AI for 5G NR
Explore deep learning workflows for 5G NR communications systems by incorporating Deep Learning Toolbox™ features.
Categories
- CSI Compression and Prediction
AI for CSI feedback compression and CSI prediction enhancements
- Beam Management
AI for beam management
- Positioning and Sensing
AI for positioning and sensing
- Spectrum Sensing
AI for spectrum sensing
- Receiver Algorithms
AI-native receivers, channel estimation, and other receiver components
- Python with MATLAB
AI for wireless directly call Python® library functionality from MATLAB®
Related Information
Featured Examples
Deep Learning Data Synthesis for 5G Channel Estimation
Generate deep learning training data for channel estimation using 5G Toolbox™.
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
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.
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 a PyTorch® neural network for channel prediction by using data generated in MATLAB.
