A Modulation-Information-guided Dual-Branch Specific Emitter Identification Method Under Variable Modulation Scenarios

WANG Dengxi; SUN Jiaxing; LIU Hui; HUANG Keju; WANG Wanze; YANG Junan

doi:10.11999/JEIT260349

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WANG Dengxi, SUN Jiaxing, LIU Hui, HUANG Keju, WANG Wanze, YANG Junan. A Modulation-Information-guided Dual-Branch Specific Emitter Identification Method Under Variable Modulation Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260349

Citation:

WANG Dengxi, SUN Jiaxing, LIU Hui, HUANG Keju, WANG Wanze, YANG Junan. A Modulation-Information-guided Dual-Branch Specific Emitter Identification Method Under Variable Modulation Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260349

Citation:

WANG Dengxi, SUN Jiaxing, LIU Hui, HUANG Keju, WANG Wanze, YANG Junan. A Modulation-Information-guided Dual-Branch Specific Emitter Identification Method Under Variable Modulation Scenarios[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260349

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A Modulation-Information-guided Dual-Branch Specific Emitter Identification Method Under Variable Modulation Scenarios

doi: 10.11999/JEIT260349 cstr: 32379.14.JEIT260349

1.
College of Electronic Engineering, National University of Defense Technology, Hefei 230000, China
2.
Anhui province key laboratory of electronic restriction, Hefei 230000, China

Funds: Innovation Research Foundation of National University of Defense Technology

Received Date: 2026-03-26
Accepted Date: 2026-09-15
Rev Recd Date: 2026-09-09

Available Online: 2026-10-10

Abstract

Abstract

Objective Specific Emitter Identification (SEI) distinguishes individual emitters by exploiting Radio Frequency Fingerprints (RFFs) and has important applications in military reconnaissance and Internet of Things (IoT) authentication. However, conventional SEI methods generally assume fixed modulation parameters, whereas practical communication systems often employ dynamically varying modulation schemes. Existing Domain Adaptation (DA) methods for variable-modulation SEI require target-domain data retraining and still suffer from insufficient robustness of extracted features under unknown modulation variations. To address these problems, this paper introduces Domain Generalization (DG) into variable-modulation SEI and proposes a Modulation-Information-Guided Dual-branch Training Framework (MIG-DTF) with a Testing-stage Model Adaptation strategy (TMA). The proposed framework aims to learn modulation-invariant RFFs from source-domain data and improve SEI performance under unknown modulation parameter variations without large-scale target-domain retraining. Methods The proposed method consists of training and testing stages. During the training stage, MIG-DTF employs an emitter identification main branch and a modulation classification auxiliary branch for collaborative optimization. The main branch utilizes cosine aggregation loss to enhance the intra-class compactness and inter-class discriminability of RFFs. Meanwhile, the auxiliary branch maximizes cross-prediction entropy to suppress modulation-related information contained in the RFFs extracted by the main branch, thereby encouraging the model to learn modulation-invariant fingerprint features. During the testing stage, the proposed TMA strategy performs one-step fine-tuning of the Batch Normalization (BN) layer parameters using test samples under an entropy minimization objective. By adapting BN statistics and affine parameters to the testing distribution, TMA alleviates feature distribution drift caused by modulation mismatch and further improves identification accuracy. Results and Discussions Experiments are conducted on a real-world variable-modulation dataset containing three typical variation scenarios: modulation type, carrier frequency, and symbol rate. The proposed method achieves identification accuracies of 79.31% (Table 1), 87.39% (Table 2), and 88.33% (Table 3) in the three scenarios, respectively, outperforming mainstream DG methods. Ablation experiments demonstrate that both MIG-DTF and TMA independently contribute to performance improvement, and their combination provides further gains (Fig. 4). Feature visualization results show that the proposed method obtains more compact intra-class distributions and clearer inter-class boundaries under different modulation conditions (Fig. 5). Correlation analysis between extracted features and modulation labels further verifies that the proposed method reduces the dependence of RFFs on modulation information while preserving emitter-discriminative characteristics (Table 5). In anti-noise experiments with Signal-to-Noise Ratio (SNR) values from 10 dB to 30 dB, the proposed method consistently outperforms the baseline method and achieves an accuracy improvement of approximately 10% at 10 dB SNR (Fig. 7), demonstrating stronger robustness under noisy conditions. In addition, overhead analysis shows that TMA requires only a small number of parameter updates and eliminates the need for target-domain training data, reducing adaptation costs for practical deployment. Conclusions This paper proposes a robust SEI framework for variable modulation scenarios by combining the training-stage MIG-DTF and testing-stage TMA strategies. MIG-DTF enables the extraction of modulation-invariant RFFs by separating emitter-related fingerprint information from modulation-related information, whereas TMA provides efficient adaptation to unknown testing distributions with low computational overhead. Experimental results on a real-world dataset demonstrate that: (1) the proposed method achieves higher identification accuracy than mainstream DG methods under multiple modulation parameter variations; (2) the extracted features exhibit stronger modulation invariance and emitter discrimination capability; (3) the proposed method maintains better performance under noise interference; and (4) TMA enables efficient adaptation without target-domain retraining. Future work will investigate more complex scenarios, including joint variations of multiple modulation parameters and time-varying channel conditions.
- Specific Emitter Identification(SEI),
- Deep Learning(DL),
- Domain Generalization(DG),
- Variable modulation

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References(26)

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