  Traditional physical (PHY) layer protocols contain chains of signal
processing blocks that have been mathematically optimized to transmit
information bits efficiently over noisy channels. Unfortunately, this same
optimality encourages ubiquity in wireless communication technology and
enhances the potential for catastrophic cyber or physical attacks due to
prolific knowledge of underlying physical layers. Additionally, optimal signal
processing for one channel medium may not work for another without significant
changes in the software protocol. Any truly resilient communications protocol
must be capable of immediate redeployment to meet quality of service (QoS)
demands in a wide variety of possible channel media. Contrary to many
traditional approaches which use immutable man-made signal processing blocks,
this work proposes generating real-time blocks {\it ad hoc} through a machine
learning framework, so-called deepmod, that is only relevant to the particular
channel medium being used. With this approach, traditional signal processing
blocks are replaced with machine learning graphs which are trained, used, and
discarded as needed. Our experiments show that deepmod, using the same machine
intelligence, converges to viable communication links over vastly different
channels including: radio frequency (RF), powerline communications (PLC), and
acoustic channels.
