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

Zeliang AN; Tianqi ZHANG; Baoze MA; Pan DENG; Yuqing XU

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

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

Papers | 更新时间：2024-06-05

- Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal
- Journal on Communications Vol. 42, Issue 7, Pages: 84-94(2021)
- 作者机构：
  
  1. 重庆邮电大学通信与信息工程学院，重庆 400065
  2. 重庆邮电大学计算机科学与技术学院，重庆 400065
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61671095);The National Natural Science Foundation of China(61702065);The National Natural Science Foundation of China(61701067)
- DOI：10.11959/j.issn.1000-436x.2021142
  CLC： TN911.7
- Online First：2021-07，
  
  Published：25 July 2021
- 稿件说明：
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Zeliang AN, Tianqi ZHANG, Baoze MA, et al. Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal[J]. Journal on Communications, 2021, 42(7): 84-94.
DOI：

Zeliang AN, Tianqi ZHANG, Baoze MA, et al. Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal[J]. Journal on Communications, 2021, 42(7): 84-94. DOI： 10.11959/j.issn.1000-436x.2021142.

摘要

为识别多入多出正交空时分组码（MIMO-OSTBC）系统所采用的调制样式，提出了一种基于一维卷积神经网络（1D-CNN）的协作调制识别算法。首先，采用迫零盲均衡来提升不同调制信号间区分度，并选用天然无损的同相正交（I/Q）信号作为浅层特征；然后，设计并训练基于 1D-CNN 的识别模型，从浅层特征中提取深层特征；最后，采用投票决策和置信度决策融合策略，提升多天线接收端协作识别精度。实验结果表明，所提算法能有效识别{BPSK

4PSK

8PSK

16QAM

4PAM}5种调制方式，当信噪比大于或等于-2 dB时，识别精度可达100%。

Abstract

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.

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references

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