  We consider the task of opportunistic channel access in a primary system
composed of independent Gilbert-Elliot channels where the secondary (or
opportunistic) user does not dispose of a priori information regarding the
statistical characteristics of the system. It is shown that this problem may be
cast into the framework of model-based learning in a specific class of
Partially Observed Markov Decision Processes (POMDPs) for which we introduce an
algorithm aimed at striking an optimal tradeoff between the exploration (or
estimation) and exploitation requirements. We provide finite horizon regret
bounds for this algorithm as well as a numerical evaluation of its performance
in the single channel model as well as in the case of stochastically identical
channels.
