数字通信信号的数据驱动盲同步与干扰抑制
Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
AI总结：
针对单通道混合信号的干扰抑制问题，提出一种利用高分辨率时间结构实现精确同步的领域知情神经网络设计，显著提升了干扰抑制性能。
摘要：
我们研究数据驱动深度学习方法在从混合观测中分离两个通信信号的潜力。具体而言，我们假设已知其中一个信号的生成过程，称为信号of interest (SOI)，而对第二个信号的生成过程一无所知，该信号被称为干扰。这种单通道源分离问题也被称为干扰抑制。我们证明，捕获高分辨率时间结构（非平稳性）能够实现对SOI和干扰的精确同步，从而带来显著的性能提升。基于这一关键洞察，我们提出了一种领域知情的神经网络 (NN) 设计，如我们的仿真所示，该设计能够改进“现成的”NN和经典的检测与干扰抑制方法。我们的发现强调了通信特定领域知识在开发数据驱动方法中所起的关键作用，这些方法有望带来前所未有的增益。
英文摘要：
We study the potential of data-driven deep learning methods for separation of two communication signals from an observation of their mixture. In particular, we assume knowledge on the generation process of one of the signals, dubbed signal of interest (SOI), and no knowledge on the generation process of the second signal, referred to as interference. This form of the single-channel source separation problem is also referred to as interference rejection. We show that capturing high-resolution temporal structures (nonstationarities), which enables accurate synchronization to both the SOI and the interference, leads to substantial performance gains. With this key insight, we propose a domain-informed neural network (NN) design that is able to improve upon both "off-the-shelf" NNs and classical detection and interference rejection methods, as demonstrated in our simulations. Our findings highlight the key role communication-specific domain knowledge plays in the development of data-driven approaches that hold the promise of unprecedented gains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
