Wireless Communications

AI for Wireless

Apply artificial intelligence (AI) techniques to wireless communications applications

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
AI for Wireless

Why Use AI for Wireless?

Using a neural network to identify 5G NR and LTE signals in a wideband spectrogram.

Spectrum Sensing and Signal Classification

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
Design a radio frequency (RF) fingerprinting convolutional neural network (CNN) with simulated data.

Device Identification

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
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.

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
Comparing 5G NR channel estimates based on either idealized estimation, linear interpolation, or deep learning techniques.

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
Comparing actual locations of objects in a room with color-coded locations predicted using CNNs.

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
Visualizing constellation plots of various autoencoders that converge to standard modulations such as Q P S K or 16 P S K.

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

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