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| Main Authors: | , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.08783 |
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| _version_ | 1866918438007472128 |
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| author | Liu, Zheng Abou-Zeid, Hatem Wu, Huaqing |
| author_facet | Liu, Zheng Abou-Zeid, Hatem Wu, Huaqing |
| contents | Convolutional neural networks (CNNs) have emerged as a powerful tool for automatic modulation classification (AMC) by directly extracting discriminative features from raw in-phase and quadrature (I/Q) signals. However, deploying CNN-based AMC models on IoT devices remains challenging because of limited computational resources, energy constraints, and real-time processing requirements. Early-exit (EE) strategies alleviate this burden by allowing qualified samples to terminate inference at an EE branch. However, our empirical analysis reveals a critical limitation of existing confidence-based EE strategies: they predominantly select samples whose early and final predictions are correct and consistent, while failing to capture whether deeper inference can provide a tangible accuracy gain. To address this limitation, we propose BEACON, a Benefit-Aware Early-Exit framework for AMC via recoverability prediction. BEACON introduces a benefit-aware EE criterion that explicitly predicts recoverable errors, defined as instances where the final-exit branch corrects an initial early-branch misclassification. Using only short-branch observables, we design a lightweight benefit-aware predictor (LBAP) to implement this criterion, estimating the likelihood of such recoverable cases and triggering deeper inference only when an accuracy gain is expected. Extensive experiments on ResNet-18-based AMC models demonstrate that the proposed approach consistently outperforms state-of-the-art baselines, achieving a superior accuracy-computation tradeoff across diverse EE threshold settings and signal-to-noise ratio regimes. These findings validate the effectiveness of the benefit-aware criterion and its practicality for energy-efficient on-device AMC under stringent resource constraints. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_08783 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BEACON: Benefit-Aware Early-Exit for Automatic Modulation Classification via Recoverability Prediction Liu, Zheng Abou-Zeid, Hatem Wu, Huaqing Signal Processing Convolutional neural networks (CNNs) have emerged as a powerful tool for automatic modulation classification (AMC) by directly extracting discriminative features from raw in-phase and quadrature (I/Q) signals. However, deploying CNN-based AMC models on IoT devices remains challenging because of limited computational resources, energy constraints, and real-time processing requirements. Early-exit (EE) strategies alleviate this burden by allowing qualified samples to terminate inference at an EE branch. However, our empirical analysis reveals a critical limitation of existing confidence-based EE strategies: they predominantly select samples whose early and final predictions are correct and consistent, while failing to capture whether deeper inference can provide a tangible accuracy gain. To address this limitation, we propose BEACON, a Benefit-Aware Early-Exit framework for AMC via recoverability prediction. BEACON introduces a benefit-aware EE criterion that explicitly predicts recoverable errors, defined as instances where the final-exit branch corrects an initial early-branch misclassification. Using only short-branch observables, we design a lightweight benefit-aware predictor (LBAP) to implement this criterion, estimating the likelihood of such recoverable cases and triggering deeper inference only when an accuracy gain is expected. Extensive experiments on ResNet-18-based AMC models demonstrate that the proposed approach consistently outperforms state-of-the-art baselines, achieving a superior accuracy-computation tradeoff across diverse EE threshold settings and signal-to-noise ratio regimes. These findings validate the effectiveness of the benefit-aware criterion and its practicality for energy-efficient on-device AMC under stringent resource constraints. |
| title | BEACON: Benefit-Aware Early-Exit for Automatic Modulation Classification via Recoverability Prediction |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2604.08783 |