AI를 사용한 무선 통신

무선 통신 시스템에서 머신러닝, 딥러닝 및 강화 학습 사용

무선 통신 시스템에서 Deep Learning Toolbox™ 기능을 사용하여 수신 알고리즘을 훈련시킬 수 있습니다.

함수

dlarray 지원

`ofdmmod`	Modulate using OFDM method
`ofdmdemod`	Demodulate using OFDM method

데이터저장소 및 데이터 가져오기

signalDatastore Datastore for collection of signals (R2020a 이후)

추천 예제

Custom Training Loops and Loss Functions for AI-Based Wireless Systems

Train an autoencoder as an end-to-end simulation system.

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Import TensorFlow Channel Feedback Compression Network and Deploy to GPU

Generate GPU specific C++ code for a pretrained TensorFlow™ channel state feedback autoencoder.

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OFDM Autoencoder for Wireless Communications

Model an end-to-end orthogonal frequency division modulation (OFDM) communications system with an autoencoder to reliably transmit information bits over a wireless channel.

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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 - Online Training

Use a neural network, that is trained online, to apply digital predistortion to offset the effects of nonlinearities in a power amplifier.

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Neural Network for Digital Predistortion Design-Offline Training

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

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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.

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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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Three-Dimensional Indoor Positioning with 802.11az Fingerprinting and Deep Learning

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

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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