Szczegóły publikacji
Opis bibliograficzny
Coordinated multi-armed bandits for improved Spatial Reuse in Wi-Fi / Francesc Wilhelmi, Boris Bellalta, Szymon SZOTT, Katarzyna KOSEK-SZOTT, Sergio Barrachina-Muño // W: ICMLCN 2025 [Dokument elektroniczny] : IEEE International Conference on Machine Learning for Communication and Networking : May 26-29, 2025, Barcelona, Spain. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : IEEE, cop. 2025. — Dod. ISBN: 979-8-3315-2043-4 (print on demand). — e-ISBN: 979-8-3315-2042-7. — S. [1–6]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [6], Abstr. — Publikacja dostępna online od: 2025-09-03
Autorzy (5)
- Wilhelmi Francesc
- Bellalta Boris
- AGHSzott Szymon
- AGHKosek-Szott Katarzyna
- Barrachina-Muñoz Sergio
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 162302 |
|---|---|
| Data dodania do BaDAP | 2025-09-11 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICMLCN64995.2025.11140340 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
Abstract
Multi-Access Point Coordination (MAPC) and Artificial Intelligence and Machine Learning (AI/ML) are expected to be key features in future Wi-Fi, such as the forthcoming IEEE 802.11bn (Wi-Fi 8) and beyond. In this paper, we explore a coordinated solution based on online learning to drive the optimization of Spatial Reuse (SR), a method that allows multiple devices to perform simultaneous transmissions by controlling interference through Packet Detect (PD) adjustment and transmit power control. In particular, we focus on a Multi-Agent Multi-Armed Bandit (MA-MAB) setting, where multiple decision-making agents concurrently configure SR parameters from coexisting networks by leveraging the MAPC framework, and study various algorithms and reward-sharing mechanisms. We evaluate different MA-MAB implementations using Komondor, a well-adopted Wi-Fi simulator, and demonstrate that AI-native SR enabled by coordinated MABs can improve the network performance over current Wi-Fi operation: mean throughput increases by 15%, fairness is improved by increasing the minimum throughput across the network by 210%, while the maximum access delay is kept below 3 ms.
