基于一维CNN的多入多出OSTBC信号协作调制识别

安泽亮; 张天骐; 马宝泽; 邓盼; 徐雨晴

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

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基于一维CNN的多入多出OSTBC信号协作调制识别

学术论文 | 更新时间：2024-06-05

- 基于一维CNN的多入多出OSTBC信号协作调制识别
- Cooperative modulation recognition based on one-dimensional convolutional neural network for MIMO-OSTBC signal
- 通信学报 2021年42卷第7期页码：84-94
- 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：
  
  [ "安泽亮（1993− ），男，安徽蚌埠人，重庆邮电大学博士生，主要研究方向为调制识别、神经网络" ]
  [ "张天骐（1971− ），男，四川眉山人，博士，重庆邮电大学教授、博士生导师，主要研究方向为神经网络、盲信号处理" ]
  [ "马宝泽（1990− ），男，河北廊坊人，重庆邮电大学博士生，主要研究方向为盲源分离改进" ]
  [ "邓盼（1990− ），男，四川宜宾人，重庆邮电大学博士生，主要研究方向为信号与信息处理、图像处理" ]
  [ "徐雨晴（1990− ），女，安徽宿州人，重庆邮电大学博士生，主要研究方向为数据分析、人工智能" ]
- 基金信息：
  
  国家自然科学基金资助项目(61671095);国家自然科学基金资助项目(61702065);国家自然科学基金资助项目(61701067)
- DOI：10.11959/j.issn.1000-436x.2021142
  中图分类号： TN911.7
- 网络出版日期：2021-07，
  
  纸质出版日期：2021-07-25
- 稿件说明：
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安泽亮, 张天骐, 马宝泽, 等. 基于一维CNN的多入多出OSTBC信号协作调制识别[J]. 通信学报, 2021,42(7):84-94.

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.
安泽亮, 张天骐, 马宝泽, 等. 基于一维CNN的多入多出OSTBC信号协作调制识别[J]. 通信学报, 2021,42(7):84-94. DOI： 10.11959/j.issn.1000-436x.2021142.

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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