  We study the single-channel source separation problem involving orthogonal
frequency-division multiplexing (OFDM) signals, which are ubiquitous in many
modern-day digital communication systems. Related efforts have been pursued in
monaural source separation, where state-of-the-art neural architectures have
been adopted to train an end-to-end separator for audio signals (as
1-dimensional time series). In this work, through a prototype problem based on
the OFDM source model, we assess -- and question -- the efficacy of using
audio-oriented neural architectures in separating signals based on features
pertinent to communication waveforms. Perhaps surprisingly, we demonstrate that
in some configurations, where perfect separation is theoretically attainable,
these audio-oriented neural architectures perform poorly in separating
co-channel OFDM waveforms. Yet, we propose critical domain-informed
modifications to the network parameterization, based on insights from OFDM
structures, that can confer about 30 dB improvement in performance.
