Energy consumption in next-generation mobile networks

8c2f940f-34be-4c54-b568-ba4583c79ee9

Active

EPSRC
EPSRC
EPSRC
EPSRC
EPSRC

No funds listed.

Sept. 30, 2021

Sept. 30, 2025

Extract taken from the Year 2 Progression Report:

The rapid evolution of the telecommunications landscape and the undeniable environmental implications of its energy consumption necessitate a thorough exploration and understanding of its operational characteristics. Beginning with the current state of affairs, this project has achieved a primary goal to understand the energy consumption in current deployments. Such an understanding serves as the foundation upon which we can build and model future strategies, allowing us to gauge and measure the shifts in energy dynamics effectively.
The past 12 months have yielded productive strides towards the successful development of a pre-existing tool that can model energy consumption for 5G networks, by
collaborating with telecommunications giant BT (owner of UK based mobile network 'EE'). Representing the current pinnacle of mobile communication, 5G will act as our
baseline for comparison with potential future technologies.
With the present toolset extended for energy modelling capability, we aim to explore the frontier of what lies ahead. This entails analysing the potential energy consumption in hypothetical deployments, especially those centred around the concepts central to the next generation of mobile network architectures, like Open RAN and AI-native networks. By extrapolating current 5G energy requirements towards these emergent architectures, we aim to derive insights into the scalability and sustainability of upcoming technologies. Further development will be pivotal in dimensioning energy demands and predicting future needs under varying conditions, backed by trusted real-world applicability from BT guidance.
However, understanding and prediction are just parts of the solution. The real crux lies in actionable change. With the integration of AI and ML technologies, we plan to formulate strategies that can optimise energy consumption. These strategies are underpinned by a joint goal: achieving statistical significance in energy reduction and
7 maximising key performance indicators for 5G and future networks. The application of AI in the RAN Intelligence Controllers (RIC) will allow for near-real-time optimisations, merging efficiency with performance.
Lastly, while modelling and strategies form the theoretical basis, tangible validation in a real-world setting is essential. Therefore, our final aim is to validate the developed models and strategies against energy consumption metrics within a RAN testbed. This hands-on validation will serve as the litmus test for our proposed solutions' efficacy, robustness, and practicality.
The overarching goal is to significantly reduce net energy consumption within the radio access network, especially in the context of 5G and emerging network technologies. Each aim serves as a stepping stone towards this innovation by understanding the energy demands of current deployments, devising a predictive tool for energy modelling, analysing the potential consumption of future network architectures, and using AI/ML strategies to streamline consumption. These endeavours will culminate with practical application and validation within an operational RAN framework. Ultimately, this project aims to pioneer a paradigm shift in energy efficiency, paving the way for sustainable practices in next-generation mobile networks.