期刊
SENSORS
卷 22, 期 15, 页码 -出版社
MDPI
DOI: 10.3390/s22155715
关键词
wireless sensor networks; modulation recognition; radio frequency database; multipath fading channels
资金
- Natural Science Foundation of Shaanxi Province, China [2021JM-220]
This study collects real-world wireless signal data and proposes a new database architecture and preprocessing operators to manage and analyze the data. Based on the collected data, two algorithms, MFHN and MJMN, are designed for recognizing unknown signals and small-sample targets. Experimental results show that these methods improve recognition performance in wireless sensor networks.
Current modulation recognition methods in wireless sensor networks rely too much on simulation datasets. Its practical application effect cannot reach the expected results. To address this issue, in this paper we collect a large amount of real-world wireless signal data based on the software radio device USRP 2920. We then propose a real radio frequency (RF) database architecture and preprocessing operators to manage real-world wireless signal data, conduct signal preprocessing, and export the dataset. Based on different feature datasets derived from the RF database, we propose a multidimensional feature hybrid network (MFHN), which is used to identify unknown signals by analyzing different kinds of signal features. Further, we improve MFHN and design a multifeatured joint migration network (MJMN) to identify small-sample targets. The experimental results show that the recognition rates for unknown target signals of the MFHN and MJMN are 82.7% and 93.2%, respectively. The proposed methods improve the recognition performance in the single node of wireless sensor networks in complex electromagnetic environments, which provides reference for subsequent decision fusion.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据