The wireless environment poses a significant challenge to the propagation of signals. Different effects such as multipath scattering, noise, degradation, distortion, attenuation, and fading affect the distribution of signals adversely. Deep learning techniques can be used to differentiate among different modulated signals for reliable detection in a communication system. This study aims at distinguishing COVID-19 disease images that have been modulated by different digital modulation schemes and are then passed through different noise channels and classified using deep learning models. We proposed a comprehensive evaluation of different 2D Convolutional Neural Network (CNN) architectures for the task of multiclass (24-classes) classification of modulated images in the presence of noise and fading. It is used to differentiate between images modulated through Binary Phase Shift Keying, Quadrature Phase Shift Keying, 16- and 64-Quadrature Amplitude Modulation and passed through Additive White Gaussian Noise, Rayleigh, and Rician channels. We obtained mixed results under different settings such as data augmentation, disharmony between batch normalization (BN), and dropout (DO), as well as lack of BN in the network. In this study, we found that the best performing model is a 2D-CNN model using disharmony between BN and DO techniques trained using 10-fold cross-validation (CV) with a small value of DO before softmax and after every convolution and fully connected layer along with BN layers in the presence of data augmentation, while the least performing model is the 2D-CNN model trained using 5-fold CV without augmentation.

中文翻译：

---

使用二维卷积神经网络对存在信道噪声的数字调制 COVID-19 图像进行分类

无线环境对信号的传播提出了重大挑战。多径散射、噪声、退化、失真、衰减和衰落等不同影响会对信号的分布产生不利影响。深度学习技术可用于区分不同的调制信号，以便在通信系统中进行可靠检测。本研究旨在区分由不同数字调制方案调制的 COVID-19 疾病图像，然后通过不同的噪声通道并使用深度学习模型进行分类。我们提出了对不同 2D 卷积神经网络 (CNN) 架构的综合评估，用于在存在噪声和衰落的情况下对调制图像进行多类（24 类）分类任务。它用于区分通过二元相移键控、正交相移键控、16 和 64 正交幅度调制调制的图像，以及通过加性高斯白噪声、瑞利和 Rician 通道传递的图像。我们在不同设置下获得了混合结果，例如数据增强、批量归一化 (BN) 和 dropout (DO) 之间的不协调以及网络中缺少 BN。在这项研究中，我们发现性能最好的模型是使用 BN 和 DO 技术之间不协调的 2D-CNN 模型，该模型使用 10 倍交叉验证 (CV) 进行训练，在 softmax 之前和每次卷积之后都具有较小的 DO 值并完全连接在存在数据增强的情况下将层与 BN 层一起使用，而性能最差的模型是使用 5 倍 CV 训练而没有增强的 2D-CNN 模型。