保持简单：用于干扰分类任务的CNN模型复杂度研究
Keep It Simple: CNN Model Complexity Studies for Interference Classification Tasks
AI总结：
针对无线设备计算资源有限而现有深度学习分类模型过于复杂的问题，本文研究了数据集大小、CNN模型复杂度与分类准确率间的权衡，发现参数更少的简单CNN模型能取得与复杂模型相当的表现。
摘要：
使用无线频谱的设备数量不断增加，这使得寻找最小化干扰和优化频谱使用的方法变得十分重要。深度学习模型，如卷积神经网络（CNN），由于其直接从数据中学习的能力，已被广泛用于识别、分类或缓解干扰。然而，关于此类深度学习模型复杂度的研究却很有限。基于深度学习的无线分类文献主要集中在提高分类准确率上，往往以牺牲模型复杂度为代价。这对于许多无线设备（例如物联网设备）可能并不实用，因为它们通常计算资源非常有限，无法处理过于复杂的模型。因此，在设计用于干扰分类的基于深度学习的模型时，考虑模型复杂度变得至关重要。为了解决这一问题，我们对基于CNN的无线分类进行了分析，探讨了在多种分类难度级别下，数据集大小、CNN模型复杂度与分类准确率之间的权衡关系：即干扰分类、异构发射机分类和同构发射机分类。我们基于三个无线数据集的研究表明，参数更少的简单CNN模型的表现可以与更复杂的模型一样好，为在计算受限的应用中使用CNN提供了重要见解。
英文摘要：
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.
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