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

2024-09-13

arXiv.org

AI Summary

Suppressing diverse interference types in radio-frequency (RF) signals remains challenging due to the absence of standardized, publicly available evaluation benchmarks. Method: We introduce RF Challenge—the first open, diverse benchmark dataset for RF signal separation—and propose a domain-aware UNet/WaveNet hybrid architecture that jointly models time-frequency structure and channel-specific characteristics. Our approach is grounded in a simplified physical model, integrating controlled signal synthesis and simulation-based interference generation, with matched filtering and linear minimum mean-square error (LMMSE) estimation serving as classical baselines. Contribution/Results: Evaluated across eight composite interference scenarios, our deep learning method achieves up to two orders-of-magnitude performance improvement over traditional techniques. The benchmark and methodology underpin the ICASSP’24 RF Signal Separation Challenge, demonstrating strong generalization and practical efficacy for interference suppression across heterogeneous signal types.

Technology Category

• Machine Learning: Multimodal Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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).

Problem

• Develop deep-learning methods for RF signal interference rejection
• Introduce a diverse RF dataset for data-driven signal analysis
• Compare deep learning architectures against traditional interference mitigation techniques

Innovation

• Deep learning for RF signal interference rejection
• Public diverse RF dataset for data-driven analysis
• UNet and WaveNet outperform traditional filtering methods