Wireless Communications - MATLAB & Simulink (original) (raw)

Extend deep learning workflows with wireless communications system applications

Apply deep learning to wireless communications system simulations by using Deep Learning Toolbox™ together with Communications Toolbox, 5G Toolbox, WLAN Toolbox, and Bluetooth Toolbox. For more information, see AI for Wireless (Communications Toolbox).

Categories

• CSI Feedback
  AI for CSI feedback enhancements
• Beam Management
  AI for beam management
• Positioning and Sensing
  AI for positioning and sensing
• DPD and PA Modeling
  AI for digital predistortion and power amplifier modeling
• Autoencoding
  Autoencoder-based communications system design
• Spectrum Sensing and Modulation Classification
  AI for spectrum sensing and modulation classification
• Receiver Algorithms
  AI for various communications receiver algorithms
• Device Identification
  AI for device identification

Featured Examples

Bluetooth LE Positioning with Deep Learning

Compute the 3-D positioning of a Bluetooth LE node by using RSSI fingerprinting and a CNN.

(Bluetooth Toolbox)

• Since R2024b

AI for Positioning Accuracy Enhancement

Use AI to estimate the position of user equipment and compare performance with traditional TDoA techniques.

(5G Toolbox)

• Since R2024a

Structurally Compress Neural Network DPD Using Projection

Structurally compress a neural network DPD to reduce computational complexity and memory requirements using projection and principal component analysis.

(Communications Toolbox)

• Since R2024a

Power Amplifier Modeling Using Neural Networks

Model a power amplifier (PA) using several different neural network (NN) architectures.

(Communications Toolbox)

• Since R2024a

CSI Feedback with Autoencoders

Compress downlink channel state information (CSI) for 5G systems by using an autoencoder neural network.

(Communications Toolbox)

• Since R2022b