RFSS: A Comprehensive Multi-Standard RF Signal Source Separation Dataset with Advanced Channel Modeling
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912539965652992 |
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| author | Chen, Hao Jin, Rui Tan, Dayuan |
| author_facet | Chen, Hao Jin, Rui Tan, Dayuan |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_12106 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RFSS: A Comprehensive Multi-Standard RF Signal Source Separation Dataset with Advanced Channel Modeling Chen, Hao Jin, Rui Tan, Dayuan Signal Processing 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 |
| title | RFSS: A Comprehensive Multi-Standard RF Signal Source Separation Dataset with Advanced Channel Modeling |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2508.12106 |