AI for 5G NR
Explore deep learning workflows for 5G NR communications systems by incorporating Deep Learning Toolbox™ features.
Featured Examples
Deep Learning Data Synthesis for 5G Channel Estimation
Generate deep learning training data for channel estimation using 5G Toolbox™.
CSI Feedback with Autoencoders
Compress CSI feedback using an autoencoder neural network in a 5G NR communications system.
NR PDSCH Throughput Using Channel State Information Feedback
Measure the PDSCH throughput of a 5G NR adaptive link that adjusts the MCS, precoding matrix, and number of layers based on the CSI feedback.
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.
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.
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 and LTE Signals
Train a semantic segmentation network using deep learning for spectrum monitoring.
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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