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State-Augmented Learning for Post-Deployment Adaptation of Coexisting O-RAN Applications — arXiv:2611.01376

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01376
License (as archived): CC0-1.0
Archived: 2026-11-03T11:21:04.944Z
SHA-256 of the archived text: d3218ed4ae8abf4795431589b1a41c43084ae8145433398bee3f32483ea773cf
Stable URL of this page: https://metraipolis.com/documento/dc_dbb92aa6eeef

How to cite

State-Augmented Learning for Post-Deployment Adaptation of Coexisting O-RAN Applications — arXiv:2611.01376 — arXiv (en), https://arxiv.org/abs/2611.01376 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_dbb92aa6eeef (Metraipolis archive dc_dbb92aa6eeef, sha256 d3218ed4ae8abf4795431589b1a41c43084ae8145433398bee3f32483ea773cf)

BibTeX

@misc{dcdbb92aa6eeef,
  title = {State-Augmented Learning for Post-Deployment Adaptation of Coexisting O-RAN Applications — arXiv:2611.01376},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01376},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_dbb92aa6eeef; sha256 d3218ed4ae8abf4795431589b1a41c43084ae8145433398bee3f32483ea773cf},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01376. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Fixed control policies in Open RAN can require retraining when service requirements, traffic loads, or interacting applications change. We propose ATTUNE, a state-augmented learning method for an energy saving application that coexists with a learned mobility load balancing application under a service constraint. ATTUNE conditions the energy saving policy on a Lagrange multiplier and updates this scalar during operation using measured service degradation, while keeping both applications' network parameters fixed. To train a single policy across service requirements, ATTUNE samples target service levels uniformly and maps them to multipliers through an adaptive table. This procedure accounts for policy-dependent multiplier responses and improves energy savings compared with training over a widened fixed multiplier range. We characterize the induced distribution of service levels and the convergence and fluctuations of the online update. An exact finite-window identity establishes convergence of average measured degradation to the target when the projection corrections vanish on average. Simulations show that a single ATTUNE policy achieves energy savings comparable to policies trained separately for each service requirement. It adapts to changes in the target, load, and partner application without retraining, increases energy savings at light load, and maintains target tracking for separately trained application pairs. No statistically significant increase in cell switching is detected in the evaluated comparisons.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Networking and Internet Architecture (cs.NI).
Pagina del lavoro: https://arxiv.org/abs/2611.01376 — 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 2026-11-03T11:02:37.527Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).