AI를 사용한 5G NR

5G 채널 추정을 위한 딥러닝 데이터 합성

5G Toolbox™를 사용하여 채널 추정을 위한 딥러닝 훈련 데이터를 생성합니다.

CSI Feedback with Autoencoders

Compress CSI feedback using an autoencoder neural network in a 5G NR communications system.

CSI Feedback with Transformer Autoencoder

Design and train a convolutional transformer deep neural network for channel state information feedback by using a downlink clustered delay line (CDL) channel model.

Prepare Data for CSI Processing

Generate channel estimates and prepare a data set to train an autoencoder for channel state information (CSI) feedback compression.

CSI Feedback with Autoencoders Implemented on an FPGA

Demonstrates how to use an autoencoder neural network to compress downlink channel state information (CSI) over a clustered delay line (CDL) channel. CSI feedback is in the form of a raw channel estimate array. In this example, the autoencoder network is implemented on an FPGA using the Deep Learning HDL Toolbox™.

(Deep Learning HDL Toolbox)

Optimize CSI Feedback Autoencoder Training Using MATLAB Parallel Server and Experiment Manager

Accelerate determination of the optimal training hyperparameters for a channel state information (CSI) autoencoder by using a Cloud Center cluster and Experiment Manager.

Online Training and Testing of PyTorch Model for CSI Feedback Compression

Train an autoencoder-based PyTorch^® neural network online and test for CSI compression.

Offline Training and Testing of PyTorch Model for CSI Feedback Compression

Train an autoencoder-based PyTorch neural network offline and test for CSI compression.

Import TensorFlow Channel Feedback Compression Network and Deploy to GPU

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

AI for Positioning Accuracy Enhancement

Use AI to estimate the position of user equipment and compare performance with traditional TDoA techniques.

Neural Network for Beam Selection

Reduce the overhead of beam selection by using the receiver location rather than knowledge of the communication channels.

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.

Spectrum Sensing with Deep Learning to Identify 5G, LTE, and WLAN Signals

Train a semantic segmentation network using deep learning for spectrum monitoring.

Apply Transfer Learning on PyTorch Model to Identify 5G and LTE Signals

Coexecution with Python to identify 5G NR and LTE signals by using the transfer learning technique on a pre-trained PyTorch™ semantic segmentation network for spectrum sensing.

Train PyTorch Channel Prediction Models

Train PyTorch-based channel prediction neural networks using data generated in MATLAB^®.