  We propose a decentralized stochastic control solution for the broadcast
message dissemination problem in wireless ad hoc networks with slow fading
channels. We formulate the control problem as a dynamic robust game which is
well justified by two key observations; first, the shared nature of the
wireless medium which inevitably cross-couples the nodes' forwarding decisions,
thus binding them together as strategic players; second, the stochastic
dynamics associated with the link qualities which renders the transmission
costs noisy, thus motivating a robust formulation. Given the non stationarity
induced by the fading process, an online solution for the formulated game would
then require an adaptive procedure capable of both convergence to and tracking
strategic equilibria as the environment changes. To this end, we deploy the
strategic and non stationary learning algorithm of regret tracking, the
temporally adaptive variant of the celebrated regret matching algorithm, to
guarantee the emergence and active tracking of the correlated equilibria in the
dynamic robust forwarding game. We also make provision for exploiting the
channel state information, when available, to enhance the convergence speed of
the learning algorithm by conducting an accurate transmission cost estimation.
This cost estimate can basically serve as a model which spares the algorithm
from extra action exploration, thus rendering the learning process more sample
efficient. Simulation results reveal that our proposed solution excels in terms
of both the number of transmissions and load distribution while also
maintaining near perfect delivery ratio, especially in dense crowded
environments.
