Neural Network-Based Signal Modulation Recognition Source Code

This repository provides a neural network source code implementation for signal modulation recognition. The solution employs deep learning techniques to automatically classify different modulation schemes in signal processing applications. Key implementation features include pre-processing modules for signal feature extraction, customizable neural network architectures (such as CNN or LSTM layers), and classification algorithms for identifying modulation types like QAM, PSK, and FSK. The codebase is structured with modular components for data loading, model training, and performance evaluation, including confusion matrix analysis and accuracy metrics. Researchers and engineers can download and utilize this implementation for signal analysis projects, with provided documentation covering parameter configuration and dataset formatting requirements. Neural networks serve as powerful tools for advanced signal processing, offering improved pattern recognition capabilities compared to traditional methods. This source code aims to facilitate research and experimentation in communication systems and digital signal processing domains.