  We consider the problem of scheduling transmissions over low-latency wireless
communication links to control various control systems. Low-latency
requirements are critical in developing wireless technology for industrial
control and Tactile Internet, but are inherently challenging to meet while also
maintaining reliable performance. An alternative to ultra reliable low latency
communications is a framework in which reliability is adapted to control system
demands. We formulate the control-aware scheduling problem as a constrained
statistical optimization problem in which the optimal scheduler is a function
of current control and channel states. The scheduler is parameterized with a
deep neural network, and the constrained problem is solved using techniques
from primal-dual learning, which have a necessary model-free property in that
they do not require explicit knowledge of channels models, performance metrics,
or system dynamics to execute. The resulting control-aware deep scheduler is
evaluated in empirical simulations and strong performance is shown relative to
other model-free heuristic scheduling methods.
