MATLAB-Simulated Dataset for Automatic Modulation Classification in Wireless Fading Channels (Ver 2)

These features provide a comprehensive representation of modulation characteristics for classification and analysis. 2. Spectrogram-Based Features – Derived from the time-frequency representation of signals using STFT. Statistical measures (entropy, variance, skewness, kurtosis), spectral attributes (centroid, spread, flatness), and amplitude-based features (contrast, homogeneity, range) were extracted to quantify spectral energy distribution and signal consistency. 3.

Image Processing-Based Features – Utilizing Binary Robust Invariant Scalable Keypoints (BRISK), Maximally Stable Extremal Regions (MSER), and Gray-Level Co-occurrence Matrix (GLCM) techniques to analyze spectrogram-based signal representations. Each dataset file contains 202 extracted features, providing a high-dimensional numerical representation of modulation signals. The dataset holds significant value across multiple domains, particularly in machine learning and deep learning, where it facilitates modulation classification using ML/DL models to develop robust classifiers for wireless communication systems.

In wireless signal processing, researchers can utilize the extracted features for channel estimation, adaptive modulation, and interference analysis. Additionally, the dataset is highly relevant for cognitive radio and LTE/5G research, supporting spectrum sensing, signal recognition, and intelligent radio technologies. It also serves as a benchmarking resource for testing feature selection methods, dimensionality reduction techniques, and classification algorithms, making it a valuable asset for both academic and industrial applications.