딥러닝을 사용한 무선 통신

무선 통신 시스템 응용 분야에서 딥러닝 워크플로 확장

Deep Learning Toolbox™를 Communications Toolbox, 5G Toolbox 및 WLAN Toolbox와 함께 사용하여 무선 통신 시스템 시뮬레이션에 딥러닝을 적용합니다. 신호 처리 응용 분야에 대해서는 딥러닝을 사용한 신호 처리 항목을 참조하십시오.

도움말 항목

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.

추천 예제

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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딥러닝을 사용한 변조 분류

이 예제에서는 변조 분류를 수행하는 컨벌루션 신경망(CNN)을 사용하는 방법을 보여줍니다. 여기서는 채널이 손상된 합성 파형을 생성해 봅니다. 생성된 파형을 훈련 데이터로 사용하여 변조 분류를 수행하는 CNN을 훈련시킵니다. 그런 다음 소프트웨어 정의 무선 통신(SDR) 하드웨어와 OTA 신호를 사용하여 CNN을 테스트합니다.

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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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