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		Device Identification
Identify signals in a wideband spectrum using deep learning techniques. Perform waveform modulation classification using deep learning networks. 💡 Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals 💡 Modulation Classification with Deep Learning		Develop radio frequency (RF) fingerprinting methods to identify various devices and detect device impersonators. 💡 Design a Deep Neural Network with Simulated Data to Detect WLAN Router Impersonation 💡 Test a Deep Neural Network with Captured Data to Detect WLAN Router Impersonation


Digital Pre-Distortion Apply neural network-based digital predistortion (DPD) to offset the effects of nonlinearities in a power amplifier (PA). 💡 Neural Network for Digital Predistortion Design – Offline Training		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. 💡 Neural Network for Beam Selection 💡 Deep Learning Data Synthesis for 5G Channel Estimation


Localization and Positioning Use generated IEEE ^® 802.11az™ data to train a CNN for localization and positioning. 💡 Three-Dimensional Indoor Positioning with 802.11az Fingerprinting and Deep Learning		Transceiver Design 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). 📖 Autoencoders for Wireless Communications 📖 Training and Testing a Neural Network for LLR Estimation