通过将 Deep Learning Toolbox™ 与 Communications Toolbox、5G Toolbox 和 WLAN Toolbox 结合使用，将深度学习应用于无线通信系统仿真。有关信号处理应用，请参阅深度学习在信号处理领域的应用。
Deep Learning Data Synthesis for 5G Channel Estimation (5G Toolbox)
Generate deep learning training data for channel estimation using 5G Toolbox™.
Model an end-to-end communications system with an autoencoder to reliably transmit information bits over a wireless channel.
此示例说明如何使用卷积神经网络 (CNN) 进行调制分类。您将生成合成的、通道减损波形。使用生成的波形作为训练数据，训练 CNN 进行调制分类。然后用软件定义的无线电 (SDR) 硬件和无线信号测试 CNN。
Generate signals and channel impairments to train a neural network, called LLRNet, to estimate exact log likelihood ratios (LLR).
Design a radio frequency (RF) fingerprinting convolutional neural network (CNN) with simulated data. You train the CNN with simulated wireless local area network (WLAN) beacon frames from known and unknown routers for RF fingerprinting. You then compare the media access control (MAC) address of received signals and the RF fingerprint detected by the CNN to detect WLAN router impersonators.
Train a radio frequency (RF) fingerprinting convolutional neural network (CNN) with captured data. You capture wireless local area network (WLAN) beacon frames from real routers using a software defined radio (SDR). You program a second SDR to transmit unknown beacon frames and capture them. You train the CNN using these captured signals. You then program a software-defined radio (SDR) as a router impersonator that transmits beacon signals with the media access control (MAC) address of one of the known routers and use the CNN to identify it as an impersonator.
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