Towards Privacy-Preserving and Lightweight Modulation Recognition for Short-Wave Signals under Channel Shifts

YAO Yizhou; DENG Wen; LI Baoguo

doi:10.11999/JEIT251017

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YAO Yizhou, DENG Wen, LI Baoguo. Towards Privacy-Preserving and Lightweight Modulation Recognition for Short-Wave Signals under Channel Shifts[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251017

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YAO Yizhou, DENG Wen, LI Baoguo. Towards Privacy-Preserving and Lightweight Modulation Recognition for Short-Wave Signals under Channel Shifts[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251017

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Towards Privacy-Preserving and Lightweight Modulation Recognition for Short-Wave Signals under Channel Shifts

doi: 10.11999/JEIT251017 cstr: 32379.14.JEIT251017

YAO Yizhou¹,
DENG Wen^{2
,
,},
LI Baoguo³

1.
Gaoling Training Base of the People's Armed Police, Beijing 101507, China
2.
People's Liberation Army Unit 63892, Luoyang 471003, China
3.
College of Electronic Science, National University of Defense Technology, Changsha 410073, China

Accepted Date: 2026-01-22
Rev Recd Date: 2026-01-22

Available Online: 2026-02-11

Abstract

Abstract

Objective Existing short-wave signal modulation recognition methods based on the supervised learning paradigm typically assume that training data (source domain) and test data (target domain) follow identical distributions. However, short-wave channels are susceptible to ionospheric variations, leading to significant distribution discrepancies across domains, which consequently causes model performance degradation. Furthermore, deployment on the edge side of unmanned platforms is constrained by limited device resources, scarce labeled samples, and data privacy requirements. To address these challenges, a lightweight recognition method based on source-model transfer is proposed in this paper, enabling privacy-preserving model adaptation without the need to access source domain data. Methods A multi-modal source-model transfer framework (M-SMOT) is developed, which utilizes information maximization loss and self-supervised pseudo-labeling techniques to facilitate model adaptation without revisiting source domain data. This approach achieves effective cross-channel recognition of short-wave modulation signals while reducing computational resource consumption and preserving data privacy. Additionally, multi-modal information—comprising in-phase/quadrature (I/Q) components, amplitude-phase (AP) characteristics, and spectral features—is fused to leverage complementary feature representations, thereby enhancing the robustness of the recognition network against complex channel variations. Results and Discussions Experimental results demonstrate that the recognition performance of the proposed method consistently surpasses that of the Source-Only baseline across six cross-channel scenarios, with improvements ranging from 0.31% to 10.81% (Table 1). In terms of few-shot adaptation, average recognition accuracies are maintained at 98.3% and 96% relative to the full-sample baseline, even when target domain training samples are reduced to 10% and 1%, respectively (Fig. 12). Ablation studies verify the necessity and effectiveness of the self-supervised pseudo-labeling module (Fig. 16) and the multi-modal fusion strategy (Fig. 17), confirming that both components contribute to the overall performance. Furthermore, the lightweight advantages are quantified: the method requires zero storage for source data, exhibits a peak memory consumption of only 6.00 MB, and achieves convergence within a single fine-tuning epoch (Table 2). These findings validate the capability of the proposed mechanism to mitigate domain discrepancies and protect privacy under resource-constrained conditions. Conclusions The M-SMOT method successfully integrates data privacy protection, source model adaptation, few-shot generalization, and low resource consumption. Consequently, it provides a practical solution for cross-channel modulation recognition in short-wave communications, demonstrating significant potential for deployment on resource-limited edge devices.
- Short-wave communication,
- Modulation recognition,
- Source-model transfer,
- Lightweight,
- Multi-modal fusion,
- Few-shot

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References(18)

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