Joint Analog Beam Selection and Digital Beamforming in Millimeter Wave Cell-Free Massive MIMO Systems
Yetis, Cenk M. ; Bjornson, Emil and Giselsson, Pontus LU
(2021)
In IEEE Open Journal of the Communications Society
2.
p.1647-1662
- Abstract
Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The proposed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam... (More)
Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The proposed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam selection decisions of the joint designs with low computational complexities. Since the training of ML algorithms is performed off-line, we propose a well-constructed joint design that combines multiple initializations, iterations, and selection features, as well as beam conflict control, i.e., the same beam cannot be used for multiple users. The numerical results indicate that ML algorithms can retain 99-100% of the original sum-rate results achieved by the proposed well-constructed designs.
(Less)
- author
- Yetis, Cenk M.
; Bjornson, Emil
and Giselsson, Pontus
LU
- organization
- publishing date
- 2021
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- analog beamforming, beam training, Cell-free, digital beamforming, hybrid architecture, millimeter wave
- in
- IEEE Open Journal of the Communications Society
- volume
- 2
- article number
- 9475518
- pages
- 16 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- scopus:85122046237
- ISSN
- 2644-125X
- DOI
- 10.1109/OJCOMS.2021.3094823
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2020 IEEE.
- id
- 668fe62b-a5e3-435e-82b2-25b676bd8fc7
- date added to LUP
- 2022-02-21 15:05:47
- date last changed
- 2023-11-21 03:02:46
@article{668fe62b-a5e3-435e-82b2-25b676bd8fc7,
abstract = {{<p>Cell-free massive MIMO systems consist of many distributed access points with simple components that jointly serve the users. In millimeter wave bands, only a limited set of predetermined beams can be supported. In a network that consolidates these technologies, downlink analog beam selection stands as a challenging task for the network sum-rate maximization. Low-cost digital filters can improve the network sum-rate further. In this work, we propose low-cost joint designs of analog beam selection and digital filters. The proposed joint designs achieve significantly higher sum-rates than the disjoint design benchmark. Supervised machine learning (ML) algorithms can efficiently approximate the input-output mapping functions of the beam selection decisions of the joint designs with low computational complexities. Since the training of ML algorithms is performed off-line, we propose a well-constructed joint design that combines multiple initializations, iterations, and selection features, as well as beam conflict control, i.e., the same beam cannot be used for multiple users. The numerical results indicate that ML algorithms can retain 99-100% of the original sum-rate results achieved by the proposed well-constructed designs. </p>}},
author = {{Yetis, Cenk M. and Bjornson, Emil and Giselsson, Pontus}},
issn = {{2644-125X}},
keywords = {{analog beamforming; beam training; Cell-free; digital beamforming; hybrid architecture; millimeter wave}},
language = {{eng}},
pages = {{1647--1662}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Open Journal of the Communications Society}},
title = {{Joint Analog Beam Selection and Digital Beamforming in Millimeter Wave Cell-Free Massive MIMO Systems}},
url = {{http://dx.doi.org/10.1109/OJCOMS.2021.3094823}},
doi = {{10.1109/OJCOMS.2021.3094823}},
volume = {{2}},
year = {{2021}},
}