  The ever increasing mobile data traffic and dense deployment of wireless
networks have made energy efficient radio access imperative. As networks are
designed to satisfy peak user demands, radio access energy can be reduced in a
number of ways at times of lower demand. This includes putting base stations
(BSs) to intermittent short sleep modes during low load, as well as adaptively
powering down select BSs completely where demand is low for prolonged time
periods. In order to fully exploit such energy conserving mechanisms, networks
should be aware of the user temporal and spatial traffic demands. To this end,
this article investigates the potential of utilizing predictions of user
location and application information as a means to energy saving. We discuss
the development of a predictive green wireless access (PreGWA) framework and
identify its key functional entities and their interaction. To demonstrate the
potential energy savings we then provide a case study on stored video streaming
and illustrate how exploiting predictions can minimize BS resource consumption
within a single cell, and across a network of cells. Finally, to emphasize the
practical potential of PreGWA, we present a distributed heuristic that reduces
resource consumption significantly without requiring considerable information
or signaling overhead.
