Domain Adaptation-Based Automatic Modulation Recognition
| Autor: | Yingzhe Xiao, Tong Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Domain adaptation
Article Subject business.industry Computer science Deep learning Network structure Pattern recognition Software-defined radio Signal Residual neural network Computer Science Applications Domain (software engineering) QA76.75-76.765 ComputingMethodologies_PATTERNRECOGNITION Modulation Computer software Artificial intelligence business Software |
| Zdroj: | Scientific Programming, Vol 2021 (2021) |
| ISSN: | 1875-919X 1058-9244 |
| DOI: | 10.1155/2021/4277061 |
| Popis: | Deep learning-based Automatic Modulation Recognition (AMR) can improve the recognition rate compared with traditional AMR methods. However, in practical applications, as training samples and real scenario signal samples have different distributions in practical applications, the recognition rate for target domain samples can deteriorate significantly. This paper proposed an unsupervised domain adaptation based AMR method, which can enhance the recognition performance by adopting labeled samples from the source domain and unlabeled samples from the target domain. The proposed method is validated through signal samples generated from the open-sourced Software Defined Radio (SDR) GNU Radio. The training dataset is composed of labeled samples in the source domain and unlabeled samples in the target domain. In the testing dataset, the samples are from the target domain to simulate the real scenario. Through the experiment, the proposed method has a recognition rate increase of about 88% under the CNN network structure and 91% under the ResNet network structure. |
| Databáze: | OpenAIRE |
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