Toward hyper-adaptive AI-enabled 6G networks for energy efficiency: techniques, classifications and tradeoffs
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866909915472199680 |
|---|---|
| author | Zayene, Mariem Habachi, Oussama Chalhoub, Gerard |
| author_facet | Zayene, Mariem Habachi, Oussama Chalhoub, Gerard |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_16296 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Toward hyper-adaptive AI-enabled 6G networks for energy efficiency: techniques, classifications and tradeoffs Zayene, Mariem Habachi, Oussama Chalhoub, Gerard Networking and Internet Architecture 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. |
| title | Toward hyper-adaptive AI-enabled 6G networks for energy efficiency: techniques, classifications and tradeoffs |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2511.16296 |