  This paper introduces the novel concept of proactive resource allocation in
which the predictability of user behavior is exploited to balance the wireless
traffic over time, and hence, significantly reduce the bandwidth required to
achieve a given blocking/outage probability. We start with a simple model in
which the smart wireless devices are assumed to predict the arrival of new
requests and submit them to the network T time slots in advance. Using tools
from large deviation theory, we quantify the resulting prediction diversity
gain to establish that the decay rate of the outage event probabilities
increases linearly with the prediction duration T. This model is then
generalized to incorporate the effect of prediction errors and the randomness
in the prediction lookahead time T. Remarkably, we also show that, in the
cognitive networking scenario, the appropriate use of proactive resource
allocation by the primary users results in more spectral opportunities for the
secondary users at a marginal, or no, cost in the primary network outage.
Finally, we conclude by a discussion of the new research questions posed under
the umbrella of the proposed proactive (non-causal) wireless networking
framework.
