Whether you use machine learning, deep learning, or reinforcement learning workflows, you can reduce development time with ready-to-use algorithms and data generated with MATLAB and wireless communications products. You can easily leverage existing deep learning networks outside MATLAB; streamline training, testing, and verification of your designs; and simplify deployment of your AI networks on embedded devices, enterprise systems, and the cloud.
With MATLAB, you can:
- Generate training data in the form of synthetic and over-the-air signals using the Wireless Waveform Generator app
- Augment signal space by adding RF impairments and channel models to your generated signals
- Label signals collected from wireless systems using the Signal Labeler app
- Apply reusable and streamlined training, simulation, and testing workflows to various wireless applications using the Deep Network Designer and Experiment Manager apps
- Add custom layers to your deep learning designs
Why Use AI for Wireless?
Spectrum Sensing and Signal Classification
Identify signals in a wideband spectrum using deep learning techniques. Perform waveform modulation classification using deep learning networks.
Develop radio frequency (RF) fingerprinting methods to identify various devices and detect device impersonators.
Apply neural network-based digital predistortion (DPD) to offset the effects of nonlinearities in a power amplifier (PA).
Beam Management and Channel Estimation
Use a neural network to reduce the computational complexity in the 5G NR beam selection task. Train a CNN for 5G NR channel estimation.
Localization and Positioning
Use generated IEEE® 802.11az™ data to train a CNN for localization and positioning.
Use an unsupervised neural network that learns how to efficiently compress and decompress data, forming an autoencoder. Train and test a neural network to estimate likelihood ratios (LLR).