Automatic modulation classification (AMC) plays a critical role in wireless communications by autonomously classifying signals transmitted over the radio spectrum. Deep learning (DL) techniques are increasingly being used for AMC due to their ability to extract complex wireless signal features. However, DL models are computationally intensive and incur high inference latencies. This paper proposes the application of early exiting (EE) techniques for DL models used for AMC to accelerate inference. We present and analyze four early exiting architectures and a customized multi-branch training algorithm for this problem. Through extensive experimentation, we show that signals with moderate to high signal-to-noise ratios (SNRs) are easier to classify, do not require deep architectures, and can therefore leverage the proposed EE architectures. Our experimental results demonstrate that EE techniques can significantly reduce the inference speed of deep neural networks without sacrificing classification accuracy. We also thoroughly study the trade-off between classification accuracy and inference time when using these architectures. To the best of our knowledge, this work represents the first attempt to apply early exiting methods to AMC, providing a foundation for future research in this area.

翻译：自动调制分类（AMC）在无线通信中发挥着关键作用，能够自主分类无线电频谱上传输的信号。深度学习（DL）技术因能够提取复杂的无线信号特征，正越来越多地应用于AMC。然而，DL模型计算量大且推理延迟高。本文提出将早期退出（EE）技术应用于AMC的DL模型中以加速推理。我们针对该问题提出并分析了四种早期退出架构以及一种定制的多分支训练算法。通过大量实验，我们发现具有中高信噪比（SNR）的信号更易于分类，无需深层架构，因此可利用所提出的EE架构。实验结果表明，EE技术能在不牺牲分类准确率的前提下显著降低深度神经网络的推理速度。我们还深入研究了使用这些架构时分类准确率与推理时间之间的权衡。据我们所知，本研究是首次将早期退出方法应用于AMC，为这一领域的未来研究奠定了基础。