  Due to the Internet of Things (IoT) proliferation, Radio Frequency (RF)
channels are increasingly congested with new kinds of devices, which carry
unique and diverse communication needs. This poses complex challenges in modern
digital communications, and calls for the development of technological
innovations that (i) optimize capacity (bitrate) in limited bandwidth
environments, (ii) integrate cooperatively with already-deployed RF protocols,
and (iii) are adaptive to the ever-changing demands in modern digital
communications. In this paper we present methods for applying deep neural
networks for spectral filling. Given an RF channel transmitting digital
messages with a pre-established modulation scheme, we automatically learn novel
modulation schemes for sending extra information, in the form of additional
messages, "around" the fixed-modulation signals (i.e., without interfering with
them). In so doing, we effectively increase channel capacity without increasing
bandwidth. We further demonstrate the ability to generate signals that closely
resemble the original modulations, such that the presence of extra messages is
undetectable to third-party listeners. We present three computational
experiments demonstrating the efficacy of our methods, and conclude by
discussing the implications of our results for modern RF applications.
