The growing number of devices using the wireless spectrum makes it important to find ways to minimize interference and optimize the use of the spectrum. Deep learning models, such as convolutional neural networks (CNNs), have been widely utilized to identify, classify, or mitigate interference due to their ability to learn from the data directly. However, there have been limited research on the complexity of such deep learning models. The major focus of deep learning-based wireless classification literature has been on improving classification accuracy, often at the expense of model complexity. This may not be practical for many wireless devices, such as, internet of things (IoT) devices, which usually have very limited computational resources and cannot handle very complex models. Thus, it becomes important to account for model complexity when designing deep learning-based models for interference classification. To address this, we conduct an analysis of CNN based wireless classification that explores the trade-off amongst dataset size, CNN model complexity, and classification accuracy under various levels of classification difficulty: namely, interference classification, heterogeneous transmitter classification, and homogeneous transmitter classification. Our study, based on three wireless datasets, shows that a simpler CNN model with fewer parameters can perform just as well as a more complex model, providing important insights into the use of CNNs in computationally constrained applications.

翻译：随着使用无线频谱的设备数量不断增加，寻找减少干扰并优化频谱利用的方法变得至关重要。卷积神经网络（CNN）等深度学习模型因其能够直接从数据中学习，已被广泛用于识别、分类或减轻干扰。然而，关于此类深度学习模型复杂度的研究仍十分有限。现有基于深度学习的无线分类文献主要关注提高分类精度，这往往以牺牲模型复杂度为代价。对于许多无线设备（例如物联网设备）而言，这类方法可能并不实用，因为此类设备通常计算资源极为有限，无法处理过于复杂的模型。因此，在设计基于深度学习的干扰分类模型时，考虑模型复杂度就显得尤为重要。为解决这一问题，我们针对基于CNN的无线分类进行了分析，探讨了在不同分类难度水平下——即干扰分类、异构发射机分类和同构发射机分类——数据集大小、CNN模型复杂度与分类精度之间的权衡。基于三个无线数据集的研究表明，参数更少的简单CNN模型能够取得与复杂模型相当的性能，这为在计算受限的应用中使用CNN提供了重要启示。