Automatic Digital Modulation Recognition Based on Novel Features and Support Vector Machine

被引：14

作者：

Hassanpour, Salman ^{[1
]}

Pezeshk, Amir Mansour ^{[1
]}

Behnia, Fereidoon ^{[1
]}

机构：

[1] Sharif Univ Technol, Dept Elect Engn, Tehran, Iran

来源：

2016 12TH INTERNATIONAL CONFERENCE ON SIGNAL-IMAGE TECHNOLOGY & INTERNET-BASED SYSTEMS (SITIS) | 2016年

关键词：

Automatic Modulation Recognition (AMR); Robust Features; Low SNRs; Pattern Recognition; Support Vector Machine (SVM); IDENTIFICATION; CLASSIFICATION;

D O I：

10.1109/SITIS.2016.35

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper a novel algorithm for automatic modulation recognition (AMR) based on pattern recognition approach is proposed. The main focus here remains on feature extraction block and the novel features are introduced in order to identify digital modulation schemes. The modulation types include: BASK, BFSK, BPSK, 4-ASK, 4-FSK, QPSK, and 16-QAM and the channel model is considered as an AWGN channel. The features are extracted from the received signal that is considered in the time, frequency and wavelet domains. Also, to overcome the multiclass problem, a hierarchical structure is investigated based on binary support vector machine (SVM). The simulations demonstrate superior capabilities of the proposed features in accurately separating digitally modulated signals in an extremely noisy environment with very low SNR values. Accordingly, the minimum SNR for the perfect identification is proven to be -5 dB, and a final accuracy percentage of 98.15 has been obtained in -10 dB.

引用

页码：172 / 177

页数：6

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