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简介:RAFF-AMACNet:适应式多速率心腔卷积网络,具有剩余注意特征融合,用于卫星信号识别

Leyan Chen¹, Bo Zang¹, Yi Zhang¹

¹School of Electronic Engineering, Xidian University, Xi'an 710071, China.

Sensors (Basel, Switzerland)

|December 31, 2025

概括

本研究引入了一种用于卫星通信中自动调制识别 (AMR) 的新型网络. 在复杂的环境中,RAFF-AMACNet实现了高精度,改善了频谱管理.

科学领域:

电气工程电气工程
计算机科学计算机科学
信号处理信号处理

背景情况:

自动调制识别 (AMR) 对卫星通信网络和频谱管理至关重要.
现有的AMR模型在复杂的卫星环境中面临挑战,其中存在显著的多普勒效应和非线性影响.

研究的目的:

提出一种先进的AMR模型,能够处理复杂的卫星通信场景.
增强特征提取和融合,以提高调制识别精度.

主要方法:

开发了一个具有剩余注意力特征融合 (RAFF-AMACNet) 的自适应性多速率心膜卷积网络.
使用自适应式多速率心膜卷积 (AMAC) 模块进行多尺度特征提取.
采用了一个金字塔骨干,堆叠了剩余注意力特征融合 (RAFF) 模块和双重注意力机制.

主要成果:

该RAFF-AMACNet显示强大的功能地图生成和增强时间序列的上下文意识.
双重注意力机制有效地减轻了特征地图的转移,并在不利条件下增加了阶级分离.
在RML24数据集上实现了92.99%的调制识别准确度,信号与噪声比为0dB.

结论:

拟议的RAFF-AMACNet在具有挑战性的卫星通信环境中显著提高了AMR性能.
该模型为卫星信号识别和频谱管理提供了强大的解决方案.
RML24数据集为评估认知无线电系统中的AMR技术提供了宝贵的资源.

关键词:

在的卷积过程中.自动调制识别自动调制识别双重注意力协作机制协作机制多个尺度的特征.卫星信号卫星信号卫星信号

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