Impact of Quantization in Decentralized Processing for Large Multi-Antenna Architectures
(2022) 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022 p.1351-1356- Abstract
The demand for an increased number of antennas at base stations is driving research on decentralized processing schemes aimed at reducing the information volume that has to be transferred to, and processed at, a central processing unit (CPU). Some of these schemes can reduce the dimensions of the data while achieving information-lossless processing with respect to centralized architectures. However, little is known about the impact of quantization in these decentralized schemes. Moreover, it is unclear if an information-lossless reduction of dimensions directly corresponds to a reduction in the bit-rate that has to be transmitted to the CPU after quantization. This paper studies how quantization affects the performance of decentralized... (More)
The demand for an increased number of antennas at base stations is driving research on decentralized processing schemes aimed at reducing the information volume that has to be transferred to, and processed at, a central processing unit (CPU). Some of these schemes can reduce the dimensions of the data while achieving information-lossless processing with respect to centralized architectures. However, little is known about the impact of quantization in these decentralized schemes. Moreover, it is unclear if an information-lossless reduction of dimensions directly corresponds to a reduction in the bit-rate that has to be transmitted to the CPU after quantization. This paper studies how quantization affects the performance of decentralized processing. Bit rates after quantization of a received vector (in a centralized scheme) are contrasted with bit rates after quantization of post-processed vectors using various information-lossless dimension reductions that can potentially be applied in decentralized schemes.
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- author
- Alegría, Juan Vidal LU ; Rusek, Fredrik LU and Lozano, Angel
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2022 56th Asilomar Conference on Signals, Systems, and Computers
- editor
- Matthews, Michael B.
- pages
- 6 pages
- publisher
- IEEE Computer Society
- conference name
- 56th Asilomar Conference on Signals, Systems and Computers, ACSSC 2022
- conference location
- Virtual, Online, United States
- conference dates
- 2022-10-31 - 2022-11-02
- external identifiers
-
- scopus:85150179982
- ISBN
- 9781665459068
- DOI
- 10.1109/IEEECONF56349.2022.10051998
- language
- English
- LU publication?
- yes
- id
- 8e921842-6658-46f4-b586-c33a0839883c
- date added to LUP
- 2023-04-03 13:35:14
- date last changed
- 2023-11-21 06:12:36
@inproceedings{8e921842-6658-46f4-b586-c33a0839883c, abstract = {{<p>The demand for an increased number of antennas at base stations is driving research on decentralized processing schemes aimed at reducing the information volume that has to be transferred to, and processed at, a central processing unit (CPU). Some of these schemes can reduce the dimensions of the data while achieving information-lossless processing with respect to centralized architectures. However, little is known about the impact of quantization in these decentralized schemes. Moreover, it is unclear if an information-lossless reduction of dimensions directly corresponds to a reduction in the bit-rate that has to be transmitted to the CPU after quantization. This paper studies how quantization affects the performance of decentralized processing. Bit rates after quantization of a received vector (in a centralized scheme) are contrasted with bit rates after quantization of post-processed vectors using various information-lossless dimension reductions that can potentially be applied in decentralized schemes.</p>}}, author = {{Alegría, Juan Vidal and Rusek, Fredrik and Lozano, Angel}}, booktitle = {{2022 56th Asilomar Conference on Signals, Systems, and Computers}}, editor = {{Matthews, Michael B.}}, isbn = {{9781665459068}}, language = {{eng}}, pages = {{1351--1356}}, publisher = {{IEEE Computer Society}}, title = {{Impact of Quantization in Decentralized Processing for Large Multi-Antenna Architectures}}, url = {{http://dx.doi.org/10.1109/IEEECONF56349.2022.10051998}}, doi = {{10.1109/IEEECONF56349.2022.10051998}}, year = {{2022}}, }