Metraipolis › archive › dc_ea4bc2891bc5

A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites — arXiv:2609.03809

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.03809
License (as archived): CC0-1.0
Archived: 2026-10-17T02:08:15.480Z
SHA-256 of the archived text: f43eb10ed63c76b6a0d23536a415385f51b086d88e8bd0ec026c21536f2f34d1
Stable URL of this page: https://metraipolis.com/documento/dc_ea4bc2891bc5

How to cite

A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites — arXiv:2609.03809 — arXiv (en), https://arxiv.org/abs/2609.03809 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ea4bc2891bc5 (Metraipolis archive dc_ea4bc2891bc5, sha256 f43eb10ed63c76b6a0d23536a415385f51b086d88e8bd0ec026c21536f2f34d1)

BibTeX

@misc{dcea4bc2891bc5,
  title = {A Peer-Relative Representation Learning Framework for Energy Inefficiency Identification in Mobile Network Sites — arXiv:2609.03809},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.03809},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ea4bc2891bc5; sha256 f43eb10ed63c76b6a0d23536a415385f51b086d88e8bd0ec026c21536f2f34d1},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.03809. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Energy consumption is one of the largest operational expenditure items for mobile network operators, yet site-level energy inefficiencies such as faulty cooling controllers, idle radio equipment, and parasitic auxiliary loads often remain undetected because no ground-truth inefficiency labels exist and historical measurements may already contain embedded inefficiencies. This study proposes an unsupervised peer-relative approach based on the premise that sites with similar structural and operational characteristics should exhibit comparable energy consumption. To capture these relationships, a novel energy-aware Minimum Distortion Embedding (MDE) formulation is introduced that extends the standard MDE objective with an energy-based repulsion mechanism. This encourages sites with anomalously high energy consumption relative to comparable peers to become displaced from their local neighbourhoods in the embedding space. The resulting low-dimensional representation simultaneously preserves structural similarity and encodes energy-related deviations, enabling the identification of potentially inefficient sites through peer-relative comparison. The derived anomaly scores provide a practical mechanism for prioritising field investigations, allowing mobile network operators to focus engineering resources on sites most likely to yield energy savings. Experimental results demonstrate that the proposed approach outperforms conventional anomaly detection baselines and provides a robust foundation for large-scale energy-efficiency optimisation in mobile networks.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-04 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2609.03809 — 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 2026-10-17T01:47:25.572Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-04?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).