频谱感知和调制分类
以下示例演示了可用于频谱感知和调制分类的 AI 技术。
精选示例
Spectrum Sensing with Deep Learning to Identify 5G, LTE, and WLAN Signals
Train a semantic segmentation network using deep learning for spectrum monitoring.
- 自 R2021b 起
- 打开实时脚本
Capture and Label NR and LTE Signals for AI Training
Scan, capture, and label bandwidths with 5G NR and LTE signals.
(Wireless Testbench)
- 自 R2023b 起
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)
- 自 R2023b 起
Modulation Classification with Deep Learning
Use a convolutional neural network (CNN) for modulation classification.
Modulation Classification by Using FPGA
Deploy a pretrained convolutional neural network (CNN) for modulation classification to the Xilinx® Zynq® UltraScale+™ MPSoC ZCU102 Evaluation Kit.
- 自 R2022b 起
- 打开实时脚本
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)
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