Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals
This work addresses interference rejection in digital communications, offering potential gains for signal processing applications, though it appears incremental as it builds on existing deep learning approaches with domain-specific adaptations.
The paper tackled the problem of separating two communication signals from a single-channel mixture, where one signal is known and the other is unknown interference, by using data-driven deep learning methods. The result showed that capturing high-resolution temporal structures for synchronization led to substantial performance gains, with a domain-informed neural network design outperforming both standard neural networks and classical methods in simulations.
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.