  The service provided by mobile networks operated today is not adapted to
spatio-temporal fluctuations in traffic demand, although such fluctuations
offer opportunities for energy savings. In particular, significant gains in
energy efficiency are realizable by disengaging temporarily redundant hardware
components of base stations. We therefore propose a novel optimization
framework that considers both the load-dependent energy radiated by the
antennas and the remaining forms of energy needed for operating the base
stations. The objective is to reduce the energy consumption of mobile networks,
while ensuring that the data rate requirements of the users are met throughout
the coverage area. Building upon sparse optimization techniques, we develop a
majorization-minimization algorithm with the ability to identify
energy-efficient network configurations. The iterative algorithm is load-aware,
has low computational complexity, and can be implemented in an online fashion
to exploit load fluctuations on a short time scale. Simulations show that the
algorithm can find network configurations with the energy consumption similar
to that obtained with global optimization tools, which cannot be applied to
real large networks. Although we consider only one currently deployed cellular
technology, the optimization framework is general, potentially applicable to a
large class of access technologies.
