Apply deep learning to wireless communications system simulations by using
Deep Learning Toolbox™ together with Communications Toolbox, 5G Toolbox, and WLAN Toolbox. For signal processing applications, see
Signal Processing Using Deep Learning.
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.
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.
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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