Digital Modulation Signal Recognition Using Artificial Neural Networks

This project focuses on implementing digital modulation signal recognition using artificial neural networks by establishing a Backpropagation (BP) network and performing simulations through MATLAB programming. Specifically, we will first analyze the characteristics and patterns of digital modulation signals, then design and construct a recognition system based on BP neural networks. The MATLAB implementation will involve key functions such as patternet for network creation, train for model training, and sim for signal recognition testing. We will extract features from modulation signals including amplitude, phase, and frequency characteristics as input vectors for the neural network. The system will be validated through extensive training and testing on large datasets of digital modulation signals to evaluate recognition accuracy and effectiveness. Furthermore, we will optimize network performance by adjusting network architecture parameters such as hidden layer numbers, neuron counts, and learning rates to achieve better recognition results. Through this project, we can gain deep insights into the application of artificial neural networks in digital signal processing and master the techniques for establishing and simulating BP networks, including practical implementation aspects like data preprocessing, network configuration, and performance evaluation metrics.