN has a high signal loss in the max-pooling layer, which reduces the accuracy of AMC [13,14]. However, the communication channel may be corrupted by additive noise, which cannot be handled by many AMC works, and the loworder features are not suitable for classifying higher-order modulation schemes [15][16][17].…”

Rényi Entropy-Based Spectrum Sensing in Mobile Cognitive Radio Networks Using Software Defined Radio

“…For instance, Rényi Entropy has been used to distinguish different modulations [ 25 ], which is closer to a real MCRN. Compared with Shannon entropy, Rényi entropy can better reflect the difference between two distributions [ 26 ].…”