The increasingly crowded radio frequency (RF) spectrum forces communication signals to coexist, creating heterogeneous interferers whose structure often departs from Gaussian models. Recovering the interference-contaminated signal of interest in such settings is a central challenge, especially in single-channel RF processing. Existing data-driven methods often assume that the interference type is known, yielding ensembles of specialized models that scale poorly with the number of interferers. We show that detect-then-separate (DTS) strategies admit an analytical justification: within a Gaussian mixture framework, a plug-in maximum a posteriori detector followed by type-conditioned optimal estimation achieves asymptotic minimum mean-square error optimality under a mild temporal-diversity condition. This makes DTS a principled benchmark, but its reliance on multiple type-specific models limits scalability. Motivated by this, we propose a unified joint model (UJM), in which a single deep neural architecture learns to jointly detect and separate when applied directly to the received signal. Using tailored UNet architectures for baseband (complex-valued) RF signals, we compare DTS and UJM on synthetic and recorded interference types, showing that a capacity-matched UJM can match oracle-aided DTS performance across diverse signal-to-interference-and-noise ratios, interference types, and constellation orders, including mismatched training and testing type-uncertainty proportions. These findings highlight UJM as a scalable and practical alternative to DTS, while opening new directions for unified separation under broader regimes.

翻译：日益拥挤的射频（RF）频谱迫使通信信号共存，从而产生结构常偏离高斯模型的异构干扰源。在此类场景中恢复受干扰污染的目标信号是一个核心挑战，特别是在单通道射频处理领域。现有数据驱动方法通常假设干扰类型已知，这导致需要构建专用模型集合，其规模随干扰源数量增加而急剧扩大。我们证明检测后分离（DTS）策略具有理论依据：在高斯混合框架下，当满足温和的时间分集条件时，采用插件式最大后验检测器配合类型条件最优估计器可实现渐近最小均方误差最优性。这使得DTS成为具有理论基础的基准方法，但其对多个类型专用模型的依赖限制了可扩展性。受此启发，我们提出统一联合模型（UJM），该模型通过单一深度神经架构直接对接收信号进行联合检测与分离学习。针对基带（复值）射频信号采用定制化UNet架构，我们在合成与实测干扰类型上对比DTS与UJM性能，结果表明：在匹配模型容量的条件下，UJM能够在不同信干噪比、干扰类型和调制阶数（包括训练与测试阶段类型不确定比例失配）的场景中，达到与先知辅助DTS相当的性能。这些发现表明UJM可作为DTS的可扩展实用替代方案，同时为更广泛机制下的统一分离研究开辟了新方向。