  Prior work has demonstrated that recurrent neural network architectures show
promising improvements over other machine learning architectures when
processing temporally correlated inputs, such as wireless communication
signals. Additionally, recurrent neural networks typically process data on a
sequential basis, enabling the potential for near real-time results. In this
work, we investigate the novel usage of "just enough" decision making metrics
for making decisions during inference based on a variable number of input
symbols. Since some signals are more complex than others, due to channel
conditions, transmitter/receiver effects, etc., being able to dynamically
utilize just enough of the received symbols to make a reliable decision allows
for more efficient decision making in applications such as electronic warfare
and dynamic spectrum sharing. To demonstrate the validity of this concept, four
approaches to making "just enough" decisions are considered in this work and
each are analyzed for their applicability to wireless communication machine
learning applications.
