{"ID":2851906,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.19985","arxiv_id":"2510.19985","title":"MATLAB-Simulated Dataset for Automatic Modulation Classification in Wireless Fading Channels","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.","short_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 classifica...","url_abs":"https://arxiv.org/abs/2510.19985","url_pdf":"https://arxiv.org/pdf/2510.19985v1","authors":"[\"M. M. Sadman Shafi\",\"Tasnia Siddiqua Ahona\",\"Ashraful Islam Mridha\"]","published":"2025-10-22T19:33:44Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}