Traffic demand-aware topology control for enhanced energy-efficiency of cellular networks

Emmanuel Pollakis,Renato L G Cavalcante,Slawomir Stanczak

doi:10.1186/s13638-016-0539-y

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.

Highlights

The strive for ubiquitous connectivity and high throughput in the development of the fifth generation (5G) of mobile networks is envisioned to lead to highly dense network topologies providing the best possible service to users at all times
We present an extensive evaluation showing the effect of the different energy consumption parts on the solution of the energy saving network topology
Even with a simplifying assumption of the worstcase interference, the energy saving problem is a mixed integer programming problem that is strongly related to the bin-packing problem, which in turn is known to be NP-hard

Summary

Introduction

The strive for ubiquitous connectivity and high throughput in the development of the fifth generation (5G) of mobile networks is envisioned to lead to highly dense network topologies providing the best possible service to users at all times. We use the worst-case interference assumption, which results in a lower bound on the true link spectral efficiency ωi,j(ρ) ≥ ωi,j := ωi,j(1) for every ρ ∈[ 0, 1]M This bound diminishes gains in energy savings when taking into account the energy consumption of hardware, and we show in Section 5 how to incorporate the actual link spectral efficiency to improve the energy savings. Given a TP assignment X inducing a cell load ρ, the energy consumption El(ρ) ≥ 0 of base station l is defined to be the power that the respective base station consumes per unit of time, where El(ρ) = 0 iff base station l is inactive. Fi :[ 0, 1] → R+ (i ∈ M), is concave and continuously differentiable This assumption is satisfied by a linear dependency of the base station energy consumption and the cell load reported in current studies such as [27].

Problem statement

Problem relaxation

Findings

Conclusions