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Trade-offs in quasi-decentralized massive MIMO

Alegria, Juan Vidal LU orcid ; Rusek, Fredrik LU ; Sanchez, Jesus Rodriguez LU and Edfors, Ove LU orcid (2020) 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 In 2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
Abstract

Typical massive multiple-input multiple-output (MIMO) architectures consider a centralized approach, in which all baseband data received by each antenna has to be sent to a central processing unit (CPU) to be processed. Due to the enormous amount of antennas expected in massive MIMO base stations (BSs), the number of connections to the CPU required in centralized massive MIMO is not scalable. In recent literature decentralized approaches have been proposed to reduce the number of connections between the antennas and the CPU. However, the reduction in the connections to the CPU requires more outputs per antenna to be generated. We study the trade-off between number of connections to the CPU and number of outputs per antenna. We propose a... (More)

Typical massive multiple-input multiple-output (MIMO) architectures consider a centralized approach, in which all baseband data received by each antenna has to be sent to a central processing unit (CPU) to be processed. Due to the enormous amount of antennas expected in massive MIMO base stations (BSs), the number of connections to the CPU required in centralized massive MIMO is not scalable. In recent literature decentralized approaches have been proposed to reduce the number of connections between the antennas and the CPU. However, the reduction in the connections to the CPU requires more outputs per antenna to be generated. We study the trade-off between number of connections to the CPU and number of outputs per antenna. We propose a generalized architecture that allows exploitation of this trade-off, and we define a novel matrix decomposition that allows information-lossless filtering within our proposed architecture.

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Please use this url to cite or link to this publication:
author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
Decentralized processing, Linear equalization, Massive MIMO, Matched filter
host publication
2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
series title
2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings
article number
9145479
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020
conference location
Dublin, Ireland
conference dates
2020-06-07 - 2020-06-11
external identifiers
  • scopus:85090274854
ISBN
9781728174402
DOI
10.1109/ICCWorkshops49005.2020.9145479
language
English
LU publication?
yes
id
b039f4b8-96bd-47d3-af67-cddef5064688
date added to LUP
2021-01-12 08:36:50
date last changed
2024-03-20 23:20:59
@inproceedings{b039f4b8-96bd-47d3-af67-cddef5064688,
  abstract     = {{<p>Typical massive multiple-input multiple-output (MIMO) architectures consider a centralized approach, in which all baseband data received by each antenna has to be sent to a central processing unit (CPU) to be processed. Due to the enormous amount of antennas expected in massive MIMO base stations (BSs), the number of connections to the CPU required in centralized massive MIMO is not scalable. In recent literature decentralized approaches have been proposed to reduce the number of connections between the antennas and the CPU. However, the reduction in the connections to the CPU requires more outputs per antenna to be generated. We study the trade-off between number of connections to the CPU and number of outputs per antenna. We propose a generalized architecture that allows exploitation of this trade-off, and we define a novel matrix decomposition that allows information-lossless filtering within our proposed architecture.</p>}},
  author       = {{Alegria, Juan Vidal and Rusek, Fredrik and Sanchez, Jesus Rodriguez and Edfors, Ove}},
  booktitle    = {{2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings}},
  isbn         = {{9781728174402}},
  keywords     = {{Decentralized processing; Linear equalization; Massive MIMO; Matched filter}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{2020 IEEE International Conference on Communications Workshops, ICC Workshops 2020 - Proceedings}},
  title        = {{Trade-offs in quasi-decentralized massive MIMO}},
  url          = {{http://dx.doi.org/10.1109/ICCWorkshops49005.2020.9145479}},
  doi          = {{10.1109/ICCWorkshops49005.2020.9145479}},
  year         = {{2020}},
}