  A new research problem named configuration learning is described in this
work. A novel algorithm is proposed to address the configuration learning
problem. The configuration learning problem is defined to be the optimization
of the Machine Learning (ML) classifier to maximize the ML performance metric
optimizing the transmitter configuration in the signal processing/communication
systems. Specifically, this configuration learning problem is investigated in
an underwater optical communication system with signal processing performance
metric of the physical-layer communication throughput. A novel algorithm is
proposed to perform the configuration learning by alternating optimization of
key design parameters and switching between several Recurrent Neural Network
(RNN) classifiers dependant on the learning objective. The proposed ML
algorithm is validated with the datasets of an underwater optical communication
system and is compared with competing ML algorithms. Performance results
indicate that the proposal outperforms the competing algorithms for binary and
multi-class configuration learning in underwater optical communication
datasets. The proposed configuration learning framework can be further
investigated and applied to a broad range of topics in signal processing and
communications.
