User-Centric Cell-Free Massive Multiple-Input-Multiple-Output System with Noisy Channel Gain Estimation and Line of Sight: A Beckmann Distribution Approach
This paper analyzes for the first time how the Beckmann distribution can be used to characterize the random variable that represents the envelope of the effective channel gain experienced by the k-th user equipment (UE) of a user-centric (UC) cell-free (CF) system in a scenario with noisy channel st...
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| Published in | Entropy (Basel, Switzerland) Vol. 27; no. 3; p. 223 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
MDPI AG
21.02.2025
MDPI |
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| Online Access | Get full text |
| ISSN | 1099-4300 1099-4300 |
| DOI | 10.3390/e27030223 |
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| Abstract | This paper analyzes for the first time how the Beckmann distribution can be used to characterize the random variable that represents the envelope of the effective channel gain experienced by the k-th user equipment (UE) of a user-centric (UC) cell-free (CF) system in a scenario with noisy channel state information (CSI) estimation and line of sight (LoS). Additionally, it is shown how the Beckmann probability density function (PDF) can be used to derive the PDF and the cumulative density function (CDF) of the instantaneous signal-to-interference-plus-noise ratio (SINR) of the UC CF k-th UE, followed by applications in the ergodic capacity (EC) and outage probability (OP) expression derivations. It is shown that, regardless of the type of distribution considered for the channel gain between each access point (AP) and UE links, the effective gain presents a Beckmann distribution. |
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| AbstractList | This paper analyzes for the first time how the Beckmann distribution can be used to characterize the random variable that represents the envelope of the effective channel gain experienced by the
-th user equipment (UE) of a user-centric (UC) cell-free (CF) system in a scenario with noisy channel state information (CSI) estimation and line of sight (LoS). Additionally, it is shown how the Beckmann probability density function (PDF) can be used to derive the PDF and the cumulative density function (CDF) of the instantaneous signal-to-interference-plus-noise ratio (SINR) of the UC CF
-th UE, followed by applications in the ergodic capacity (EC) and outage probability (OP) expression derivations. It is shown that, regardless of the type of distribution considered for the channel gain between each access point (AP) and UE links, the effective gain presents a Beckmann distribution. This paper analyzes for the first time how the Beckmann distribution can be used to characterize the random variable that represents the envelope of the effective channel gain experienced by the k-th user equipment (UE) of a user-centric (UC) cell-free (CF) system in a scenario with noisy channel state information (CSI) estimation and line of sight (LoS). Additionally, it is shown how the Beckmann probability density function (PDF) can be used to derive the PDF and the cumulative density function (CDF) of the instantaneous signal-to-interference-plus-noise ratio (SINR) of the UC CF k-th UE, followed by applications in the ergodic capacity (EC) and outage probability (OP) expression derivations. It is shown that, regardless of the type of distribution considered for the channel gain between each access point (AP) and UE links, the effective gain presents a Beckmann distribution.This paper analyzes for the first time how the Beckmann distribution can be used to characterize the random variable that represents the envelope of the effective channel gain experienced by the k-th user equipment (UE) of a user-centric (UC) cell-free (CF) system in a scenario with noisy channel state information (CSI) estimation and line of sight (LoS). Additionally, it is shown how the Beckmann probability density function (PDF) can be used to derive the PDF and the cumulative density function (CDF) of the instantaneous signal-to-interference-plus-noise ratio (SINR) of the UC CF k-th UE, followed by applications in the ergodic capacity (EC) and outage probability (OP) expression derivations. It is shown that, regardless of the type of distribution considered for the channel gain between each access point (AP) and UE links, the effective gain presents a Beckmann distribution. This paper analyzes for the first time how the Beckmann distribution can be used to characterize the random variable that represents the envelope of the effective channel gain experienced by the k-th user equipment (UE) of a user-centric (UC) cell-free (CF) system in a scenario with noisy channel state information (CSI) estimation and line of sight (LoS). Additionally, it is shown how the Beckmann probability density function (PDF) can be used to derive the PDF and the cumulative density function (CDF) of the instantaneous signal-to-interference-plus-noise ratio (SINR) of the UC CF k-th UE, followed by applications in the ergodic capacity (EC) and outage probability (OP) expression derivations. It is shown that, regardless of the type of distribution considered for the channel gain between each access point (AP) and UE links, the effective gain presents a Beckmann distribution. This paper analyzes for the first time how the Beckmann distribution can be used to characterize the random variable that represents the envelope of the effective channel gain experienced by the k -th user equipment (UE) of a user-centric (UC) cell-free (CF) system in a scenario with noisy channel state information (CSI) estimation and line of sight (LoS). Additionally, it is shown how the Beckmann probability density function (PDF) can be used to derive the PDF and the cumulative density function (CDF) of the instantaneous signal-to-interference-plus-noise ratio (SINR) of the UC CF k -th UE, followed by applications in the ergodic capacity (EC) and outage probability (OP) expression derivations. It is shown that, regardless of the type of distribution considered for the channel gain between each access point (AP) and UE links, the effective gain presents a Beckmann distribution. |
| Audience | Academic |
| Author | Alencar, Marcelo S. Queiroz, Wamberto J. L. Duarte, Rafael M. Madeiro, Francisco Almeida, Danilo B. T. |
| AuthorAffiliation | 1 Department of Electrical Engineering, Federal University of Campina Grande, Campina Grande 58429-900, Brazil; wamberto@dee.ufcg.edu.br 4 Polytechnic School of Pernambuco, University of Pernambuco, Recife 50720-001, Brazil; madeiro@poli.br 3 Department of Computer Engineering, Federal University of Vale do São Francisco, Petrolina 56304-917, Brazil; rafael.mouraduarte@univasf.edu.br 2 Department of Electrical Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil; malencar@iecom.org.br |
| AuthorAffiliation_xml | – name: 2 Department of Electrical Engineering, Federal University of Paraíba, João Pessoa 58051-970, Brazil; malencar@iecom.org.br – name: 3 Department of Computer Engineering, Federal University of Vale do São Francisco, Petrolina 56304-917, Brazil; rafael.mouraduarte@univasf.edu.br – name: 4 Polytechnic School of Pernambuco, University of Pernambuco, Recife 50720-001, Brazil; madeiro@poli.br – name: 1 Department of Electrical Engineering, Federal University of Campina Grande, Campina Grande 58429-900, Brazil; wamberto@dee.ufcg.edu.br |
| Author_xml | – sequence: 1 givenname: Danilo B. T. orcidid: 0000-0003-1699-8883 surname: Almeida fullname: Almeida, Danilo B. T. – sequence: 2 givenname: Marcelo S. orcidid: 0000-0002-2849-1644 surname: Alencar fullname: Alencar, Marcelo S. – sequence: 3 givenname: Wamberto J. L. orcidid: 0000-0002-5065-2221 surname: Queiroz fullname: Queiroz, Wamberto J. L. – sequence: 4 givenname: Rafael M. orcidid: 0000-0001-9274-7838 surname: Duarte fullname: Duarte, Rafael M. – sequence: 5 givenname: Francisco orcidid: 0000-0002-6123-0390 surname: Madeiro fullname: Madeiro, Francisco |
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| Cites_doi | 10.1109/VTC2021-Spring51267.2021.9449014 10.1109/TVT.2018.2867606 10.1109/APCC55198.2022.9943729 10.1109/TCOMM.2018.2846272 10.1109/ICC45855.2022.9838980 10.1109/OJCOMS.2021.3089281 10.1109/ACSSC.2015.7421222 10.1109/JSYST.2023.3255865 10.1109/TWC.2017.2655515 10.1109/ACCESS.2024.3370944 10.1109/ACSSC.2018.8645135 10.1109/SPAWC.2019.8815488 10.1109/LWC.2017.2734893 10.1109/PIMRC54779.2022.9977597 10.1109/TCOMM.2020.2987311 10.1109/WCL.2013.012513.120837 10.3390/app14051684 10.1109/LCOMM.2018.2825428 10.1109/GlobalSIP.2018.8646666 10.1109/PIMRC.2017.8292302 10.1109/ICCE-TW46550.2019.8991725 10.1109/ACCESS.2022.3196000 10.1109/TWC.2019.2935434 10.1017/CBO9780511841224 10.1109/SPAWC.2015.7227028 10.1109/SPAWC.2017.8227722 10.1145/3481646.3481652 10.1109/VTCSpring.2016.7504435 10.1109/VTC2024-Spring62846.2024.10683642 10.1109/ISWCS.2018.8491054 10.1109/ACSSC.2016.7869024 10.1109/ICC.2018.8422865 10.1109/LCOMM.2023.3275054 10.1109/ICCNC.2017.7876099 10.1109/TWC.2019.2952117 10.1109/VTC2023-Spring57618.2023.10200653 |
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| SubjectTerms | Analysis cell-free channel capacity distributed architecture Distribution (Probability theory) Inequality Line of sight Monte Carlo simulation outage probability Probability density functions Random variables Statistics |
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| Title | User-Centric Cell-Free Massive Multiple-Input-Multiple-Output System with Noisy Channel Gain Estimation and Line of Sight: A Beckmann Distribution Approach |
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