MATLAB-Simulated Dataset for Automatic Modulation Classification in Wireless Fading Channels

TL;DR

This paper introduces a comprehensive MATLAB-simulated dataset for automatic modulation classification in wireless fading channels, aiding machine learning research in dynamic communication environments.

Contribution

It provides a labeled, synthetic dataset with diverse modulation schemes, channel conditions, and features, along with MATLAB scripts for reproducibility and benchmarking in wireless communication research.

Findings

Dataset covers five modulation schemes and two channel types.

Features include statistical, time, frequency, spectrogram, and image descriptors.

Scripts enable reproducibility and facilitate model development.

Abstract

Accurate modulation classification is a core challenge in cognitive radio, adaptive communications, spectrum analysis, and related domains, especially under dynamic channels without transmitter knowledge. To address this need, this article presents a labeled synthetic dataset designed for wireless modulation classification under realistic propagation scenarios. The signals were generated in MATLAB by modulating randomly generated bitstreams using five digital modulation schemes: BPSK, QPSK, 16-QAM, 64-QAM, and 256-QAM. These signals were then transmitted through Rayleigh and Rician fading channels with standardized parameters, along with additional impairments to enhance realism and diversity. Each modulated signal contains 1000 symbols. A comprehensive set of features was extracted from the signals, encompassing statistical, time-domain, frequency-domain, spectrogram-based, spectral correlation-based, and image-processing-based descriptors such as BRISK, MSER, and GLCM. The dataset is organized into 10 CSV files covering two channel types (Rayleigh and Rician) across five sampling frequencies: 1 MHz, 10 MHz, 100 MHz, 500 MHz, and 1 GHz. To facilitate reproducibility and encourage further experimentation, the MATLAB scripts used for signal generation and feature extraction are also provided. This dataset serves as a valuable benchmark for developing and evaluating machine learning models in modulation classification, signal identification, and wireless communication research.