基于残差密集网络的频谱感知方法

盖建新; 薛宪峰; 南瑞祥; 吴静谊

doi:10.11959/j.issn.1000-436x.2021220

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基于残差密集网络的频谱感知方法

学术论文 | 更新时间：2024-06-05

- 基于残差密集网络的频谱感知方法
- Spectrum sensing method based on residual dense network
- 通信学报 2021年42卷第12期页码：182-191
- 作者机构：
  
  哈尔滨理工大学测控技术与仪器黑龙江省高校重点实验室，黑龙江哈尔滨 150080
- 作者简介：
  
  [ "盖建新（1980- ），男，辽宁朝阳人，博士，哈尔滨理工大学副教授，主要研究方向为频谱感知、机器学习、亚奈奎斯特采样理论、压缩感知等" ]
  [ "薛宪峰（1996- ），男，山东菏泽人，哈尔滨理工大学硕士生，主要研究方向为深度学习" ]
  [ "南瑞祥（1996- ），男，山东济宁人，哈尔滨理工大学硕士生，主要研究方向为通信信号处理" ]
  [ "吴静谊（1996- ），女，黑龙江绥化人，哈尔滨理工大学硕士生，主要研究方向为压缩感知" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501150);黑龙江省自然科学基金资助项目(QC2014C074);黑龙江省省属本科高校基本科研业务费基金资助项目(2018-KYYWF-1656)
- DOI：10.11959/j.issn.1000-436x.2021220
  中图分类号： TN911
- 网络首发：2021-12，
  
  纸质出版：2021-12-25
- 稿件说明：
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盖建新, 薛宪峰, 南瑞祥, 等. 基于残差密集网络的频谱感知方法[J]. 通信学报, 2021,42(12):182-191.

Jianxin GAI, Xianfeng XUE, Ruixiang NAN, et al. Spectrum sensing method based on residual dense network[J]. Journal on Communications, 2021, 42(12): 182-191.
盖建新, 薛宪峰, 南瑞祥, 等. 基于残差密集网络的频谱感知方法[J]. 通信学报, 2021,42(12):182-191. DOI： 10.11959/j.issn.1000-436x.2021220.

Jianxin GAI, Xianfeng XUE, Ruixiang NAN, et al. Spectrum sensing method based on residual dense network[J]. Journal on Communications, 2021, 42(12): 182-191. DOI： 10.11959/j.issn.1000-436x.2021220.

摘要

针对传统卷积神经网络（CNN）频谱感知方法没有充分利用特征图信息并且提取特征图的能力受限于浅层的网络结构等问题，通过在传统 CNN 频谱感知方法中添加密集连接，实现特征图信息重利用，同时在密集单元的两端加入捷径连接，实现更深层的网络训练，进而提出一种基于残差密集网络（ResDenNet）的频谱感知方法。该方法将频谱感知问题映射为图像二分类问题，首先对接收信号分割成矩阵并归一化灰度处理，得到的灰度图像作为网络的输入，然后通过密集学习和残差学习训练网络，最后将在线数据输入ResDenNet中，完成基于图像分类的频谱感知。数值实验表明，所提方法优于传统频谱感知方法，在信噪比低至-19 dB时，所提方法检测概率仍高达0.96，虚警概率低至0.1，同时具有更好的泛化能力。

Abstract

Aiming at the problem that the traditional spectrum sensing method based on convolutional neural network（CNN） did not make full use of image feature and the ability of extracting the image feature was limited by the shallow network structure， a spectrum sensing method based on the residual dense network （ResDenNet） was proposed.By adding dense connections in the traditional neural network， the information reuse of the image feature was achieved.Meanwhile， shortcut connections were employed at both ends of the dense unit to implement deeper network training.The spectrum sensing problem was transformed into the image binary classification problem.Firstly， the received signals were integrated into a matrix， which was normalized and transformed by gray level.The obtained gray level images were used as the input of the network.Then， the network was trained through dense learning and residual learning.Finally， the online data was input into the ResDenNet and spectrum sensing was implemented based on image classification.The numerical experiments show that the proposed method is superior to the traditional ones in terms of performance.When the SNR is as low as -19 dB， the detection probability of the proposed method is still high up to 0.96 with a low false alarm probability of 0.1， while a better generalization ability is displayed.

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