.png){kind=icon}
返回
A Lightweight Decentralized-Learning-Based Automatic Modulation Classification Method for Resource-Constrained Edge Devices
.png){kind=icon}
OA
AI
DOI：10.1109/JIOT.2022.3194508.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the computing capability and memory limitations, it is difficult to apply the traditional deep learning (DL) models to the edge devices (EDs) for realizing lightweight automatic modulation classification (AMC). Recently, many works attempt to use different ways to realize lightweight AMC methods for EDs. However, the lightweight seems to be a contradiction with the classification performance in these lightweight networks. In this article, we propose an efficient lightweight decentralized-learning-based AMC (DecentAMC) method using spatiotemporal hybrid deep neural network based on multichannels and multifunction blocks (MCMBNN). Specifically, the lightweight network is designed from the perspectives of comprehensive consideration of lightweight and classification performance, which is composed of three parts to extract different features for realizing high classification performance and they are phase estimator and transformer (PET) block, spatial feature extraction block and temporal feature extraction & Softmax block. In addition, we use a multichannel input to extract complementary features of different channels for a better classification performance. The proposed DecentAMC method is an efficient training method, which is achieved by the cooperation in which multiple EDs update and upload the model weight to a central device (CD) for model aggregation to avoid the data privacy disclosure and reduce the computing power and storage pressure of CD. Experimental results show that the proposed MCMBNN can obtain an improved classification accuracy while reducing model complexity with the contributions of three blocks. Moreover, the proposed DecentAMC method can be deployed on EDs efficiently. Thus, the method has the advantages of avoiding data leakage on EDs and relieving the computing pressure of CD with relatively lower communication overhead. The simulation code and datasets are shared on GitHub.
Keyword:
Automatic modulation classification (AMC)
decentralized learning
Internet of Things (IoT)
lightweight neural network
spatiotemporal hybrid deep neural network
期刊
IF:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
A Study on Global Investors’ Criteria for Investment in the Local Currency Bond Markets Using AHP Methods: The Case of the Republic of Korea.png)
RisksOAAI
RisksIF0
OAAI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 for Channel Estimation: Interpretation, Performance, and Comparison用于信道估计的深度学习: 解释、性能和比较OAAI
OAAI分享收藏
查看更多内容