A software‐defined radio testbed for deep learning‐based automatic modulation classification,International Journal of Communication Systems

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A software‐defined radio testbed for deep learning‐based automatic modulation classification
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-07-25 , DOI: 10.1002/dac.4556
Sowjanya Ponnaluru ₁ , Satyanarayana Penke ₁

Affiliation

Automatic modulation classification (AMC) is the demodulation process on the receiver side, which is a crucial protocol for current and next‐generation intelligent communication systems. This method becomes complicated, in the presence of channel noise, to identify the modulation of the transmitted signal, that is, the transmitter and receiver with its ambiguous parameters like timing information, signal strength, phase offset, and carrier frequency. Two fundamental approaches are used for the AMC, namely, the signal statistical feature‐based approach and the maximum likelihood approach. Current Feature‐Based AMC approaches typically built for a limited set of modulation; a comprehensive AMC approach utilizing convolutional neural networks (CNN) is suggested in this article to overcome this obstacle. Altogether, 11 different types of modulations considered. In this method, without an extraction function, the transmitted signal can be identified directly. Also, the features of the received signal are known directly by using this method. The classification accuracy using CNN seems to be remarkable, especially for low SNRs. In this article, a realistic AMC framework that can be quickly applied to provide reliable efficiency in numerous commercial real‐time scenarios has developed and tested. Therefore, to prove the functional viability of our proposed model, it was applied to the software‐defined radio test‐bed.

中文翻译：

一个软件定义的无线电测试平台，用于基于深度学习的自动调制分类

自动调制分类（AMC）是接收机端的解调过程，它是当前和下一代智能通信系统的关键协议。在存在信道噪声的情况下，此方法变得很复杂，无法识别发送信号的调制，即发送器和接收器及其模棱两可的参数，例如定时信息，信号强度，相位偏移和载波频率。AMC使用了两种基本方法，即基于信号统计特征的方法和最大似然方法。当前基于特征的AMC方法通常是为有限的一组调制而构建的；本文提出了一种使用卷积神经网络（CNN）的综合AMC方法来克服这一障碍。共，考虑了11种不同类型的调制。在这种方法中，没有提取功能，可以直接识别发送的信号。同样，通过使用此方法可以直接知道接收信号的特征。使用CNN的分类准确性似乎非常出色，尤其是对于低SNR。在本文中，已经开发并测试了一个现实的AMC框架，该框架可以快速应用以在众多商业实时场景中提供可靠的效率。因此，为了证明我们提出的模型的功能可行性，将其应用于软件定义的无线电测试台。使用CNN的分类准确性似乎非常出色，尤其是对于低SNR。在本文中，已经开发并测试了一个现实的AMC框架，该框架可以快速应用以在众多商业实时场景中提供可靠的效率。因此，为了证明我们提出的模型的功能可行性，将其应用于软件定义的无线电测试台。使用CNN的分类准确性似乎非常出色，尤其是对于低SNR。在本文中，已经开发并测试了一个现实的AMC框架，该框架可以快速应用以在众多商业实时场景中提供可靠的效率。因此，为了证明我们提出的模型的功能可行性，将其应用于软件定义的无线电测试台。

更新日期：2020-07-25

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