基于深度学习的无线信号实时识别

Christopher Gravelle, Todd Morehouse, Ruolin Zhou
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引用次数: 6

摘要

在这个演示中,我们执行了一个实时识别无线信号的软件定义无线电(SDR)原型。使用深度学习算法卷积神经网络(CNN)。通用软件无线电外设用作射频前端,用于传输和接收空中信号。使用MATLAB工具箱生成训练波形,建立学习模型,训练和测试识别和分类。数字调制类型的识别和分类在这个演示。同时,我们建议演示多载波波形和多进多出(MIMO)通信的分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning-Enabled Real-Time Recognition of Wireless Signals
In this demonstration, we perform a software defined radio (SDR) prototype of recognizing wireless signals in real-time. Convolutional Neural Network (CNN), a deep learning algorithm, is applied. Universal Software Radio Peripheral is used as RF front-end to transmit and receive over-the-air signals. MATLAB toolboxes are used to generate waveform for training, build learning models, train and test recognition and classification. Digital modulation types are recognized and classified in this demo. Meanwhile, we propose to demonstrate the classification of multi-carrier waveforms and multiple-in-multiple-out (MIMO) communications.
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