Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems. Unlike traditional approaches that rely on pre-designed strategies that are solely based on expert knowledge, the proposed data-driven subsampling strategy employs deep neural network architectures to simulate the effect of removing candidate combinations of samples from each training input vector, in a manner inspired by how wrapper feature selection models work. The subsampled data is then processed by another deep learning classifier that recognizes each of the considered 10 modulation types. We show that the proposed subsampling strategy not only introduces drastic reduction in the classifier training time, but can also improve the classification accuracy for the considered dataset. An important feature herein is exploiting the transferability property of deep neural networks to avoid retraining the wrapper models and obtain superior performance through an ensemble of wrappers over that possible through solely relying on any one of them.

中文翻译：

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用于深度调制分类的集成包装子采样

接收无线信号的子采样对于放宽硬件要求以及依赖于输出样本的信号处理算法的计算成本非常重要。我们提出了一种子采样技术，以促进在无线通信系统中使用深度学习进行自动调制分类。与依赖于完全基于专家知识的预先设计的策略的传统方法不同，所提出的数据驱动子采样策略采用深度神经网络架构来模拟从每个训练输入向量中去除候选样本组合的效果，以一种受启发的方式通过包装器特征选择模型的工作方式。子采样数据然后由另一个深度学习分类器处理，该分类器识别所考虑的 10 种调制类型中的每一种。我们表明，所提出的子采样策略不仅大大减少了分类器的训练时间，而且还可以提高所考虑数据集的分类精度。这里的一个重要特征是利用深度神经网络的可转移性来避免重新训练包装器模型，并通过一组包装器获得优于仅依赖其中任何一个包装器的性能。