Trade-offs in quasi-decentralized massive MIMO
(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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- author
- Alegria, Juan Vidal LU ; Rusek, Fredrik LU ; Sanchez, Jesus Rodriguez LU and Edfors, Ove LU
- organization
- publishing date
- 2020-06
- 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}}, }