Data-driven Energy Efficiency Modelling in Large-scale Networks: An
  Expert Knowledge and ML-based Approach
D López-Pérez A De Domenico N Piovesan M . Debbah
The energy consumption of mobile networks poses a critical challenge.
Mitigating this concern necessitates the deployment and optimization of network
energy-saving solutions, such as carrier shutdown, to dynamically manage
network resources. Traditional optimization approaches encounter complexity due
to factors like the large number of cells, stochastic traffic, channel
variations, and intricate trade-offs. This paper introduces the simulated
reality of communication networks (SRCON) framework, a novel, data-driven
modeling paradigm that harnesses live network data and employs a blend of
machine learning (ML)- and expert-based models. These mix of models accurately
characterizes the functioning of network components, and predicts network
energy efficiency and user equipment (UE) quality of service for any energy
carrier shutdown configuration in a specific network. Distinguishing itself
from existing methods, SRCON eliminates the reliance on expensive expert
knowledge, drive testing, or incomplete maps for predicting network
performance. This paper details the pipeline employed by SRCON to decompose the
large network energy efficiency modeling problem into ML and expert-based
submodels. It demonstrates how, by embracing stochasticity, and carefully
crafting the relationship between such submodels, the overall computational
complexity can be reduced and prediction accuracy enhanced. Results derived
from real network data underscore the paradigm shift introduced by SRCON,
showcasing significant gains over a state-of-the art method used by a operator
for network energy efficiency modeling. The reliability of this local,
data-driven modeling of the network proves to be a key asset for network
energy-saving optimization.