适用于 5G NR 的 AI
R2026a通过引入 Deep Learning Toolbox™ 功能探索适用于 5G NR 通信系统的深度学习工作流。
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Train Low Complexity CNN for 5G SISO Channel Estimation
Train convolutional neural network (CNN) to perform channel estimation in SISO mode using the PDSCH DM-RS.
Import TensorFlow Channel Feedback Compression Network and Deploy to GPU
Generate GPU specific C++ code for a pretrained TensorFlow™ channel state feedback autoencoder.
AI for Positioning Accuracy Enhancement
Use AI to estimate the position of user equipment and compare performance with traditional TDoA techniques.
Neural Network for Beam Selection
Use a neural network to reduce overhead in the beam selection task by using the location of the receiver rather than knowledge of the communication channels.
Train DQN Agent for Beam Selection
Train a deep Q-network (DQN) reinforcement learning agent for beam selection in a 5G new radio communications system.
Spectrum Sensing with Deep Learning to Identify 5G, LTE, and WLAN Signals
Train a semantic segmentation network using deep learning for spectrum monitoring.
Apply Transfer Learning on PyTorch Model to Identify 5G and LTE Signals
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.
Train PyTorch Channel Prediction Models
Train a PyTorch neural network for channel prediction by using data generated in MATLAB.
