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Agentic Quantum Deep Reinforcement Learning for RAN Slicing — arXiv:2608.30206

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.30206
License (as archived): CC0-1.0
Archived: 2026-10-17T10:42:34.128Z
SHA-256 of the archived text: 5a46fa7738320b3b7b1e3e7198e43533972f786ee8c39a5b5f0fa8c4273176dc
Stable URL of this page: https://metraipolis.com/documento/dc_342f786406ff

How to cite

Agentic Quantum Deep Reinforcement Learning for RAN Slicing — arXiv:2608.30206 — arXiv (en), https://arxiv.org/abs/2608.30206 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_342f786406ff (Metraipolis archive dc_342f786406ff, sha256 5a46fa7738320b3b7b1e3e7198e43533972f786ee8c39a5b5f0fa8c4273176dc)

BibTeX

@misc{dc342f786406ff,
  title = {Agentic Quantum Deep Reinforcement Learning for RAN Slicing — arXiv:2608.30206},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.30206},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_342f786406ff; sha256 5a46fa7738320b3b7b1e3e7198e43533972f786ee8c39a5b5f0fa8c4273176dc},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.30206. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Radio access network (RAN) slicing enables ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) services to share radio resources, but their requirements create a challenging reliability--throughput tradeoff. URLLC requires low-latency and reliable packet delivery, whereas eMBB targets high sustained throughput. This paper considers downlink URLLC/eMBB RAN slicing and formulates it as a queue-aware long-term eMBB throughput maximization problem subject to URLLC delay-violation, physical resource block (PRB) exclusivity, and slice-budget constraints. To solve this problem, we propose agentic quantum deep reinforcement learning (Agentic-QDRL), a two-time-scale framework that combines agentic slice-level resource control with quantum-enhanced PRB scheduling. At the slow time scale, a perceive--memory--act--reflect (PMAR) controller adapts the resource shares of URLLC and eMBB slices. At the fast time scale, a compact variational quantum circuit (VQC)-based QDRL scheduler performs PRB allocation under the current slice configuration. A feasibility projection and a safety fallback mechanism is further introduced to satisfy scheduling constraints and reduce URLLC deadline violations. Simulation results under different eMBB traffic loads show that Agentic-QDRL improves eMBB throughput, reduces eMBB queue buildup, and maintains URLLC delay reliability compared with classical DRL and heuristic baselines.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-01 in eess.SP).
Categorie arXiv: Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2608.30206 — 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 2026-10-17T10:32:30.782Z dalla pagina https://arxiv.org/catchup/eess.SP/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).