Toward hyper-adaptive AI-enabled 6G networks for energy efficiency: techniques, classifications and tradeoffs

TL;DR

This paper surveys AI techniques for 6G networks, focusing on energy efficiency, adaptability, and tradeoffs among key performance metrics, highlighting current capabilities and future research needs.

Contribution

It provides a comprehensive review of AI-based methods for adaptive energy-efficient 6G networks, organized around practical use cases and dynamic management challenges.

Findings

AI enhances feedback-driven adaptability in 6G networks

Tradeoffs between energy efficiency and latency, reliability, fairness, coverage are analyzed

Identifies gaps and future directions for AI in 6G energy management

Abstract

Energy efficiency is shaping up to be one of the most challenging issues for 6G networks. The reason is fairly straightforward: Networks will need to meet extreme service demands while remaining sustainable and traditional optimization techniques are too limited. With users moving, traffic swinging unpredictably and services pulling in different directions, management has to be adaptive and AI may offer a way forward. This survey looks at how well AI-based methods actually deliver on that promise. We organize the review around practical use cases. For each use case, we examine how AI techniques contribute to feedback-driven adaptability and rapid decision-making under dynamic conditions. We then evaluate them against seven central dynamic aspects that we consider unavoidable in 6G. The survey also discusses crucial tradeoffs between energy efficiency and the remaining 6G main objectives such as latency, reliability, fairness and coverage, and finally identifies gaps and future research directions.