  In the context of wireless networking, it was recently shown that multiple
DNNs can be jointly trained to offer a desired collaborative behaviour capable
of coping with a broad range of sensing uncertainties. In particular, it was
established that DNNs can be used to derive policies that are robust with
respect to the information noise statistic affecting the local information
(e.g. CSI in a wireless network) used by each agent (e.g. transmitter) to make
its decision. While promising, a major challenge in the implementation of such
method is that information noise statistics may differ from agent to agent and,
more importantly, that such statistics may not be available at the time of
training or may evolve over time, making burdensome retraining necessary. This
situation makes it desirable to devise a "universal" machine learning model,
which can be trained once for all so as to allow for decentralized cooperation
in any future feedback noise environment. With this goal in mind, we propose an
architecture inspired from the well-known Mixture of Experts (MoE) model, which
was previously used for non-linear regression and classification tasks in
various contexts, such as computer vision and speech recognition. We consider
the decentralized power control problem as an example to showcase the validity
of the proposed model and to compare it against other power control algorithms.
We show the ability of the so called Team-DMoE model to efficiently track
time-varying statistical scenarios.
