Receiver Algorithms
R2026bThese examples demonstrate AI techniques you can use for various receiver algorithms.
Featured Examples
Train and Evaluate Neural Networks for 5G MIMO Channel Estimation
Train and compare three neural network architectures for channel estimation in a 5G NR MIMO-OFDM downlink.
Verify Performance of 6G AI-Native Receiver Using MATLAB and PyTorch Coexecution
Integrate a trained PyTorch® network with MATLAB®-based data generation to simulate an AI-native air interface.
AI-Native Fully Convolutional Receiver
Use a convolutional neural network to replace conventional channel estimation, equalization, and symbol demodulation.
Training and Testing a Neural Network for LLR Estimation
Generate signals and channel impairments to train a neural network, called LLRNet, to estimate exact log likelihood ratios (LLR).
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.
