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