Traffic Demand-Aware Topology Control for Enhanced Energy-Efficiency of Cellular Networks

TL;DR

This paper introduces a load-aware optimization framework that dynamically adjusts cellular network topology to improve energy efficiency by temporarily disabling redundant hardware based on real-time traffic demand.

Contribution

It presents a novel sparse optimization-based algorithm that efficiently finds energy-efficient network configurations considering load fluctuations and can be implemented online.

Findings

Achieves energy savings comparable to global optimization methods.

Low computational complexity enables real-time application.

Framework applicable to various access technologies.

Abstract

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.