A Semantic-Consistent Few-Shot Modulation Recognition Framework for IoT Applications

doi:10.1109/TNNLS.2024.3441597

	A Semantic-Consistent Few-Shot Modulation Recognition Framework for IoT Applications
	Su, Jie 1,2,3; Sun, Peng 4; Jiang, Yuting 1,2; Wen, Zhenyu 1,2,3; Guo, Fangda 5; Wu, Yiming 1,2,3; Hong, Zhen 1,2; Duan, Haoran 6; Huang, Yawen 7; Ranjan, Rajiv 8; Zheng, Yefeng 4
	2024-08-23
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
页码	12
摘要	The rapid growth of the Internet of Things (IoT) has led to the widespread adoption of the IoT networks in numerous digital applications. To counter physical threats in these systems, automatic modulation classification (AMC) has emerged as an effective approach for identifying the modulation format of signals in noisy environments. However, identifying those threats can be particularly challenging due to the scarcity of labeled data, which is a common issue in various IoT applications, such as anomaly detection for unmanned aerial vehicles (UAVs) and intrusion detection in the IoT networks. Few-shot learning (FSL) offers a promising solution by enabling models to grasp the concepts of new classes using only a limited number of labeled samples. However, prevalent FSL techniques are primarily tailored for tasks in the computer vision domain and are not suitable for the wireless signal domain. Instead of designing a new FSL model, this work suggests a novel approach that enhances wireless signals to be more efficiently processed by the existing state-of-the-art (SOTA) FSL models. We present the semantic-consistent signal pretransformation (ScSP), a parameterized transformation architecture that ensures signals with identical semantics exhibit similar representations. ScSP is designed to integrate seamlessly with various SOTA FSL models for signal modulation recognition and supports commonly used deep learning backbones. Our evaluation indicates that ScSP boosts the performance of numerous SOTA FSL models, while preserving flexibility.
关键词	Deep learning few-shot learning (FSL) Internet of Things (IoT) signal processing
DOI	10.1109/TNNLS.2024.3441597
收录类别	SCI
语种	英语
资助项目	National Nature Science Foundation of China[62302454] ; National Nature Science Foundation of China[62072408] ; National Nature Science Foundation of China[62302485] ; Zhejiang Provincial Science Fund for Distinguished Young Scholars[LR24F020004] ; China Postdoctoral Science Foundation[2023M743403] ; China Postdoctoral Science Foundation[2022M713206] ; Zhejiang Provincial Natural Science Foundation of Major Program(Youth Original Project)[LDQ24F020001] ; Key Research and Development Program of Zhejiang[2024C03288] ; Chinese Academy of Sciences (CAS) Special Research Assistant Program
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:001297411200001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/39608
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wen, Zhenyu; Guo, Fangda
作者单位	1.Zhejiang Univ Technol, Inst Cyberspace Secur, Hangzhou 310023, Zhejiang, Peoples R China 2.Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Zhejiang, Peoples R China 3.Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230026, Anhui, Peoples R China 4.Westlake Univ, Sch Engn, Hangzhou 310030, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab AI Safety, Beijing 100190, Peoples R China 6.Univ Durham, Dept Comp Sci, Durham DH1 3LE, England 7.Tencent Jarvis Lab, Shenzhen 518057, Peoples R China 8.Newcastle Univ, Sch Comp Sci, Newcastle Upon Tyne NE1 7RU, England
推荐引用方式 GB/T 7714	Su, Jie,Sun, Peng,Jiang, Yuting,et al. A Semantic-Consistent Few-Shot Modulation Recognition Framework for IoT Applications[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2024:12.
APA	Su, Jie.,Sun, Peng.,Jiang, Yuting.,Wen, Zhenyu.,Guo, Fangda.,...&Zheng, Yefeng.(2024).A Semantic-Consistent Few-Shot Modulation Recognition Framework for IoT Applications.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA	Su, Jie,et al."A Semantic-Consistent Few-Shot Modulation Recognition Framework for IoT Applications".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2024):12.