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

TL;DR

This paper introduces the RF Challenge dataset and demonstrates that deep learning architectures like UNet and WaveNet significantly outperform traditional interference rejection methods in RF signal separation tasks.

Contribution

The paper presents a new RF dataset, develops deep learning models with domain-informed modifications, and benchmarks their performance against traditional methods for RF interference rejection.

Findings

Deep learning models outperform traditional methods by up to two orders of magnitude.

The same architectures can be trained and applied across different RF signal types.

Results support the potential of data-driven approaches for scalable RF interference mitigation.

Abstract

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).