AI, Positioning, and Sensing

Vertical applications of 802.11™ standards

Follow the examples to use WLAN Toolbox™ for vertical applications of the 802.11 standards. You can use deep learning, machine learning, and reinforcement learning to detect human presence, determine the position of stations, and detect router impersonation.

Topics

Deep Learning in MATLAB (Deep Learning Toolbox)
Discover deep learning capabilities in MATLAB^® using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.

Featured Examples

Detect Human Presence Using Wireless Sensing with Deep Learning

Perform wireless sensing to detect human presence using a CNN and the channel state information in wireless local area networks.

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Three-Dimensional Indoor Positioning with 802.11az Fingerprinting and Deep Learning

Train a convolutional neural network for IEEE^® 802.11az™ localization and positioning.

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802.11az Positioning Using Super-Resolution Time of Arrival Estimation

Calculate the position of a STA in an IEEE 802.11az network.

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802.11az Three-Dimensional Tracking Using Time of Arrival Estimation

Use an IEEE 802.11az Wi-Fi network to track Wi-Fi devices in a three-dimensional space using time of arrival (TOA) estimation.

(Phased Array System Toolbox)

Design a Deep Neural Network with Simulated Data to Detect WLAN Router Impersonation

Design and train an RF fingerprinting CNN with simulated data to detect WLAN router impersonation.

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Test a Deep Neural Network with Captured Data to Detect WLAN Router Impersonation

Train an RF fingerprinting CNN with data captured from an SDR to detect WLAN router impersonation.

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