Wireless Communications Using Deep Learning

Extend deep learning workflows with wireless communications system applications

Apply deep learning to wireless communications system simulations by using Deep Learning Toolbox™ together withCommunications Toolbox,5G Toolbox, andWLAN Toolbox。有关信号处理应用程序，请参阅Signal Processing Using Deep Learning。

话题

Deep Learning Data Synthesis for 5G Channel Estimation(5G Toolbox)
Generate deep learning training data for channel estimation using 5G Toolbox™.
Three-Dimensional Indoor Positioning with 802.11az Fingerprinting and Deep Learning(WLAN Toolbox)
培训IEEE的卷积神经网络^®802.11az™ localization and positioning.

Featured Examples

数字预性设计的神经网络 - 离线培训

使用经过离线训练的神经网络（NN）应用数字预性（DPD）来抵消功率放大器（PA）中非线性的影响。

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神经网络对梁的选择

Use a neural network to reduce overhead in the beam selection task by using the location of the receiver rather than knowledge of the communication channels.

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Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals

Train a semantic segmentation network using deep learning for spectrum monitoring. One of the uses of spectrum monitoring is to characterize spectrum occupancy. The neural network in this example is trained to identify 5G NR and LTE signals in a wideband spectrogram.

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无线通信的自动编码器

Model an end-to-end communications system with an autoencoder to reliably transmit information bits over a wireless channel.

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Modulation Classification with Deep Learning

Use a convolutional neural network (CNN) for modulation classification. You generate synthetic, channel-impaired waveforms. Using the generated waveforms as training data, you train a CNN for modulation classification. You then test the CNN with software-defined radio (SDR) hardware and over-the-air signals.

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Training and Testing a Neural Network for LLR Estimation

产生信号和渠道障碍，以训练称为LLRNET的神经网络，以估算精确的对数似然比（LLR）。

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Design a Deep Neural Network with Simulated Data to Detect WLAN Router Impersonation

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.

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Test a Deep Neural Network with Captured Data to Detect WLAN Router Impersonation

使用捕获的数据训练射频（RF）指纹卷积神经网络（CNN）。您可以使用软件定义的无线电（SDR）从真实路由器中捕获无线局域网（WLAN）标准框架。您为第二个SDR编程以传输未知的信标框架并捕获它们。您使用这些捕获的信号训练CNN。然后，您将软件定义的无线电（SDR）编程为路由器的模仿者，该路由器的模仿者使用媒体访问控制（MAC）的一个已知路由器的地址传输信号信号，并使用CNN将其识别为模仿者。

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