# Deep-Learning-End-to-End-Digital-Modulation-Classification-and-Demodulation-System

**Repository Path**: YangNuoCheng/Deep-Learning-End-to-End-Digital-Modulation-Classification-and-Demodulation-System

## Basic Information

- **Project Name**: Deep-Learning-End-to-End-Digital-Modulation-Classification-and-Demodulation-System
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-04-01
- **Last Updated**: 2024-04-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Deep Learning End to End Digital Modulation Classification and Demodulation System
- Conducted data processing using MATLAB signal toolbox, which calculate cummulants and gen-
erate labeled QPSK, 8PSK, 16QAM train and test dataset (including different signal-to-noise
ratio)
- Built modulation recognition model using Multilayer Perceptron, for data with SNR above 15db,
the accuracy rate is close to 100%.
- Built demodulation model using Convolutional Neural Network and generated confusion matrix,
for data with SNR above 20db, the accuracy rate is close to 100%.
- Parameter tuning, using relu activation function, softmax crossentropy loss function, adam opti-
mization algorithm and adjusting learning rate, which increase the convergence speed and improve
classification accuracy.

- Cascaded two models to achieve end-to-end modulation and demodulation