计算机科学
特征提取
调制(音乐)
对比度(视觉)
特征(语言学)
人工智能
信号(编程语言)
模式识别(心理学)
背景(考古学)
机器学习
哲学
语言学
程序设计语言
美学
古生物学
生物
作者
Jing Bai,Xuebo Liu,Yiran Wang,Zhu Xiao,Feng Chen,Huaji Zhou,Licheng Jiao
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2024-03-19
卷期号:11 (12): 21461-21473
被引量:4
标识
DOI:10.1109/jiot.2024.3377916
摘要
With the advancement of Internet of Things technology, the need for sophisticated signal modulation classification has intensified, ensuring seamless communication and bolstering security among interconnected devices. In the contemporary complex channel environment, the difficult lies in dealing with a multitude of modulation schemes that exhibit subtle distinctions. Prior knowledge-guided and deep learning methods have complementary strengths in the current context of signal modulation classification. To synthesize the advantages of these two methods, we propose an integrated method of prior knowledge and contrast feature for signal modulation classification, called APFS. APFS integrates prior knowledge from the modulation task with feature information acquired through contrastive learning. Feature extraction guided by prior knowledge accurately captures the key patterns in modulated signals. Contrastive learning reveals the inherent distinctions among various modulation modes by comparing different samples. In the joint feature extraction approach for prior knowledge, each form of prior knowledge is first analyzed independently, and then jointed to extract information from its temporal sequence. The contrast features surpass the constraints of labeling and unearth deeper implicit information. In experiments, we systematically compared the performance of our method with various baselines, as well as combinations of prior knowledge and contrast feature. The results demonstrate the superior performance of our method.
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