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Adaptive Hybrid Precoding Strategy for Cell-Free Massive MIMO

Dercio M. Mathe et al · Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo · 2023

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Abstract This work presents an adaptive hybrid signal precoding strategy for Cell-Free (CF) Massive multiple-input multiple-output (MIMO) systems. The proposed solution exploits the advantages of both distributed and centralized signal processing schemes to perform precoding. In our proposal, some access points (APs) are dynamically configured to be used in a centralized precoding scheme, whereas others are adjusted to employ a distributed method. Simulation results show that it is possible to achieve a good compromise between spectral efficiency (SE) and computational complexity (CC) in CF Massive MIMO systems finding a sensible balance between APs utilized in centralized and distributed precoding. To the best of our knowledge, prior-art solutions have not addressed an adaptive hybrid precoding method itself, they have only proposed a specific solution for each architecture, i.e., distributed or centralized.

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APA 7

al, D. M. M. E. (2023). Adaptive Hybrid Precoding Strategy for Cell-Free Massive MIMO. https://doi.org/10.1590/2179-10742023v22i2266934

MLA

al, Dercio M. Mathe et. "Adaptive Hybrid Precoding Strategy for Cell-Free Massive MIMO." 2023. https://doi.org/10.1590/2179-10742023v22i2266934.

Chicago

al, Dercio M. Mathe et. 2023. "Adaptive Hybrid Precoding Strategy for Cell-Free Massive MIMO.". https://doi.org/10.1590/2179-10742023v22i2266934.

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al, D. M. M. E. 2023, Adaptive Hybrid Precoding Strategy for Cell-Free Massive MIMO, Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo, available at: https://doi.org/10.1590/2179-10742023v22i2266934 [Accessed 6 Aug. 2026].

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Title
Adaptive Hybrid Precoding Strategy for Cell-Free Massive MIMO
Author / contributors
Dercio M. Mathe et al
Publisher
Sociedade Brasileira de Microondas e Optoeletrônica e Sociedade Brasileira de Eletromagnetismo
Publication year
2023
ISSN
2179-1074
ISSN
2179-1074
Language
English

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