  Optimizing resource allocation with predicted information has shown promising
gain in boosting network performance and improving user experience. Earlier
research efforts focus on optimizing proactive policies under the assumption of
knowing the future information. Recently, various techniques have been proposed
to predict the required information, and the prediction results were then
treated as the true value in the optimization, i.e.,
"first-predict-then-optimize". In this paper, we introduce a proactive
optimization framework for anticipatory resource allocation, where the future
information is implicitly predicted under the same objective with the policy
optimization in a single step. An optimization problem is formulated to
integrate the implicit prediction and the policy optimization, based on the
conditional distribution of the future information given the historical
observations. To solve such a problem, we transform it equivalently to a
problem depending on the joint distribution of future and historical
information. Then, we resort to unsupervised learning with neural networks to
learn the proactive policy as a function of the past observations via
stochastic optimization. We take proactive caching and bandwidth allocation at
base stations as a concrete example, where the objective function is the
conditional expectation of successful offloading probability taken over the
future popularity given the historically observed popularity. We use simulation
to validate the proposed framework and compare it with the
"first-predict-then-optimize" strategy and a heuristic "end-to-end"
optimization strategy with supervised learning.
