Channel Prediction
End-to-end workflow for examples exploring channel prediction techniques using artificial intelligence (AI) in wireless communication systems.
Wireless channel prediction is a crucial aspect of modern communication systems, enabling more efficient and reliable data transmission. Recent advancements in machine learning, particularly neural networks, have introduced a data-driven approach to wireless channel prediction. This approach does not rely on predefined models but instead learns directly from historical channel data. As a result, neural networks can adapt to realistic data, making them less sensitive to disturbances and interference.
Channel prediction using neural networks is fundamentally a time series learning problem since it involves forecasting future channel states based on past estimations. This method is particularly advantageous in environments where spatial correlation is minimal or absent, such as crowded urban areas with numerous moving objects. By focusing on temporal correlations and historical data, neural networks provide a computationally efficient and scalable solution across various environments.
This figure shows a workflow to highlight AI steps in a sequential set of examples.
These examples enable you to explore various workflow steps from data generation to hardware deployment, demonstrating the potential of AI-based neural networks for efficient and adaptive channel prediction in wireless networks.
Generate and prepare data:
Generate MIMO OFDM Channel Realizations for AI-Based Systems (5G Toolbox) — Generate synthetic data for training and testing a wireless network
Preprocess Data for AI-Based CSI Prediction (5G Toolbox) — Preprocess channel realizations for a channel prediction neural network.
Train Deep Learning Models in PyTorch®:
Train PyTorch Channel Prediction Models (5G Toolbox) — Use neural networks for channel prediction modeling.
Train PyTorch Channel Prediction Models with Online Training (5G Toolbox) — Train a PyTorch gated recurrent unit (GRU) channel prediction network online by generating each training batch in MATLAB® on-the-fly, enabling real‐time adaptation to time‐varying wireless channels.
Test and Verify Deep Learning Models in PyTorch:
Verify Performance of 6G AI-Native Receiver Using MATLAB and PyTorch Coexecution (5G Toolbox) — Test and verify a PyTorch AI-native receiver and compare it with a conventional 5G receiver using both multi-process central processing unit (CPU) and graphics processing unit (GPU) hardware acceleration.
