  The deep learning trend has recently impacted a variety of fields, including
communication systems, where various approaches have explored the application
of neural networks in place of traditional designs. Neural networks flexibly
allow for data/simulation-driven optimization, but are often employed as black
boxes detached from direct application of domain knowledge. Our work considers
learning-based approaches addressing modulation and signal detection design for
the non-coherent MIMO channel. We demonstrate that simulation-driven
optimization can be performed while entirely avoiding neural networks, yet
still perform comparably. Additionally, we show the feasibility of MIMO
communications over extremely short coherence windows (i.e., channel
coefficient stability period), with as few as two time slots.
