.png){kind=icon}
返回
Edge-Learning-Based Collaborative Automatic Modulation Classification for Hierarchical Cognitive Radio Networks
.png){kind=icon}
OA
AI
DOI：10.1109/JIOT.2024.3431236.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In hierarchical cognitive radio networks, the edge or cloud servers utilize the data collected by the edge devices for modulation classification, which, however, is faced with problems of the computation load, transmission overhead, and data privacy. In this article, an edge learning (EL)-based framework jointly mobilizing the edge device and the edge server for intelligent co-inference is proposed to realize the collaborative automatic modulation classification (C-AMC) between them. A spectrum semantic compression neural network is designed for the edge device to compress the collected raw data into a compact semantic embedding that is then sent to the edge server via the wireless channel. On the edge server side, a modulation classification neural network combining the bidirectional long-short term memory and attention structures is elaborated to determine the modulation type from the noisy semantic embedding. The C-AMC framework decently balances the computation resources of both the sides while avoiding the high transmission overhead and data privacy leakage. Both the offline and online training procedures of the C-AMC framework are elaborated. The compression strategy of the C-AMC framework is also developed to further facilitate the deployment, especially for the resource-constrained edge device. Simulation results show the superiority of the EL-based C-AMC framework in terms of the classification accuracy, computational complexity, and the data compression rate as well as reveal useful insights paving the practical implementation.
Keyword:
Automatic modulation classification (AMC)
cognitive radio (CR)
edge learning (EL)
model compression
spectrum semantics
Automatic modulation classification (AMC)
cognitive radio (CR)
edge learning (EL)
model compression
spectrum semantics
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
暂无机构信息
引用论文
Selenocarbonyl complexes of iridium(I) and iridium(III). Synthesis and reactions of IrCl(CSe)(PPh3)2铱 (I) 和铱 (III) 的硒羰基配合物。IrCl(CSe)(PPh3)2的合成与反应
.png){kind=icon}
PREAI
分享收藏MCNet: An Efficient CNN Architecture for Robust Automatic Modulation ClassificationMCNet: 一种用于鲁棒自动调制分类的高效CNN架构
PREAI
分享收藏Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum Sensors基于分布式低成本频谱传感器的无线信号分类深度学习模型
OAAI
OAAI分享收藏A Lightweight Decentralized-Learning-Based Automatic Modulation Classification Method for Resource-Constrained Edge Devices一种面向资源受限边缘设备的轻量级分散学习自动调制分类方法OAAI
OAAI分享收藏
查看更多内容