A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation
This addresses signal separation in dense RF spectrums for communications, with incremental improvements.
The paper tackles RF signal separation by adapting the WaveNet architecture with learnable dilation and data augmentation, resulting in a 58.82% increase in SINR at a BER of 10^-3 for OFDM-QPSK with EMI Signal 1 and winning a challenge.
In this paper, we address the intricate issue of RF signal separation by presenting a novel adaptation of the WaveNet architecture that introduces learnable dilation parameters, significantly enhancing signal separation in dense RF spectrums. Our focused architectural refinements and innovative data augmentation strategies have markedly improved the model's ability to discern complex signal sources. This paper details our comprehensive methodology, including the refined model architecture, data preparation techniques, and the strategic training strategy that have been pivotal to our success. The efficacy of our approach is evidenced by the substantial improvements recorded: a 58.82\% increase in SINR at a BER of $10^{-3}$ for OFDM-QPSK with EMI Signal 1, surpassing traditional benchmarks. Notably, our model achieved first place in the challenge \cite{datadrivenrf2024}, demonstrating its superior performance and establishing a new standard for machine learning applications within the RF communications domain.