Application of nonlinear regression in recognizing distribution of signals in wireless channels
In many applications, it is important to recognise the distribution of empirical data in almost real time. One of the specific applications is the identification of statistical models for fading in wireless systems of the base station receivers. This is one of the most important problems in spatial...
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| Format: | Article |
| Language: | English |
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Estonian Academy Publishers
2023-03-01
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| Series: | Proceedings of the Estonian Academy of Sciences |
| Subjects: | |
| Online Access: | https://kirj.ee/wp-content/plugins/kirj/pub/proc-2-2023-105-114_20230317012741.pdf |
| _version_ | 1797834992867344384 |
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| author | Dragiša Miljković Siniša Ilić Dragana Radosavljević Stefan Pitulić |
| author_facet | Dragiša Miljković Siniša Ilić Dragana Radosavljević Stefan Pitulić |
| author_sort | Dragiša Miljković |
| collection | DOAJ |
| description | In many applications, it is important to recognise the distribution of empirical data in almost real time. One of the specific applications is the identification of statistical models for fading in wireless systems of the base station receivers. This is one of the most important problems in spatial diversity. In this paper, we describe the methodology and the results of a nonlinear regression approach for recognising the distribution of the input signal with the values of its parameters. Furthermore, the proposed approach could be used for the real-time recognition of the probability distributions without any prior knowledge about the input signal. To prove its performance, the LevenbergâMarquardt nonlinear least-squares algorithm is tested on a large set of randomly generated signals with the Gamma, Rayleigh, Rician, Nakagami-m, and Weibull distributions. The experimental results demonstrate that this approach is accurate in recognizing statistical distributions from the signal. |
| first_indexed | 2024-04-09T14:47:28Z |
| format | Article |
| id | doaj.art-20612af7cffc4b599d025f859389a4a1 |
| institution | Directory Open Access Journal |
| issn | 1736-6046 1736-7530 |
| language | English |
| last_indexed | 2024-04-09T14:47:28Z |
| publishDate | 2023-03-01 |
| publisher | Estonian Academy Publishers |
| record_format | Article |
| series | Proceedings of the Estonian Academy of Sciences |
| spelling | doaj.art-20612af7cffc4b599d025f859389a4a12023-05-02T12:08:05ZengEstonian Academy PublishersProceedings of the Estonian Academy of Sciences1736-60461736-75302023-03-01722105114https://doi.org/10.3176/proc.2023.2.01https://doi.org/10.3176/proc.2023.2.01Application of nonlinear regression in recognizing distribution of signals in wireless channelsDragiša Miljković0Siniša Ilić1Dragana Radosavljević2Stefan Pitulić3Faculty of Technical Sciences, University of Pristina in Kosovska Mitrovica, Knjaza Milosa 7, 38220 Kosovska Mitrovica, SerbiaFaculty of Technical Sciences, University of Pristina in Kosovska Mitrovica, Knjaza Milosa 7, 38220 Kosovska Mitrovica, SerbiaFaculty of Technical Sciences, University of Pristina in Kosovska Mitrovica, Knjaza Milosa 7, 38220 Kosovska Mitrovica, SerbiaFaculty of Technical Sciences, University of Pristina in Kosovska Mitrovica, Knjaza Milosa 7, 38220 Kosovska Mitrovica, SerbiaIn many applications, it is important to recognise the distribution of empirical data in almost real time. One of the specific applications is the identification of statistical models for fading in wireless systems of the base station receivers. This is one of the most important problems in spatial diversity. In this paper, we describe the methodology and the results of a nonlinear regression approach for recognising the distribution of the input signal with the values of its parameters. Furthermore, the proposed approach could be used for the real-time recognition of the probability distributions without any prior knowledge about the input signal. To prove its performance, the LevenbergâMarquardt nonlinear least-squares algorithm is tested on a large set of randomly generated signals with the Gamma, Rayleigh, Rician, Nakagami-m, and Weibull distributions. The experimental results demonstrate that this approach is accurate in recognizing statistical distributions from the signal.https://kirj.ee/wp-content/plugins/kirj/pub/proc-2-2023-105-114_20230317012741.pdfdata miningclassificationnonlinear regressioncurve fittingprobability distribution. |
| spellingShingle | Dragiša Miljković Siniša Ilić Dragana Radosavljević Stefan Pitulić Application of nonlinear regression in recognizing distribution of signals in wireless channels Proceedings of the Estonian Academy of Sciences data mining classification nonlinear regression curve fitting probability distribution. |
| title | Application of nonlinear regression in recognizing distribution of signals in wireless channels |
| title_full | Application of nonlinear regression in recognizing distribution of signals in wireless channels |
| title_fullStr | Application of nonlinear regression in recognizing distribution of signals in wireless channels |
| title_full_unstemmed | Application of nonlinear regression in recognizing distribution of signals in wireless channels |
| title_short | Application of nonlinear regression in recognizing distribution of signals in wireless channels |
| title_sort | application of nonlinear regression in recognizing distribution of signals in wireless channels |
| topic | data mining classification nonlinear regression curve fitting probability distribution. |
| url | https://kirj.ee/wp-content/plugins/kirj/pub/proc-2-2023-105-114_20230317012741.pdf |
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