Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal

被引：0

作者：

An Z. ^{[1
]}

Zhang T. ^{[1
]}

Ma B. ^{[1
]}

Deng P. ^{[1
]}

Xu Y. ^{[2
]}

机构：

[1] School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing

[2] School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing

来源：

Tongxin Xuebao/Journal on Communications | 2021年 / 42卷 / 07期

基金：

中国国家自然科学基金;

关键词：

1D-CNN; Decision fusion; MIMO-OSTBC; Modulation recognition; Zero-forcing blind equalization;

D O I：

10.11959/j.issn.1000-436x.2021142

中图分类号：

学科分类号：

摘要：

To recognize the modulation style adopted in multiple-input-multiple-output orthogonal space-time block code (MIMO-OSTBC) systems, a cooperative modulation recognition algorithm based on the one-dimensional convolutional neural network (1D-CNN) was proposed. With the lossless I/Q signal selected as shallow features, the zero-forcing blind equalization was first leveraged to improve the discrimination of different modulation signals. Then the 1D-CNN recognition model was devised and trained to extract deep features from shallow ones. Later, two decision fusion strategies of voting-based and confidence-based were leveraged in the multiple-antenna receiver to improve recognition accuracy. Experimental results show that the proposed algorithm can effectively recognize five modulation types {BPSK, 4PSK,8PSK,16QAM,4PAM}, with a 100% recognition accuracy when the signal-to-noise is equal or greater than -2 dB. © 2021, Editorial Board of Journal on Communications. All right reserved.

引用

页码：84 / 94

页数：10

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