CSI-Kompression und -Vorhersage
KI für CSI-Feedback-Kompression und Verbesserungen der CSI-Vorhersage
Diese Beispiele zeigen die KI-Techniken für CSI-Feedback-Kompression (Channel State Information) und Verbesserungen der CSI-Vorhersage bei drahtlosen 5G-Kommunikationssystemen. Verwenden Sie diese für einen Workflow, der Datengenerierung, Datenvorbereitung, tiefes neuronales Training, Kompression, Systemtest und Bereitstellung umfasst.
Themen
Einführung
- AI-Based CSI Feedback (5G Toolbox)
End-to-end workflow for examples exploring channel state information (CSI) feedback compression techniques using artificial intelligence (AI) in 5G wireless communication systems. (Seit R2026a)
Datengenerierung
- Generate MIMO OFDM Channel Realizations for AI-Based Systems (5G Toolbox)
Generate channel estimates to train an autoencoder for CSI feedback compression and temporal channel prediction. (Seit R2026a)
Datenvorbereitung
- Preprocess Data for AI-Based CSI Feedback Compression (5G Toolbox)
Preprocess channel estimates and prepare a data set to train an autoencoder for CSI feedback compression. (Seit R2025a) - Preprocess Data for AI Eigenvector-Based CSI Feedback Compression (5G Toolbox)
Preprocess channel estimates and prepare a data set to train an autoencoder for eigenvector based CSI feedback compression. (Seit R2026a) - Preprocess Data for AI-Based CSI Prediction (5G Toolbox)
Preprocess channel estimates and prepare a data set to train a gated recurrent unit (GRU) channel prediction network. (Seit R2026a)
Modelltraining
- Train Autoencoders for CSI Feedback Compression (5G Toolbox)
Compress CSI feedback using an autoencoder neural network in a 5G NR communications system. (Seit R2022b) - Train Transformer Autoencoder for Eigenvector-based CSI Feedback Compression (5G Toolbox)
Train an autoencoder neural network with a transformer backbone to compress downlink CSI over a clustered delay line (CDL) channel. (Seit R2026a) - CSI Feedback with Transformer Autoencoder (5G Toolbox)
Design and train a convolutional transformer deep neural network for CSI feedback by using a downlink clustered delay line (CDL) channel model. (Seit R2024b) - Optimize CSI Feedback Autoencoder Training Using MATLAB Parallel Server and Experiment Manager (5G Toolbox)
Accelerate determination of the optimal training hyperparameters for a CSI autoencoder by using a Cloud Center cluster and Experiment Manager. (Seit R2024a) - Offline Training and Testing of PyTorch Model for CSI Feedback Compression (5G Toolbox)
Train an autoencoder-based PyTorch® neural network offline and test for CSI compression. (Seit R2025a) - Online Training and Testing of PyTorch Model for CSI Feedback Compression (5G Toolbox)
Train an autoencoder-based PyTorch neural network online and test for CSI compression. (Seit R2025a) - Train PyTorch Channel Prediction Models (5G Toolbox)
Train a PyTorch neural network for channel prediction by using data generated in MATLAB®. (Seit R2025a) - Train PyTorch Channel Prediction Models with Online Training (5G Toolbox)
Enable real‐time adaptation to time‐varying wireless channels by generating each training batch in MATLAB on-the-fly to train a PyTorch GRU channel prediction network online. (Seit R2026a)
Modelltests
- Test AI-based CSI Compression Techniques for Enhanced PDSCH Throughput (5G Toolbox)
Measure physical downlink shared channel (PDSCH) throughput in a 5G New Radio (NR) system, with a primary focus on AI-based compression methods for CSI feedback. (Seit R2026a) - CSI Feedback Compression for 802.11be Using AI (WLAN Toolbox)
Use a k-means based AI/ML technique to compress CSI feedback in an 802.11be SU-MIMO beamforming scenario. (Seit R2025a)
Bereitstellung
- CSI Feedback with Autoencoders Implemented on an FPGA (Deep Learning HDL Toolbox)
This example demonstrates how to use an autoencoder neural network to compress downlink channel state information (CSI) over a clustered delay line (CDL) channel. (Seit R2024b)
