  The behavior of users in relatively predictable, both in terms of the data
they request and the wireless channels they observe. In this paper, we consider
the statistics of such predictable patterns of the demand and channel jointly
across multiple users, and develop a novel predictive resource allocation
method. This method is shown to provide performance benefits over a reactive
approach, which ignores these patterns and instead aims to satisfy the
instantaneous demands, irrespective of cost to the system. In particular, we
show that our proposed method is able to attain a novel fundamental bound on
the achievable cost, as the service window grows. Through numerical evaluation,
we gain insights into how different uncertainty sources affect the decisions
and the cost.
