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

arXiv: https://arxiv.org/abs/2401.00443 · HTML: https://mlsanity.com/paper/2401.00443/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 74 references that are in the map.
- First posted on arXiv 31 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks](https://mlsanity.com/paper/2005.11650/) (2020)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Digital Twin of Wireless Systems: Overview, Taxonomy, Challenges, and Opportunities](https://mlsanity.com/paper/2202.02559/) (2022)
- [Digital-Twin-Enabled 6G: Vision, Architectural Trends, and Future Directions](https://mlsanity.com/paper/2102.12169/) (2021)
- [A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems](https://mlsanity.com/paper/2212.01351/) (2022)
- [A Survey on 5G Radio Access Network Energy Efficiency: Massive MIMO, Lean Carrier Design, Sleep Modes, and Machine Learning](https://mlsanity.com/paper/2101.11246/) (2021)
- [End-to-End Simulation of 5G mmWave Networks](https://mlsanity.com/paper/1705.02882/) (2017)
- [Graph Neural Networks for Channel Decoding](https://mlsanity.com/paper/2207.14742/) (2022)
- [Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?](https://mlsanity.com/paper/1403.6150/) (2014)
- [Machine Learning and Analytical Power Consumption Models for 5G Base Stations](https://mlsanity.com/paper/2209.11600/) (2022)
- [Calibration of the 5G-LENA System Level Simulator in 3GPP reference scenarios](https://mlsanity.com/paper/2205.03278/) (2022)
- [Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks](https://mlsanity.com/paper/1812.11654/) (2018)
- [Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2203.09954/) (2022)
- [Deep Learning-Based Synchronization for Uplink NB-IoT](https://mlsanity.com/paper/2205.10805/) (2022)
- [Intelligent MIMO Detection Using Meta Learning](https://mlsanity.com/paper/2208.03953/) (2022)

## Built on by (most cited first, 2 of 2)

- [Modelling the 5G Energy Consumption using Real-world Data: Energy Fingerprint is All You Need](https://mlsanity.com/paper/2406.16929/) (2024)
- [On the Impact of PRB Load Uncertainty Forecasting for Sustainable Open RAN](https://mlsanity.com/paper/2407.14400/) (2024)

## Abstract (arXiv)

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.