RFSS: A Comprehensive Multi-Standard RF Signal Source Separation Dataset with Advanced Channel Modeling

TL;DR

This paper introduces RFSS, a large open-source dataset of multi-standard RF signals with advanced channel modeling, enabling improved research in RF source separation using deep learning.

Contribution

The paper provides a comprehensive, realistic RF signal dataset with multi-standard signals and advanced channel effects, facilitating reproducible research and development of new separation techniques.

Findings

CNN-LSTM achieves 26.7 dB SINR in source separation

Outperforms traditional ICA and NMF methods

Enables advanced research in RF signal processing

Abstract

The rapid evolution of wireless communication systems has created complex electromagnetic environments where multiple cellular standards (2G/3G/4G/5G) coexist, necessitating advanced signal source separation techniques. We present RFSS (RF Signal Source Separation), a comprehensive open-source dataset containing 52,847 realistic multi-standard RF signal samples with complete 3GPP standards compliance. Our framework generates authentic baseband signals for GSM, UMTS, LTE, and 5G NR with advanced channel modeling including multipath fading, MIMO processing up to 8 by 8 antennas, and realistic interference scenarios. Experimental validation demonstrates superior performance of CNN-LSTM architectures achieving 26.7 dB SINR improvement in source separation tasks, significantly outperforming traditional ICA (15.2 dB) and NMF (18.3 dB) approaches. The RFSS dataset enables reproducible research in RF source separation, cognitive radio, and machine learning applications while maintaining complete open-source accessibility