  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.
