A Survey on AI-driven Energy Optimisation in Terrestrial Next Generation Radio Access Networks

TL;DR

This survey reviews AI-driven energy optimization in next-generation terrestrial radio networks, highlighting modeling approaches, energy costs of AI techniques, and identifying gaps for future research.

Contribution

It categorizes existing methods for energy reduction, compares modeling approaches, and discusses the overlooked energy costs of AI techniques in mobile networks.

Findings

Difficulty in sourcing real-world power consumption data.

Challenges in comparing energy-saving methods due to diverse models.

Energy costs of AI techniques are often neglected in studies.

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.