Artificial Neural Network-Based Digital Signal Modulation Recognition

This implementation focuses on digital signal modulation recognition using artificial neural networks, providing comprehensive recognition rate analysis. To enhance recognition performance, consider the following optimization approaches:

1. Increase Training Data Volume: Collect more digital signal modulation samples to improve neural network learning capacity. For code implementation, consider using data augmentation techniques like adding Gaussian noise, applying time-shifts, or generating synthetic modulation signals through MATLAB's Communication Toolbox functions such as pskmod(), qammod(), or fskmod().

2. Optimize Neural Network Architecture: Experiment with layer configurations and neuron counts to enhance fitting and generalization capabilities. In MATLAB, utilize the nntool or Deep Learning Toolbox to systematically test architectures using fitnet() for feedforward networks or patternnet() for classification tasks, adjusting parameters like hiddenLayerSizes and trainFcn.

3. Implement Advanced Neural Network Models: Employ deep learning architectures like Convolutional Neural Networks (CNN) for spectral feature extraction or Recurrent Neural Networks (RNN) for temporal pattern recognition. Code implementation could involve using MATLAB's convolutionalLayer() for CNN-based feature learning or lstmLayer() for RNN-based sequence analysis of signal patterns.

4. Feature Engineering: Extract comprehensive signal characteristics including spectral features (using FFT via fft() function), time-domain features (amplitude, phase statistics), and higher-order statistical moments. Implement feature selection algorithms like PCA (pca() function) or mutual information-based methods to identify the most discriminative features for modulation classification.

Through these enhancement strategies, the recognition rate of artificial neural network-based digital signal modulation identification can be significantly improved, leading to better performance in practical applications.