基于TCN的USRP调制信号识别算法
作者:
作者单位:

1.中北大学信息与通信工程学院,太原 030051;2.中北大学电气与控制工程学院,太原 030051

作者简介:

通讯作者:

基金项目:

山西省基础研究计划(20210302123062)。


A TCN-Based Modulation Signal Recognition Algorithm for USRP
Author:
Affiliation:

1.School of Information and Communication Engineering, North University of China, Taiyuan 030051, China;2.School of Electrical and Control Engineering, North University of China, Taiyuan 030051, China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    针对自动调制识别(Automatic modulation recognition,AMR)中未充分利用原始信号时序信息、识别率低的问题,提出基于频域降噪和时序卷积网络(Temporal convolutional network,TCN)的信号模式识别算法。实验使用标准数据集RML2016.10a,引入频域去噪模块(Frequency domain denoising module,FDDM)有效抑制环境噪声,将信号的I/Q分量转换为A/P分量,并进行向量归一化处理提升稳定性。最后,将预处理后的信号输入TCN网络进行分类识别。结果表明,该算法在处理复杂调制方式时(如16 QAM和64 QAM),平均识别率高于循环门控单元(Gated recurrent unit,GRU)、长短期记忆网络(Long short-term memory,LSTM)、经济高效的卷积神经网络(Memory-cost-efficient convolutional neural network,MCNet)、经济高效的混合神经网络(Cost-efficient hybrid deep learning network,CGDNet)和去噪自动编码器(Denoising auto-encoder,DAE)等模型。此外,通过通用软件无线电外设(Universal software radio peripheral,USRP)采集的实际I/Q数据验证,该算法在加性高斯白噪声(Additive white Gaussian noise,AWGN)信道下表现出良好的鲁棒性和应用潜力。

    Abstract:

    To address the low recognition rates due to the insufficient utilization of original signal timing information in automatic modulation recognition (AMR), this paper proposes a signal pattern recognition algorithm based on frequency domain denoising and temporal convolutional networks (TCN). Experiments are conducted using the standard dataset RML2016.10a, and a frequency domain denoising module (FDDM) is introduced to effectively suppress environmental noise. The I/Q components of the signal are converted into A/P components, followed by vector normalization to enhance stability. Finally, the preprocessed signals are fed into the TCN network for classification recognition. Results indicate that this algorithm achieves an average recognition rate higher than those of models such as gated recurrent unit (GRU), convolutional long short-term memory (LSTM), memory-cost-efficient convolutional neural network (MCNet), cost-efficient hybrid deep learning network (CGDNet), and denoising auto-encoder (DAE) when processing complex modulation schemes like 16 QAM and 64 QAM. Additionally, validation using actual I/Q data collected through the universal software radio peripheral (USRP) demonstrates that the algorithm exhibits good robustness and application potential under additive white Gaussian noise (AWGN) channels.

    参考文献
    相似文献
    引证文献
引用本文

杨宵,姚爱琴,石喜玲.基于TCN的USRP调制信号识别算法[J].数据采集与处理,2025,40(6):1527-1537

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
历史
  • 收稿日期:2024-07-24
  • 最后修改日期:2024-11-09
  • 录用日期:
  • 在线发布日期: 2025-12-10