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Télécommunications avec Deep Learning

Enrichir des workflows de Deep Learning avec des applications de systèmes de télécommunications

Appliquez le Deep Learning à des simulations de systèmes de télécommunications en utilisant Deep Learning Toolbox™ avec Communications Toolbox, 5G Toolbox et WLAN Toolbox. Pour des applications de traitement du signal, veuillez consulter Traitement du signal avec Deep Learning.

Rubriques

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)
Train a convolutional neural network for IEEE^® 802.11az™ localization and positioning.

Exemples présentés

CSI Feedback with Autoencoders

Compress downlink channel state information (CSI) for 5G systems by using an autoencoder neural network.

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Train DQN Agent for Beam Selection

Train a deep Q-network (DQN) reinforcement learning agent for beam selection in a 5G new radio communications system.

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Neural Network for Beam Selection

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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Neural Network for Digital Predistortion Design-Offline Training

Use a neural network (NN), that is trained offline, to apply digital predistortion (DPD) to offset the effects of nonlinearities in a power amplifier (PA).

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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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Autoencoders for Wireless Communications

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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Modulation Classification by Using FPGA

Deploy a pretrained convolutional neural network (CNN) for modulation classification to the Xilinx^® Zynq^® UltraScale+™ MPSoC ZCU102 Evaluation Kit.

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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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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

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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