ABSTRACT 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 Notably, our model achieved first place in the challenge [1], demonstrating its Figure 1: Modified Wavenet with Learnable Dilation and superior performance and establishing a new standard for Padding machine learning applications within the RF communications domain. Index Terms-- Radio Frequency Signal Separation, Machine Learning, WaveNet Architecture, Learnable Dilation, Data Augmentation 1. INTRODUCTION The co-channel signal separation in the crowded radiofrequency Figure 1: An Illustration of Learnable Dilation Rate (RF) spectrum is a crucial task for enabling various wireless systems to operate simultaneously.