Spectrum Sensing and Modulation Classification - MATLAB & Simulink (original) (raw)

AI for spectrum sensing and modulation classification

These examples demonstrate AI techniques you can use for spectrum sensing and modulation classification.

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Apply Transfer Learning on PyTorch Model to Identify 5G and LTE Signals

Coexecution with Python to identify 5G NR and LTE signals by using the transfer learning technique on a pre-trained PyTorch™ semantic segmentation network for spectrum sensing.

(Communications Toolbox)

• Since R2025a

Spectrum Sensing with Deep Learning to Identify 5G, LTE, and WLAN Signals

Train a semantic segmentation network using deep learning for spectrum monitoring.

(Communications Toolbox)

• Since R2021b

Capture and Label NR and LTE Signals for AI Training

Scan, capture, and label bandwidths with 5G NR and LTE signals.

(Wireless Testbench)

• Since R2023b

Identify LTE and NR Signals from Captured Data Using SDR and Deep Learning

Use a spectrum sensing neural network to identify LTE and NR signals from wireless data you capture over the air.

(Wireless Testbench)

• Since R2023b

Modulation Classification by Using FPGA

Deploy a pretrained convolutional neural network (CNN) for modulation classification to the Xilinx® Zynq® UltraScale+™ MPSoC ZCU102 Evaluation Kit.

(Communications Toolbox)

• Since R2022b

Modulation Classification with Deep Learning

Use a convolutional neural network (CNN) for modulation classification.

(Communications Toolbox)

• Since R2020b

Radar and Communications Waveform Classification Using Deep Learning

Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

(Phased Array System Toolbox)