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A Novel Approach to WaveNet Architecture for RF Signal Separation with Learnable Dilation and Data Augmentation
Feb. 16, 2024, 5:42 a.m. | Yu Tian, Ahmed Alhammadi, Abdullah Quran, Abubakar Sani Ali
cs.LG updates on arXiv.org arxiv.org
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 …
abstract architecture arxiv augmentation cs.lg data eess.sp issue novel paper parameters presenting signal type wavenet
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