无线通信

使用无线通信系统应用扩展深度学习工作流

通过将 Deep Learning Toolbox™ 与 Communications Toolbox、5G Toolbox 和 WLAN Toolbox 结合使用，将深度学习应用于无线通信系统仿真。有关详细信息，请参阅AI for Wireless (Communications Toolbox)。

主题

AI for Positioning Accuracy Enhancement (5G Toolbox)
Use AI to estimate the position of user equipment and compare performance with traditional TDoA techniques. (自 R2024a 起)
Detect Human Presence Using Wireless Sensing with Deep Learning (WLAN Toolbox)
Perform wireless sensing to detect human presence using a CNN and the channel state information in wireless local area networks.
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.

精选示例

Data Preparation for Neural Network Digital Predistortion Design

Prepare data for training a neural network digital predistortion (DPD) system. The DPD is used to offset the effects of nonlinearities in a power amplifier (PA).

自 R2024a 起
打开实时脚本

Structurally Compress Neural Network DPD Using Projection

Structurally compress a neural network DPD to reduce computational complexity and memory requirements using projection and principal component analysis.

自 R2024a 起
打开实时脚本

Power Amplifier Modeling Using Neural Networks

Model a power amplifier (PA) using several different neural network (NN) architectures.

自 R2024a 起
打开实时脚本

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

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

打开实时脚本

Import TensorFlow Channel Feedback Compression Network and Deploy to GPU

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

打开实时脚本

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.

打开实时脚本

CSI Feedback with Autoencoders

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

打开实时脚本

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.

打开实时脚本

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.

打开实时脚本

Neural Network for Digital Predistortion Design - Online Training

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

打开实时脚本

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).

打开实时脚本

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.

打开实时脚本

Autoencoders for Wireless Communications

Model an end-to-end communications system with an autoencoder to reliably transmit information bits over a wireless channel.

打开实时脚本

使用深度学习进行调制分类

此示例说明如何使用卷积神经网络 (CNN) 进行调制分类。您将生成合成的、通道减损波形。使用生成的波形作为训练数据，训练 CNN 进行调制分类。然后用软件无线电 (SDR) 硬件和空口信号测试 CNN。

打开实时脚本

Modulation Classification by Using FPGA

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

打开实时脚本

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).

打开实时脚本

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.

打开实时脚本

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.

打开实时脚本

无线通信

主题

精选示例

WeChat