<i>α</i>-Fluctuating Nakagami-<i>m</i> Fading Model for Wireless Communications
This research introduces and studies the performance of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-Fluctuating Nakagami-<i>m&...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3430 |
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| author | Aleksey S. Gvozdarev |
| author_facet | Aleksey S. Gvozdarev |
| author_sort | Aleksey S. Gvozdarev |
| collection | DOAJ |
| description | This research introduces and studies the performance of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-Fluctuating Nakagami-<i>m</i> model, which addresses the limitations of conventional models for wireless communications. For the assumed channel model, the research presents a complete first-order statistical description (including the probability density function (PDF), cumulative distribution function (CDF), moment generating function (MGF), and raw moments) and provides closed-form results for system performance (assessed in terms of outage probability, average bit error rate (ABER), and channel capacity). All of the expressions have the same numerical complexity as the base-line Fluctuating Nakagami-<i>m</i> model, and are accompanied by their high signal-to-noise ratio (SNR) asymptotics. The derived results helped to identify the amount of fading (AoF) and diversity/coding gain of the proposed channel model. In-depth analysis of the system performance was carried out for all possible fading channel parameter values. Numerical analysis of the proposed solutions demonstrated their high computational efficiency. The comparison with experimental results demonstrated that the model offers enhanced flexibility and better characterization of fading regimes. Numerical analysis and simulation results show a high degree of correspondence with the analytical work and help study the dependence of channel nonlinearity effects on overall system performance. |
| format | Article |
| id | doaj-art-eefcdcf7735b453a98db04155e93583e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-eefcdcf7735b453a98db04155e93583e2025-08-20T02:32:57ZengMDPI AGSensors1424-82202025-05-012511343010.3390/s25113430<i>α</i>-Fluctuating Nakagami-<i>m</i> Fading Model for Wireless CommunicationsAleksey S. Gvozdarev0Department of Intelligent Radiophysical Information Systems (IRIS), Physics Faculty, P.G. Demidov Yaroslavl State University, Yaroslavl 150003, RussiaThis research introduces and studies the performance of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>-Fluctuating Nakagami-<i>m</i> model, which addresses the limitations of conventional models for wireless communications. For the assumed channel model, the research presents a complete first-order statistical description (including the probability density function (PDF), cumulative distribution function (CDF), moment generating function (MGF), and raw moments) and provides closed-form results for system performance (assessed in terms of outage probability, average bit error rate (ABER), and channel capacity). All of the expressions have the same numerical complexity as the base-line Fluctuating Nakagami-<i>m</i> model, and are accompanied by their high signal-to-noise ratio (SNR) asymptotics. The derived results helped to identify the amount of fading (AoF) and diversity/coding gain of the proposed channel model. In-depth analysis of the system performance was carried out for all possible fading channel parameter values. Numerical analysis of the proposed solutions demonstrated their high computational efficiency. The comparison with experimental results demonstrated that the model offers enhanced flexibility and better characterization of fading regimes. Numerical analysis and simulation results show a high degree of correspondence with the analytical work and help study the dependence of channel nonlinearity effects on overall system performance.https://www.mdpi.com/1424-8220/25/11/3430fading channelstatistical descriptionFluctuating Nakagamierror rateoutagecapacity |
| spellingShingle | Aleksey S. Gvozdarev <i>α</i>-Fluctuating Nakagami-<i>m</i> Fading Model for Wireless Communications Sensors fading channel statistical description Fluctuating Nakagami error rate outage capacity |
| title | <i>α</i>-Fluctuating Nakagami-<i>m</i> Fading Model for Wireless Communications |
| title_full | <i>α</i>-Fluctuating Nakagami-<i>m</i> Fading Model for Wireless Communications |
| title_fullStr | <i>α</i>-Fluctuating Nakagami-<i>m</i> Fading Model for Wireless Communications |
| title_full_unstemmed | <i>α</i>-Fluctuating Nakagami-<i>m</i> Fading Model for Wireless Communications |
| title_short | <i>α</i>-Fluctuating Nakagami-<i>m</i> Fading Model for Wireless Communications |
| title_sort | i α i fluctuating nakagami i m i fading model for wireless communications |
| topic | fading channel statistical description Fluctuating Nakagami error rate outage capacity |
| url | https://www.mdpi.com/1424-8220/25/11/3430 |
| work_keys_str_mv | AT alekseysgvozdarev iaifluctuatingnakagamiimifadingmodelforwirelesscommunications |