返回
Wireless signal modulation identification method based on RF I/Q data distribution
DOI:10.1038/s41598-021-00723-7.png)
摘要
En 中文
Electromagnetic spectrum detection is the basis of the next generation wireless communication technology. Wireless signal identification is an important part of electromagnetic spectrum detection and management activities. This paper proposes to extract the distribution features of different modulated signals from the signal I/Q data. A two-dimensional gradient matrix is used to describe the characteristics of the signal classification. The minimum gradient cumulative distance (GCD) estimate between the sample and the model is used as the decision criterion for the signal classification. According to the result of the confusion matrix, the weight of the model is adjusted. Experiments show that the recognition rate of the modulated signal mentioned in this paper can reach 82.75%. The I/Q data sample was extracted under actual engineering conditions involving random noise, and the recognition rate dropped to approximately 79%. Based on the initial model gradient matrix, a reasonable algorithm is set to adjust the weight of the model, which can effectively improve the recognition rate of the modulated signal.
Keyword:
SPECTRUM
INTERFERENCE
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
28.1W
被引数:
83.5W
机构
引用论文
End-to-End Learning From Spectrum Data A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications
IEEE ACCESS
IF3.6
没有更多内容

