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WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO — arXiv:2609.05725

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05725
License (as archived): CC0-1.0
Archived: 2026-10-16T14:09:17.565Z
SHA-256 of the archived text: b4e3563f5b417a1349e6c9f772e8c0996e0d1489182dfd74cc495b64feb8e1aa
Stable URL of this page: https://metraipolis.com/documento/dc_0ee104f21463

How to cite

WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO — arXiv:2609.05725 — arXiv (en), https://arxiv.org/abs/2609.05725 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0ee104f21463 (Metraipolis archive dc_0ee104f21463, sha256 b4e3563f5b417a1349e6c9f772e8c0996e0d1489182dfd74cc495b64feb8e1aa)

BibTeX

@misc{dc0ee104f21463,
  title = {WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO — arXiv:2609.05725},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05725},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0ee104f21463; sha256 b4e3563f5b417a1349e6c9f772e8c0996e0d1489182dfd74cc495b64feb8e1aa},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05725. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

One way to increase the Energy Efficiency (EE) of 6G wireless networks is to utilize existing network infrastructure more efficiently. This can be achieved by introducing User-Centric Cell-Free Massive Multiple-Input-Multiple-Output (UCCF MMMIMO), which allows for simultaneously serving a single user by multiple Base Stations (BSs). From this perspective, the key challenge is to decide which BSs should serve a given user, known as the Serving Cluster Formulation (SCF). In this paper, we propose to deal with this problem by using an Artificial Intelligence (AI) agent based on a Large Language Model (LLM), targeting improvement of EE. We evaluated the proposed AI agent using a complex, 3D Ray Tracer-based, cellular network simulator, comparing a few GPT models and state-of-the-art algorithms. The results show up to 32% gain in EE of the proposed AI agent compared to the baseline.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-09 in cs.NI).
Categorie arXiv: Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2609.05725 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T13:58:54.211Z dalla pagina https://arxiv.org/catchup/cs.NI/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).