MATLAB과 함께 사용하는 Python

무선 통신용 AI가 MATLAB^®에서 Python^® 라이브러리 기능을 직접 호출함

MATLAB에서 Python을 직접 호출하여, 다른 딥러닝 프레임워크에서 작업하는 동료들과 협업해 PyTorch^®, TensorFlow™ 또는 ONNX™ 모델을 훈련시키고 테스트합니다. 함수를 가져오고 내보낼 수도 있습니다.

워크플로 단계에는 데이터 생성, 데이터 준비, 심층 신경망 모델 훈련, 모델 압축, 모델 테스트 및 모델 배포가 포함됩니다.

다음 예제들은 5G 무선 통신 시스템에서 인공 지능(AI)을 활용한 채널 상태 정보(CSI) 피드백 압축, CSI 예측 기법을 위한 PyTorch 모델 실행 과정을 보여주기 위해 훈련, 테스트, 배포 워크플로 단계에 중점을 두고 있습니다.

도움말 항목

Call Python from MATLAB for Wireless
AI for wireless workflows calling Python from MATLAB to run PyTorch or TensorFlow models. (R2025a 이후)
PyTorch Wrapper Template
You can use your own PyTorch models in MATLAB by using the Python interface.

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)
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)

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)
Apply Transfer Learning on PyTorch Model to Identify 5G and LTE Signals (5G Toolbox)
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. (R2025a 이후)
Verify Performance of 6G AI-Native Receiver Using MATLAB and PyTorch Coexecution (5G Toolbox)
Integrate a trained PyTorch network with MATLAB-based data generation to simulate an AI-native air interface. (R2025a 이후)

Import TensorFlow Channel Feedback Compression Network and Deploy to GPU (5G Toolbox)
Generate GPU specific C++ code for a pretrained TensorFlow channel state feedback autoencoder. (R2023b 이후)