Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
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DOI：
10.1109/globecom48099.2022.10001513
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
A. Lancho;A. Weiss;Gary C. F. Lee;Jennifer Tang;Yuheng Bu;Yury Polyanskiy;G. Wornell
中科院分区：
文献类型：
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作者：
A. Lancho;A. Weiss;Gary C. F. Lee;Jennifer Tang;Yuheng Bu;Yury Polyanskiy;G. Wornell
我们研究了数据驱动的深度学习方法从两个通信信号的混合观测中分离出它们的潜力。具体地，我们假设知道其中一个信号的产生过程，称为感兴趣信号(SOI)，而不知道第二个信号的产生过程，称为干扰。这种形式的单通道信源分离问题也称为干扰抑制。我们表明，捕获高分辨率的时间结构(非平稳性)可以实现对SOI和干扰的准确同步，从而导致显著的性能提升。基于这一关键见解，我们提出了一种域信息神经网络(NN)设计，该设计能够改进现有的NNS和经典的检测和干扰抑制方法，如我们的模拟所展示的那样。我们的发现突显了特定于通信的领域知识在开发数据驱动的方法中发挥的关键作用，这些方法有望获得前所未有的收益。
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.