Compressione e previsione CSI
IA per la compressione del feedback CSI e il miglioramento della previsione CSI
Questi esempi illustrano tecniche di IA per la compressione del feedback delle informazioni sullo stato del canale (CSI) e per il miglioramento della previsione CSI nei sistemi di comunicazione wireless 5G. Utilizzarle per seguire passo dopo passo un workflow che comprende la generazione di dati, la loro preparazione, l'addestramento delle reti neurali profonde, la compressione, i test di sistema e la distribuzione.
Argomenti
Introduzione
- 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. (Da R2026a)
Generazione di dati
- 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. (Da R2026a)
Preparazione dei dati
- 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. (Da 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. (Da 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. (Da R2026a)
Addestramento del modello
- Train Autoencoders for CSI Feedback Compression (5G Toolbox)
Compress CSI feedback using an autoencoder neural network in a 5G NR communications system. (Da 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. (Da 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. (Da 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. (Da 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. (Da 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. (Da R2025a) - Train PyTorch Channel Prediction Models (5G Toolbox)
Train a PyTorch neural network for channel prediction by using data generated in MATLAB®. (Da 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. (Da R2026a)
Test del modello
- 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. (Da 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. (Da R2025a)
Distribuzione
- 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. (Da R2024b)
