CSI 圧縮と予測
CSI フィードバック圧縮および CSI 予測強化のための AI
これらの例は、5G 無線通信システムにおけるチャネル状態情報 (CSI) フィードバック圧縮および CSI 予測強化のための AI 手法を示しています。これらを使用して、データ生成、データ準備、深層ニューラル学習、圧縮、システム テスト、および展開を含むワークフローを段階的に進めます。
トピック
はじめに
- 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. (R2026a 以降)
データの生成
- 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. (R2026a 以降)
データの準備
- 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. (R2025a 以降) - 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. (R2026a 以降) - 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. (R2026a 以降)
モデルの学習
- 自己符号化器を使った CSI フィードバック (5G Toolbox)
5G NR 通信システムで自己符号化器ニューラル ネットワークを使用して CSI フィードバックを圧縮する。 - 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. (R2026a 以降) - 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. (R2024b 以降) - 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. (R2024a 以降) - 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. (R2025a 以降) - 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. (R2025a 以降) - Train PyTorch Channel Prediction Models (5G Toolbox)
Train a PyTorch neural network for channel prediction by using data generated in MATLAB®. (R2025a 以降) - 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. (R2026a 以降)
モデルのテスト
- 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. (R2026a 以降) - 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. (R2025a 以降)
展開
- 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. (R2024b 以降)
