AI-Ready Energy Modelling for Next Generation RAN

TL;DR

This paper introduces an extended AI-enabled simulator for RAN, enabling realistic energy and throughput modeling, and explores the trade-offs between energy efficiency and spectrum efficiency through extensive simulations.

Contribution

It presents a novel extension to the AIMM Simulator for realistic RAN modeling and demonstrates its effectiveness in analyzing energy and spectrum efficiency trade-offs.

Findings

Energy and spectrum efficiency often require different power settings.

The AIMM Simulator achieves low CPU execution times (~2 seconds).

Simulations show varying optimal power settings for different scenarios.

Abstract

Recent sustainability drives place energy-consumption metrics in centre-stage for the design of future radio access networks (RAN). At the same time, optimising the trade-off between performance and system energy usage by machine-learning (ML) is an approach that requires large amounts of granular RAN data to train models, and to adapt in near realtime. In this paper, we present extensions to the system-level discrete-event AIMM (AI-enabled Massive MIMO) Simulator, generating realistic figures for throughput and energy efficiency (EE) towards digital twin network modelling. We further investigate the trade-off between maximising either EE or spectrum efficiency (SE). To this end, we have run extensive simulations of a typical macrocell network deployment under various transmit power-reduction scenarios with a range of difference of 43 dBm. Our results demonstrate that the EE and SE objectives often require different power settings in different scenarios. Importantly, low mean user CPU execution times of 2.17 $\pm$ 0.05 seconds (2~s.d.) demonstrate that the AIMM Simulator is a powerful tool for quick prototyping of scalable system models which can interface with ML frameworks, and thus support future research in energy-efficient next generation networks.