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Scheduling for Massive MIMO with Hybrid Precoding using Contextual Multi-Armed Bandits

Mauricio, Weskley V. F. ; Maciel, Tarcisio Ferreira ; Klein, Anja ; Marques Lima, Francisco Rafael (2022)
Scheduling for Massive MIMO with Hybrid Precoding using Contextual Multi-Armed Bandits.
In: IEEE Transactions on Vehicular Technology, 71 (7)
doi: 10.1109/TVT.2022.3166654
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

In this work we study different scheduling problems in the downlink of a Frequency Division Duplex multiuser wireless system that employs a hybrid precoding antenna architecture for massive Multiple Input Multiple Output. In this context, we propose a scheduling framework using Reinforcement Learning (RL) tools, namely Contextual Multi-Armed Bandits (CMAB), that can dynamically adapt themselves to solve three scheduling problems, which are: i) Maximum Throughput (MT); ii) Maximum Throughput with Fairness Guarantees (MTFG), and; iii) Maximum Throughput with QoS Guarantees (MTQG), which are well-known relevant problems. Before performing scheduling itself, we exploit statistical Channel State Information (CSI) to create clusters of spatially compatible User Equipmentss (UEss). This structure, combined with the usage of Zero-Forcing precoding, allows us to reduce the scheduler complexity by considering each cluster as an independent virtual RL scheduling agent. Next, we apply a new learning-based scheduler aiming to optimize the desired system performance metric. Moreover, only scheduled UEss need to feed back instantaneous equivalent CSI, which also reduces the signaling overhead of the proposal. The superiority of the proposed framework is demonstrated through numerical simulations in comparison with reference solutions.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Mauricio, Weskley V. F. ; Maciel, Tarcisio Ferreira ; Klein, Anja ; Marques Lima, Francisco Rafael
Art des Eintrags: Bibliographie
Titel: Scheduling for Massive MIMO with Hybrid Precoding using Contextual Multi-Armed Bandits
Sprache: Englisch
Publikationsjahr: Juli 2022
Verlag: IEEE
Titel der Zeitschrift, Zeitung oder Schriftenreihe: IEEE Transactions on Vehicular Technology
Jahrgang/Volume einer Zeitschrift: 71
(Heft-)Nummer: 7
DOI: 10.1109/TVT.2022.3166654
URL / URN: https://ieeexplore.ieee.org/abstract/document/9756247
Kurzbeschreibung (Abstract):

In this work we study different scheduling problems in the downlink of a Frequency Division Duplex multiuser wireless system that employs a hybrid precoding antenna architecture for massive Multiple Input Multiple Output. In this context, we propose a scheduling framework using Reinforcement Learning (RL) tools, namely Contextual Multi-Armed Bandits (CMAB), that can dynamically adapt themselves to solve three scheduling problems, which are: i) Maximum Throughput (MT); ii) Maximum Throughput with Fairness Guarantees (MTFG), and; iii) Maximum Throughput with QoS Guarantees (MTQG), which are well-known relevant problems. Before performing scheduling itself, we exploit statistical Channel State Information (CSI) to create clusters of spatially compatible User Equipmentss (UEss). This structure, combined with the usage of Zero-Forcing precoding, allows us to reduce the scheduler complexity by considering each cluster as an independent virtual RL scheduling agent. Next, we apply a new learning-based scheduler aiming to optimize the desired system performance metric. Moreover, only scheduled UEss need to feed back instantaneous equivalent CSI, which also reduces the signaling overhead of the proposal. The superiority of the proposed framework is demonstrated through numerical simulations in comparison with reference solutions.

Freie Schlagworte: DAAD, Open6HUB, emergenCITY, emergenCITY_KOM
Fachbereich(e)/-gebiet(e): 18 Fachbereich Elektrotechnik und Informationstechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik
18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Kommunikationstechnik
DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche
LOEWE
LOEWE > LOEWE-Zentren
LOEWE > LOEWE-Zentren > emergenCITY
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > B: Adaptionsmechanismen > Teilprojekt B3: Adaptionsökonomie
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > C: Kommunikationsmechanismen
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1053: MAKI – Multi-Mechanismen-Adaption für das künftige Internet > C: Kommunikationsmechanismen > Teilprojekt C1 : Netzzentrische Sicht
Hinterlegungsdatum: 23 Jan 2023 14:32
Letzte Änderung: 06 Feb 2024 11:30
PPN: 505734672
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