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.

本文介绍了改进的 WaveNet 架构，引入了可学习的扩张参数，极大地提高了在密集射频频谱中的信号分离能力。通过创新的数据增强策略和重点的架构优化，我们的模型在辨别复杂信号源方面取得了显著的改进，并在 OFDM-QPSK with EMI Signal 1 的 BER 为 $10^{-3}$ 时，SINR 有 58.82% 的增加，超过传统基准。值得注意的是，我们的模型在挑战赛中取得了第一名，验证了其卓越的性能，为射频通信领域的机器学习应用建立了新的标准。

无线电信号分离的 WaveNet 架构的新方法：可学习的扩张和数据增强

A Novel Approach to WaveNet Architecture for RF Signal Separation with  Learnable Dilation and Data Augmentation

We present a new model for singing synthesis based on a modified version of
the WaveNet architecture. Instead of modeling raw waveform, we model features
produced by a parametric vocoder that separates the influence of pitch and
timbre. This allows conveniently modifying pitch to match any target melody,
facilitates training on more modest dataset sizes, and significantly reduces
training and generation times. Our model makes frame-wise predictions using
mixture density outputs rather than categorical outputs in order to reduce the
required parameter count. As we found overfitting to be an issue with the
relatively small datasets used in our experiments, we propose a method to
regularize the model and make the autoregressive generation process more robust
to prediction errors. Using a simple multi-stream architecture, harmonic,
aperiodic and voiced/unvoiced components can all be predicted in a coherent
manner. We compare our method to existing parametric statistical and
state-of-the-art concatenative methods using quantitative metrics and a
listening test. While naive implementations of the autoregressive generation
algorithm tend to be inefficient, using a smart algorithm we can greatly speed
up the process and obtain a system that's competitive in both speed and
quality.

利用基于 WaveNet 架构的新型合成模型，对参数声码器产生的特征进行建模，并使用混合密度输出，实现对每个帧的预测，避免了过度拟合，在预测错误的情况下进行自回归生成算法的正则化，成功将和声、非周期性和有声 / 无声组件预测在一起，比现有的参数化统计方法和拼接方法更为有效。