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A Heterogeneous Multi-agent Deep Reinforcement Learning Framework for Wireless Power Allocation under Realistic Urban Mobility Models

; ; ; ;

In
GLOBECOM 2025 - 2025 IEEE Global Communications Conference : 8-12 Dec. 2025, Seiten/Artikel-Nr: 2204-2209

Konferenz/Event:2025 IEEE Global Communications Conference , Taipei , Taiwan , GLOBECOM 2025 , 2025-12-08 - 2025-12-12

: IEEE

Umfang2204-2209

ISBN979-8-3315-7781-0, 979-8-3315-7782-7

Date Added to IEEE Xplore: 19 March 2026

Online
DOI: 10.1109/GLOBECOM59602.2025.11431641

URL: https://publications.rwth-aachen.de/record/1034999/files/A_Heterogeneous_Multi-agent_Deep_Reinforcement_Learning_Framework_for_Wireless_Power_Allocation_under_Realistic_Urban_Mobility_Models.pdf

Einrichtungen

  1. Lehrstuhl für Verteilte Signalverarbeitung (612310)

Projekte

  1. BMFTR 16KISK036K - Verbundprojekt: 6G-Forschungs-Hub für offene, effiziente und sichere Mobilfunksysteme - 6GEM -; Teilvorhaben: Adaptive hierarchische vielseitig einsetzbare 6G Netze (16KISK036K) (16KISK036K)

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Dokumenttyp
Contribution to a book/Contribution to a conference proceedings

Format
online, print

Sprache
English

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-105036326382

Interne Identnummern
RWTH-2026-04809
Datensatz-ID: 1034999

Beteiligte Länder
Germany

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Faculty of Electrical Engineering and Information Technology (Fac.6)
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612310
 Record created 2026-05-05, last modified 2026-07-01
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