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On Scalability of FDD-Based Cell-Free Massive MIMO Framework

Cell-free massive multiple-input multiple-output (MIMO) systems have the potential of providing joint services, including joint initial access, efficient clustering of access points (APs), and pilot allocation to user equipment (UEs) over large coverage areas with reduced interference. In cell-free...

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Autores principales: Hassan, Beenish, Baig, Sobia, Aslam, Saad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422490/
https://www.ncbi.nlm.nih.gov/pubmed/37571774
http://dx.doi.org/10.3390/s23156991
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author Hassan, Beenish
Baig, Sobia
Aslam, Saad
author_facet Hassan, Beenish
Baig, Sobia
Aslam, Saad
author_sort Hassan, Beenish
collection PubMed
description Cell-free massive multiple-input multiple-output (MIMO) systems have the potential of providing joint services, including joint initial access, efficient clustering of access points (APs), and pilot allocation to user equipment (UEs) over large coverage areas with reduced interference. In cell-free massive MIMO, a large coverage area corresponds to the provision and maintenance of the scalable quality of service requirements for an infinitely large number of UEs. The research in cell-free massive MIMO is mostly focused on time division duplex mode due to the availability of channel reciprocity which aids in avoiding feedback overhead. However, the frequency division duplex (FDD) protocol still dominates the current wireless standards, and the provision of angle reciprocity aids in reducing this overhead. The challenge of providing a scalable cell-free massive MIMO system in an FDD setting is also prevalent, since computational complexity regarding signal processing tasks, such as channel estimation, precoding/combining, and power allocation, becomes prohibitively high with an increase in the number of UEs. In this work, we consider an FDD-based scalable cell-free network with angular reciprocity and a dynamic cooperation clustering approach. We have proposed scalability for our FDD cell-free and performed a comparative analysis with reference to channel estimation, power allocation, and precoding/combining techniques. We present expressions for scalable spectral efficiency, angle-based precoding/combining schemes and provide a comparison of overhead between conventional and scalable angle-based estimation as well as combining schemes. Simulations confirm that the proposed scalable cell-free network based on an FDD scheme outperforms the conventional matched filtering scheme based on scalable precoding/combining schemes. The angle-based LP-MMSE in the FDD cell-free network provides [Formula: see text] improvement in spectral efficiency and [Formula: see text] improvement in energy efficiency compared to the scalable MF scheme.
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spelling pubmed-104224902023-08-13 On Scalability of FDD-Based Cell-Free Massive MIMO Framework Hassan, Beenish Baig, Sobia Aslam, Saad Sensors (Basel) Article Cell-free massive multiple-input multiple-output (MIMO) systems have the potential of providing joint services, including joint initial access, efficient clustering of access points (APs), and pilot allocation to user equipment (UEs) over large coverage areas with reduced interference. In cell-free massive MIMO, a large coverage area corresponds to the provision and maintenance of the scalable quality of service requirements for an infinitely large number of UEs. The research in cell-free massive MIMO is mostly focused on time division duplex mode due to the availability of channel reciprocity which aids in avoiding feedback overhead. However, the frequency division duplex (FDD) protocol still dominates the current wireless standards, and the provision of angle reciprocity aids in reducing this overhead. The challenge of providing a scalable cell-free massive MIMO system in an FDD setting is also prevalent, since computational complexity regarding signal processing tasks, such as channel estimation, precoding/combining, and power allocation, becomes prohibitively high with an increase in the number of UEs. In this work, we consider an FDD-based scalable cell-free network with angular reciprocity and a dynamic cooperation clustering approach. We have proposed scalability for our FDD cell-free and performed a comparative analysis with reference to channel estimation, power allocation, and precoding/combining techniques. We present expressions for scalable spectral efficiency, angle-based precoding/combining schemes and provide a comparison of overhead between conventional and scalable angle-based estimation as well as combining schemes. Simulations confirm that the proposed scalable cell-free network based on an FDD scheme outperforms the conventional matched filtering scheme based on scalable precoding/combining schemes. The angle-based LP-MMSE in the FDD cell-free network provides [Formula: see text] improvement in spectral efficiency and [Formula: see text] improvement in energy efficiency compared to the scalable MF scheme. MDPI 2023-08-07 /pmc/articles/PMC10422490/ /pubmed/37571774 http://dx.doi.org/10.3390/s23156991 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hassan, Beenish
Baig, Sobia
Aslam, Saad
On Scalability of FDD-Based Cell-Free Massive MIMO Framework
title On Scalability of FDD-Based Cell-Free Massive MIMO Framework
title_full On Scalability of FDD-Based Cell-Free Massive MIMO Framework
title_fullStr On Scalability of FDD-Based Cell-Free Massive MIMO Framework
title_full_unstemmed On Scalability of FDD-Based Cell-Free Massive MIMO Framework
title_short On Scalability of FDD-Based Cell-Free Massive MIMO Framework
title_sort on scalability of fdd-based cell-free massive mimo framework
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422490/
https://www.ncbi.nlm.nih.gov/pubmed/37571774
http://dx.doi.org/10.3390/s23156991
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