Data-driven Energy Efficiency Modelling in Large-scale Networks: An Expert Knowledge and ML-based Approach

TL;DR

This paper presents SRCON, a novel data-driven framework combining machine learning and expert models to accurately predict energy efficiency and QoS in large-scale mobile networks, enabling more effective energy-saving strategies.

Contribution

The paper introduces SRCON, a new data-driven modeling paradigm that reduces reliance on expert knowledge and drive testing for network energy efficiency prediction.

Findings

SRCON outperforms existing methods in prediction accuracy.

Real network data validates the effectiveness of SRCON.

Significant energy savings are achievable with SRCON-based optimization.

Abstract

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.