CSI 압축 및 예측
AI를 사용한 CSI 피드백 압축 및 CSI 예측 향상
다음 예제들은 5G 무선 통신 시스템에서 CSI(채널 상태 정보) 피드백 압축과 CSI 예측 성능 개선을 위한 AI 기술을 보여줍니다. 데이터 생성과 준비부터 심층 신경망 훈련, 압축, 시스템 테스트 및 배포에 이르는 워크플로를 단계적으로 실행해 볼 수 있습니다.
도움말 항목
소개
- 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 이후)
데이터 생성
- 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 이후)
데이터 준비
- 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 이후) - 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 이후) - 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 이후)
모델 훈련
- Train Autoencoders for CSI Feedback Compression (5G Toolbox)
Compress CSI feedback using an autoencoder neural network in a 5G NR communications system. (R2022b 이후) - 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 이후) - 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 이후) - 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 이후) - 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 이후) - 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 이후) - Train PyTorch Channel Prediction Models (5G Toolbox)
Train a PyTorch neural network for channel prediction by using data generated in MATLAB®. (R2025a 이후) - 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 이후)
모델 테스트
- 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 이후) - 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 이후)
배포
- 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 이후)
