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Deep Reinforcement Learning for BD-RIS-Assisted Multi-UAV RSMA Communications — arXiv:2611.03047

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03047
License (as archived): CC0-1.0
Archived: 2026-11-03T18:51:52.026Z
SHA-256 of the archived text: 4d8d87b13e7cf85b269c15b3c7178a155bf384ecc0e65f8e08d5ca5a04caa5e2
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How to cite

Deep Reinforcement Learning for BD-RIS-Assisted Multi-UAV RSMA Communications — arXiv:2611.03047 — arXiv (en), https://arxiv.org/abs/2611.03047 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_586f62d3a3a8 (Metraipolis archive dc_586f62d3a3a8, sha256 4d8d87b13e7cf85b269c15b3c7178a155bf384ecc0e65f8e08d5ca5a04caa5e2)

BibTeX

@misc{dc586f62d3a3a8,
  title = {Deep Reinforcement Learning for BD-RIS-Assisted Multi-UAV RSMA Communications — arXiv:2611.03047},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03047},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_586f62d3a3a8; sha256 4d8d87b13e7cf85b269c15b3c7178a155bf384ecc0e65f8e08d5ca5a04caa5e2},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03047. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) extend conventional diagonal RISs by allowing controllable coupling among reflecting elements. This flexibility is attractive for aerial networks, where unmanned aerial vehicles (UAVs) face rapidly varying channels and strict power limits. In this paper, we study a hybrid BD-RIS-assisted multi-UAV rate-splitting multiple access (RSMA) downlink, in which UAVs within favourable BD-RIS coverage use both direct and BD-RIS-aided links, whereas the remaining UAVs are served through direct links only. We formulate a network sum-rate maximization problem that jointly optimizes UAV trajectories, RSMA common-rate allocation, beamforming, BD-RIS scattering matrices, and UAV-BD-RIS association. The resulting mixed-integer non-convex problem is solved by using deep deterministic policy gradient (DDPG) algorithm, equipped with a constraint-preserving action layer that generates reciprocal and lossless BD-RIS responses in closed form and handles the binary association inside the environment transition. Simulation results, averaged over three training seeds under a physically consistent channel model with blocked direct links near the BD-RISs, show that the proposed design attains a sum rate comparable to SAC and higher than twin delayed deep deterministic policy gradient ( TD3), proximal policy optimization (PPO), and deep Q-network (DQN), exceeds per-slot alternating optimization with fixed trajectories at about 1/65 of its per-slot runtime, and benefits mainly from RSMA, while the additional BD-RIS gain is modest.

Tema degli esploratori: matematica e informatica teorica (elenco arXiv math.IT, nuovi lavori del giorno).
Categorie arXiv: Information Theory (cs.IT) ; Emerging Technologies (cs.ET).
Pagina del lavoro: https://arxiv.org/abs/2611.03047 — 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 2026-11-03T18:15:52.833Z dalla pagina https://arxiv.org/list/math.IT/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).