| 杨小洪,余春泉.基于波形优化的深度调制识别方法[J].电子测量与仪器学报,2020,34(10):117-124 |
| 基于波形优化的深度调制识别方法 |
| Waveform optimization based automatic modulation recognition |
| |
| DOI： |
| 中文关键词: 调制识别 深度学习 波形优化 |
| 英文关键词:modulation recognition deep learning waveform optimization |
| 基金项目:2018 年江西省体育局体育科研项目(2018032)资助 |
|
|
|
|
| 摘要点击次数: 100 |
| 全文下载次数: 1184 |
| 中文摘要: |
| 自动调制识别技术,可在接收信号在样式未知、内容未知的前提条件下,自动对其调制方式进行估计。 提出一种基于深
度学习的调制识别方法,该方法通过波形优化的方法迭代更新滤波器参数,对接收信号样本进行滤波,再通过深度识别网络进
行识别。 该方法在处理流程中存在识别结果对滤波器参数的反馈回路,能够减轻信道对调制识别结果的不利影响。 通过开源
数据集进行验证,证明了该方法相比于几种利用经典深度学习网络的方法,识别率均有所提高。 特别是相比于传统的 CNN 方
法,识别率提高了约 7%。 |
| 英文摘要: |
| Automatic modulation recognition (AMR) can automatically estimate the modulation type under the condition that the signal is
unknown at all. A deep learning based AMR is proposed. The proposed method can update the filter taps through waveform optimization,
which can filter the signal samples in order to overcome the unfavorable effects of the transmission channels. In the proposed method, a
feedback path exists between the recognition network and the inverse-channel filter. According to the experiments from an open-source
dataset, the proposed feedback-structured method can increase the recognition rate compared with the traditional deep learning methods.
Specially, compared with the CNN based method, the recognition rate has increased by about 7%. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
|
|
|
