AI for 5G NR

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.

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.

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.

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.