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?
![Using a neural network to identify 5G NR and LTE signals in a wideband spectrogram.](https://in.mathworks.com/solutions/wireless-communications/ai/_jcr_content/mainParsys/band_copy/mainParsys/columns_copy/507537ca-afa8-41c5-806f-bbdd06667040/image_copy_copy.adapt.full.medium.jpg/1737544574015.jpg)
Spectrum Sensing and Signal Classification
Identify signals in a wideband spectrum using deep learning techniques. Perform waveform modulation classification using deep learning networks.
![Design a radio frequency (RF) fingerprinting convolutional neural network (CNN) with simulated data.](https://in.mathworks.com/solutions/wireless-communications/ai/_jcr_content/mainParsys/band_copy/mainParsys/columns_copy/b827a46e-7d00-424f-81d8-b611fc9edab9/image_copy.adapt.full.medium.jpg/1737544574071.jpg)
Device Identification
Develop radio frequency (RF) fingerprinting methods to identify various devices and detect device impersonators.
![A screenshot of a spectrum analyzer shows that the performance characteristics change when the power amplifier (P A) heats, which creates a visual plot system as a function of time.](https://in.mathworks.com/solutions/wireless-communications/ai/_jcr_content/mainParsys/band_copy/mainParsys/columns_copy_copy/507537ca-afa8-41c5-806f-bbdd06667040/image_copy_copy.adapt.full.medium.jpg/1737544574164.jpg)
Digital Pre-Distortion
Apply neural network-based digital predistortion (DPD) to offset the effects of nonlinearities in a power amplifier (PA).
![Comparing 5G NR channel estimates based on either idealized estimation, linear interpolation, or deep learning techniques.](https://in.mathworks.com/solutions/wireless-communications/ai/_jcr_content/mainParsys/band_copy/mainParsys/columns_copy_copy/b827a46e-7d00-424f-81d8-b611fc9edab9/image_copy_copy_copy.adapt.full.medium.jpg/1737544574213.jpg)
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.
![Comparing actual locations of objects in a room with color-coded locations predicted using CNNs.](https://in.mathworks.com/solutions/wireless-communications/ai/_jcr_content/mainParsys/band_copy/mainParsys/columns_copy_copy_co/507537ca-afa8-41c5-806f-bbdd06667040/image_copy_copy.adapt.full.medium.jpg/1737544574369.jpg)
Localization and Positioning
Use generated IEEE® 802.11az™ data to train a CNN for localization and positioning.
![Visualizing constellation plots of various autoencoders that converge to standard modulations such as Q P S K or 16 P S K.](https://in.mathworks.com/solutions/wireless-communications/ai/_jcr_content/mainParsys/band_copy/mainParsys/columns_copy_copy_co/b827a46e-7d00-424f-81d8-b611fc9edab9/image_copy.adapt.full.medium.jpg/1737544574433.jpg)
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).