AI for Wireless
R2026bMachine learning, deep learning, and reinforcement learning in wireless
communications systems
Implement AI workflows for wireless communications systems. Use Deep Learning Toolbox™, Statistics and Machine Learning Toolbox™, and Reinforcement Learning Toolbox™ to incorporate AI in wireless applications.
Related Information
Categories
- Functionality for AI
Functions and objects that support deep learning workflows for wireless applications
- Applications
Incorporate AI techniques into wireless applications
- Python with MATLAB for Wireless
AI for wireless directly calling Python® library functionality from MATLAB® or write Python programs that call MATLAB functionality
- End-to-End AI Workflows
Use deep learning in wireless end-to-end tasks including data preparation, deep neural network training, compression, network testing and verification, and deployment
Featured Examples
AI for Positioning Accuracy Enhancement
Use AI to estimate the position of user equipment and compare performance with traditional TDoA techniques.
Structurally Compress Neural Network DPD Using Projection
Structurally compress a neural network DPD to reduce computational complexity and memory requirements using projection and principal component analysis.
Power Amplifier Modeling Using Neural Networks
Model a power amplifier (PA) using several different neural network (NN) architectures.
Train Autoencoders for CSI Feedback Compression
Compress downlink CSI feedback for 5G systems by using an autoencoder neural network.
