  In this paper, proactive resource allocation based on user location for
point-to-point communication over fading channels is introduced, whereby the
source must transmit a packet when the user requests it within a deadline of a
single time slot. We introduce a prediction model in which the source predicts
the request arrival $T_p$ slots ahead, where $T_p$ denotes the prediction
window (PW) size. The source allocates energy to transmit some bits proactively
for each time slot of the PW with the objective of reducing the transmission
energy over the non-predictive case. The requests are predicted based on the
user location utilizing the prior statistics about the user requests at each
location. We also assume that the prediction is not perfect. We propose
proactive scheduling policies to minimize the expected energy consumption
required to transmit the requested packets under two different assumptions on
the channel state information at the source. In the first scenario, offline
scheduling, we assume the channel states are known a-priori at the source at
the beginning of the PW. In the second scenario, online scheduling, it is
assumed that the source has causal knowledge of the channel state. Numerical
results are presented showing the gains achieved by using proactive scheduling
policies compared with classical (reactive) networks. Simulation results also
show that increasing the PW size leads to a significant reduction in the
consumed transmission energy even with imperfect prediction.
