A Survey on AI-Driven Energy Optimization in Terrestrial Next Generation Radio Access Networks

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Saeed, Nagham ORCID: https://orcid.org/0000-0002-5124-7973, STHANKIYA, KISHAN, MCSORLEY, GREG, JABER, MONA and G. CLEGG, RICHARD (2024) A Survey on AI-Driven Energy Optimization in Terrestrial Next Generation Radio Access Networks. IEEE Access, 12. pp. 157540-157555.

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Official URL: https://ieeexplore.ieee.org/document/10720780

Abstract

This survey uncovers the tension between AI techniques designed for energy saving in
mobile networks and the energy demands those same techniques create. We compare modeling approaches
that estimate power usage cost of current commercial terrestrial next-generation radio access network
deployments. We then categorize emerging methods for reducing power usage by domain: time, frequency,
power, and spatial. Next, we conduct a timely review of studies that attempt to estimate the power usage
of the AI techniques themselves. We identify several gaps in the literature. Notably, real-world data for
the power consumption is difficult to source due to commercial sensitivity. Comparing methods to reduce
energy consumption is beyond challenging because of the diversity of system models and metrics. Crucially,
the energy cost of AI techniques is often overlooked, though some studies provide estimates of algorithmic
complexity or run-time. We find that extracting even rough estimates of the operational energy cost of AI
models and data processing pipelines is complex. Overall, we find the current literature hinders a meaningful
comparison between the energy savings from AI techniques and their associated energy costs. Finally,
we discuss future research opportunities to uncover the utility of AI for energy saving.

Item Type:	Article
Identifier:	10.1109/ACCESS.2024.3482561
Subjects:	Computing
Depositing User:	Nagham Saeed
Date Deposited:	02 Dec 2024 09:27
Last Modified:	03 Dec 2024 15:45
URI:	https://repository.uwl.ac.uk/id/eprint/12943