Meta Supervised Contrastive Learning for Few-Shot Open-Set Modulation Classification With Signal Constellation

Jikui Zhao; Huaxia Wang; Shengliang Peng; Yu Dong Yao

doi:10.1109/LCOMM.2024.3363630

Meta Supervised Contrastive Learning for Few-Shot Open-Set Modulation Classification With Signal Constellation

Jikui Zhao, Huaxia Wang, Shengliang Peng, Yu Dong Yao

Research output: Contribution to journal › Article › peer-review

Abstract

In this work, we introduce a method for few-shot open-set modulation classification utilizing signal constellation diagrams, based on a Meta Supervised Contrastive Learning (MSCL) algorithm. MSCL combines the strengths of supervised contrastive learning and meta-learning to effectively amplify inter-class distinctions and reinforce intra-class compactness. The experimental results demonstrate that MSCL exhibits superior performance in both few-shot and open-set Automatic Modulation Classification (AMC) recognition. Code available at: https://github.com/jikuizhao/MSCL

Original language	American English
Pages (from-to)	837-841
Number of pages	5
Journal	IEEE Communications Letters
Volume	28
Issue number	4
DOIs	https://doi.org/10.1109/LCOMM.2024.3363630
State	Published - Apr 1 2024
Externally published	Yes

ASJC Scopus subject areas

Modeling and Simulation
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/LCOMM.2024.3363630

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title = "Meta Supervised Contrastive Learning for Few-Shot Open-Set Modulation Classification With Signal Constellation",

abstract = "In this work, we introduce a method for few-shot open-set modulation classification utilizing signal constellation diagrams, based on a Meta Supervised Contrastive Learning (MSCL) algorithm. MSCL combines the strengths of supervised contrastive learning and meta-learning to effectively amplify inter-class distinctions and reinforce intra-class compactness. The experimental results demonstrate that MSCL exhibits superior performance in both few-shot and open-set Automatic Modulation Classification (AMC) recognition. Code available at: https://github.com/jikuizhao/MSCL",

author = "Jikui Zhao and Huaxia Wang and Shengliang Peng and Yao, {Yu Dong}",

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AU - Yao, Yu Dong

PY - 2024/4/1

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AB - In this work, we introduce a method for few-shot open-set modulation classification utilizing signal constellation diagrams, based on a Meta Supervised Contrastive Learning (MSCL) algorithm. MSCL combines the strengths of supervised contrastive learning and meta-learning to effectively amplify inter-class distinctions and reinforce intra-class compactness. The experimental results demonstrate that MSCL exhibits superior performance in both few-shot and open-set Automatic Modulation Classification (AMC) recognition. Code available at: https://github.com/jikuizhao/MSCL

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Meta Supervised Contrastive Learning for Few-Shot Open-Set Modulation Classification With Signal Constellation

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ASJC Scopus subject areas

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