Receiver Algorithms

AI for various communications reception scenarios

These examples demonstrate AI techniques you can use for various reception scenarios.

Enthaltene Beispiele

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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Training and Testing a Neural Network for LLR Estimation

Generate signals and channel impairments to train a neural network, called LLRNet, to estimate exact log likelihood ratios (LLR).

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

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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Deep Learning Data Synthesis for 5G Channel Estimation

Generate deep learning training data for channel estimation using 5G Toolbox™.