# RF Challenge: The Data-Driven Radio Frequency Signal Separation Challenge

arXiv: https://arxiv.org/abs/2409.08839 · HTML: https://mlsanity.com/paper/2409.08839/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- ["Double-DIP": Unsupervised Image Decomposition via Coupled Deep-Image-Priors](https://mlsanity.com/paper/1812.00467/) (2018)
- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [Sudo rm -rf: Efficient Networks for Universal Audio Source Separation](https://mlsanity.com/paper/2007.06833/) (2020)
- [A Wavenet for Speech Denoising](https://mlsanity.com/paper/1706.07162/) (2017)
- [Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation](https://mlsanity.com/paper/1502.04149/) (2015)
- [A Survey on LoRa Networking: Research Problems, Current Solutions and Open Issues](https://mlsanity.com/paper/1908.10195/) (2019)
- [Speech Denoising in the Waveform Domain with Self-Attention](https://mlsanity.com/paper/2202.07790/) (2022)
- [Towards a Wireless Physical-Layer Foundation Model: Challenges and Strategies](https://mlsanity.com/paper/2403.12065/) (2024)
- [Interference Suppression Using Deep Learning: Current Approaches and Open Challenges](https://mlsanity.com/paper/2112.08988/) (2021)
- [Score-based Source Separation with Applications to Digital Communication Signals](https://mlsanity.com/paper/2306.14411/) (2023)
- [Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation](https://mlsanity.com/paper/2208.10325/) (2022)
- [Next Generation Wi-Fi and 5G NR-U in the 6 GHz Bands: Opportunities & Challenges](https://mlsanity.com/paper/2006.16534/) (2020)
- [Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals](https://mlsanity.com/paper/2209.04871/) (2022)
- [On Neural Architectures for Deep Learning-based Source Separation of Co-Channel OFDM Signals](https://mlsanity.com/paper/2303.06438/) (2023)
- [A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation](https://mlsanity.com/paper/2402.09461/) (2024)

## Built on by (most cited first, 6 of 6)

- [The Radio-Frequency Transformer for Signal Separation](https://mlsanity.com/paper/2603.09201/) (2026)
- [Advancing AI Challenges for the United States Department of the Air Force](https://mlsanity.com/paper/2511.00267/) (2025)
- [Applied AI-Enhanced RF Interference Rejection](https://mlsanity.com/paper/2604.22816/) (2026)
- [Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models](https://mlsanity.com/paper/2602.04650/) (2026)
- [Calibrated RF-Fingerprinting Under Interference With Heterogeneous Transmission Protocols](https://mlsanity.com/paper/2609.20765/) (2026)
- [FlowSep-RF: Rectified Flow Matching for Single-Channel Radio-Frequency Signal Separation](https://mlsanity.com/paper/2610.20269/) (2026)

## Abstract (arXiv)

We address the critical problem of interference rejection in radio-frequency (RF) signals using a data-driven approach that leverages deep-learning methods. A primary contribution of this paper is the introduction of the RF Challenge, which is a publicly available, diverse RF signal dataset for data-driven analyses of RF signal problems. Specifically, we adopt a simplified signal model for developing and analyzing interference rejection algorithms. For this signal model, we introduce a set of carefully chosen deep learning architectures, incorporating key domain-informed modifications alongside traditional benchmark solutions to establish baseline performance metrics for this intricate, ubiquitous problem. Through extensive simulations involving eight different signal mixture types, we demonstrate the superior performance (in some cases, by two orders of magnitude) of architectures such as UNet and WaveNet over traditional methods like matched filtering and linear minimum mean square error estimation. Our findings suggest that the data-driven approach can yield scalable solutions, in the sense that the same architectures may be similarly trained and deployed for different types of signals. Moreover, these findings further corroborate the promising potential of deep learning algorithms for enhancing communication systems, particularly via interference mitigation. This work also includes results from an open competition based on the RF Challenge, hosted at the 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'24).