Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework
Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone....
Ausführliche Beschreibung
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| Autor*in: |
Mukherjee, Tanmay [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Wireless personal communications - Springer US, 1994, 106(2019), 2 vom: 20. Feb., Seite 789-803 |
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| Übergeordnetes Werk: |
volume:106 ; year:2019 ; number:2 ; day:20 ; month:02 ; pages:789-803 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11277-019-06190-8 |
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OLC2053826215 |
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| 520 | |a Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. | ||
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10.1007/s11277-019-06190-8 doi (DE-627)OLC2053826215 (DE-He213)s11277-019-06190-8-p DE-627 ger DE-627 rakwb eng 620 VZ Mukherjee, Tanmay verfasserin aut Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. Superstatistics -Lognormal distribution Weibull distribution Entropy Fading Shadowing Singh, Amit Kumar aut Senapati, Dilip (orcid)0000-0002-3157-4627 aut Enthalten in Wireless personal communications Springer US, 1994 106(2019), 2 vom: 20. Feb., Seite 789-803 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:106 year:2019 number:2 day:20 month:02 pages:789-803 https://doi.org/10.1007/s11277-019-06190-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 106 2019 2 20 02 789-803 |
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10.1007/s11277-019-06190-8 doi (DE-627)OLC2053826215 (DE-He213)s11277-019-06190-8-p DE-627 ger DE-627 rakwb eng 620 VZ Mukherjee, Tanmay verfasserin aut Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. Superstatistics -Lognormal distribution Weibull distribution Entropy Fading Shadowing Singh, Amit Kumar aut Senapati, Dilip (orcid)0000-0002-3157-4627 aut Enthalten in Wireless personal communications Springer US, 1994 106(2019), 2 vom: 20. Feb., Seite 789-803 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:106 year:2019 number:2 day:20 month:02 pages:789-803 https://doi.org/10.1007/s11277-019-06190-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 106 2019 2 20 02 789-803 |
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10.1007/s11277-019-06190-8 doi (DE-627)OLC2053826215 (DE-He213)s11277-019-06190-8-p DE-627 ger DE-627 rakwb eng 620 VZ Mukherjee, Tanmay verfasserin aut Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. Superstatistics -Lognormal distribution Weibull distribution Entropy Fading Shadowing Singh, Amit Kumar aut Senapati, Dilip (orcid)0000-0002-3157-4627 aut Enthalten in Wireless personal communications Springer US, 1994 106(2019), 2 vom: 20. Feb., Seite 789-803 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:106 year:2019 number:2 day:20 month:02 pages:789-803 https://doi.org/10.1007/s11277-019-06190-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 106 2019 2 20 02 789-803 |
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10.1007/s11277-019-06190-8 doi (DE-627)OLC2053826215 (DE-He213)s11277-019-06190-8-p DE-627 ger DE-627 rakwb eng 620 VZ Mukherjee, Tanmay verfasserin aut Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. Superstatistics -Lognormal distribution Weibull distribution Entropy Fading Shadowing Singh, Amit Kumar aut Senapati, Dilip (orcid)0000-0002-3157-4627 aut Enthalten in Wireless personal communications Springer US, 1994 106(2019), 2 vom: 20. Feb., Seite 789-803 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:106 year:2019 number:2 day:20 month:02 pages:789-803 https://doi.org/10.1007/s11277-019-06190-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 106 2019 2 20 02 789-803 |
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10.1007/s11277-019-06190-8 doi (DE-627)OLC2053826215 (DE-He213)s11277-019-06190-8-p DE-627 ger DE-627 rakwb eng 620 VZ Mukherjee, Tanmay verfasserin aut Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. Superstatistics -Lognormal distribution Weibull distribution Entropy Fading Shadowing Singh, Amit Kumar aut Senapati, Dilip (orcid)0000-0002-3157-4627 aut Enthalten in Wireless personal communications Springer US, 1994 106(2019), 2 vom: 20. Feb., Seite 789-803 (DE-627)188950273 (DE-600)1287489-9 (DE-576)049958909 0929-6212 nnns volume:106 year:2019 number:2 day:20 month:02 pages:789-803 https://doi.org/10.1007/s11277-019-06190-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MKW GBV_ILN_70 AR 106 2019 2 20 02 789-803 |
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Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework |
| abstract |
Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstractGer |
Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract In wireless communication channels, the signals arriving at the receiver may be of stochastic nature or be superpositioned due to non-uniform scattering and shadowing. For the ease of computation, we generally assume the mean ergodic property of communication channels which is error prone. The well known lognormal model fails to capture the extreme tail fluctuations in the presence of shadowing. In this setting, we exploit the importance of Tsallis non-extensive parameter ‘q’ to characterize various fading channels. The q-lognormal distribution captures the tail phenomena due to presence of non-extensive parameter ‘q’. In this paper, we provide an excellent agreement between the generated synthetic signal and the proposed q-Lognormal distribution for different values of parameter ‘q’. This paper also presents the analytical expression for the superstatistics Weibull/q-lognormal model to capture both fading and shadowing effects. It is observed that the Weibull/q-Lognormal model provides a better fit to the generated signal for $$q=1.8$$ in comparison to the well known Weibull/Lognormal model. Finally, we provide an excellent agreement between the derived measures viz., amount of fading, outage probability, average channel capacity with extensive Monte-Carlo simulation scheme. © Springer Science+Business Media, LLC, part of Springer Nature 2019 |
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| title_short |
Performance Evaluation of Wireless Communication Systems over Weibull/q-Lognormal Shadowed Fading Using Tsallis’ Entropy Framework |
| url |
https://doi.org/10.1007/s11277-019-06190-8 |
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| author2 |
Singh, Amit Kumar Senapati, Dilip |
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Singh, Amit Kumar Senapati, Dilip |
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| doi_str |
10.1007/s11277-019-06190-8 |
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2024-07-03T20:48:02.522Z |
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