  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.
