论文标题
在低资源场景中，用于快速自动调制分类的Ultra Lite卷积神经网络
Ultra Lite Convolutional Neural Network for Fast Automatic Modulation Classification in Low-Resource Scenarios
论文作者
论文摘要
自动调制分类（AMC）是设计非合作通信系统的关键技术，并且有效地将深度学习（DL）应用于AMC，以提高分类精度。但是，大多数基于DL的AMC方法具有大量参数和较高的计算复杂性，并且它们不能直接应用于具有有限的计算能力和存储空间的低资源场景。在这封信中，我们提出了一种使用超轻质卷积神经网络（ULCNN）的快速和低复杂性的快速AMC方法，该方法包括数据增强，复杂值卷积，可分开的卷积，通道注意力和通道洗牌。仿真结果表明，我们提出的基于ULCNN的AMC方法的平均准确度在RML2016.10A上的平均准确度为62.47％，只有9,751个参数。此外，在典型的边缘设备（Raspberry Pi）上验证了ULCNN，其中每个样品的干扰时间约为0.775 ms。可再现的代码可以从github \ footNote {https://github.com/beechburgpiestar/beechburgpiestar/ultra-lite-convolutional-networtal-neal-network-network-for-automation-modastic-classification}下载。
Automatic modulation classification (AMC) is a key technique for designing non-cooperative communication systems, and deep learning (DL) is applied effectively to AMC for improving classification accuracy. However, most of the DL-based AMC methods have a large number of parameters and high computational complexity, and they cannot be directly applied to low-resource scenarios with limited computing power and storage space. In this letter, we propose a fast AMC method with lightweight and low-complexity using ultra lite convolutional neural network (ULCNN) consisting of data augmentation, complex-valued convolution, separable convolution, channel attention, and channel shuffle. Simulation results demonstrate that our proposed ULCNN-based AMC method achieves an average accuracy of 62.47% on RML2016.10a and only 9,751 parameters. Moreover, ULCNN is verified on a typical edge device (Raspberry Pi), where the interference time per sample is about 0.775 ms. The reproducible code can be downloaded from GitHub\footnote{https://github.com/BeechburgPieStar/Ultra-Lite-Convolutional-Neural-Network-for-Automatic-Modulation-Classification}.
