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GML-Based Optimization for Movable Antenna Wireless Networks: Challenges and Opportunities — arXiv:2608.12882

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.12882
License (as archived): CC0-1.0
Archived: 2026-10-19T05:14:00.486Z
SHA-256 of the archived text: 2538082f5f960abe3c2ba5022e256d7be6465094ef4a82a1546815c1a63efd47
Stable URL of this page: https://metraipolis.com/documento/dc_4f596ec308c8

How to cite

GML-Based Optimization for Movable Antenna Wireless Networks: Challenges and Opportunities — arXiv:2608.12882 — arXiv (en), https://arxiv.org/abs/2608.12882 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4f596ec308c8 (Metraipolis archive dc_4f596ec308c8, sha256 2538082f5f960abe3c2ba5022e256d7be6465094ef4a82a1546815c1a63efd47)

BibTeX

@misc{dc4f596ec308c8,
  title = {GML-Based Optimization for Movable Antenna Wireless Networks: Challenges and Opportunities — arXiv:2608.12882},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.12882},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4f596ec308c8; sha256 2538082f5f960abe3c2ba5022e256d7be6465094ef4a82a1546815c1a63efd47},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.12882. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Movable antenna (MA) is proposed as an emerging technology for future wireless networks. By leveraging the additional spatial degrees of freedom, MA can proactively reshape the wireless propagation environment, thereby enhancing network this http URL , fully unlocking the potential of MA networks necessitates the joint optimization of MA antenna positioning and beamforming. For this non-convex and highly coupled problem, existing solutions exhibit significant limitations. Therefore, this paper proposes a gradient-based meta learning (GML) optimization framework. Specifically, we first elaborate on the hardware architecture and channel characteristics of MA, based on which we analyze the primary challenges in optimizing MA wireless networks. Subsequently, we introduce the fundamental logic of the GML framework and compare it with existing methods. Furthermore, we discuss the constraint handling strategies for applying the proposed optimization framework to MA networks. A specific case is studied to show the performance of proposed framework based on numerical simulation. Finally, this paper outlines future research directions for both the GML framework and MA wireless networks.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-14 in eess.SP).
Categorie arXiv: Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2608.12882 — 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 2026-10-19T05:07:37.922Z dalla pagina https://arxiv.org/catchup/eess.SP/2026-08-14?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).