Millimetre wave channel modeling based on grey genetic optimization model
Abstract In this paper, grey genetic optimization model (GGOM) is proposed for predicting insufficient channel parameters without increasing the amount of measurement data. Based on the millimetre wave 28 GHz indoor measurement data for both LOS and NLOS scenarios, the GGOM model is compared with tr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-06-01
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| Series: | IET Communications |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cmu2.12156 |
| _version_ | 1818517980932210688 |
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| author | Suiyan Geng Yang Wen Xiongwen Zhao Lei Zhang Suhong Chen |
| author_facet | Suiyan Geng Yang Wen Xiongwen Zhao Lei Zhang Suhong Chen |
| author_sort | Suiyan Geng |
| collection | DOAJ |
| description | Abstract In this paper, grey genetic optimization model (GGOM) is proposed for predicting insufficient channel parameters without increasing the amount of measurement data. Based on the millimetre wave 28 GHz indoor measurement data for both LOS and NLOS scenarios, the GGOM model is compared with traditional back propagation (BP) and grey model (GM) to analyse channel parameters like delay spread, excess delay and azimuth spread. Results show that the fitness of GGOM is better than the grey model in improving the stability of system. It works well with insufficient data (size less than 30) in most cases as it is set regardless of the specific scene and measurement data. This is verified by QuaDRiGa platform by generating uniformly distributed and interpolated data between the experimental measurement data. GGOM fits best with the measurement data compared with other prediction methods in channel characterization. Moreover, the mean absolute percentage error (MAPE) for GGOM is the least compared with GM and BP methods. The proposed GGOM model has good performance in modeling insufficient data of propagation channel, practically. |
| first_indexed | 2024-12-11T01:03:51Z |
| format | Article |
| id | doaj.art-7ea28e1648004741ab638091d6dc54e4 |
| institution | Directory Open Access Journal |
| issn | 1751-8628 1751-8636 |
| language | English |
| last_indexed | 2024-12-11T01:03:51Z |
| publishDate | 2021-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj.art-7ea28e1648004741ab638091d6dc54e42022-12-22T01:26:14ZengWileyIET Communications1751-86281751-86362021-06-011591240124810.1049/cmu2.12156Millimetre wave channel modeling based on grey genetic optimization modelSuiyan Geng0Yang Wen1Xiongwen Zhao2Lei Zhang3Suhong Chen4School of Electrical & Electronic Engineering North China Electric Power University Beijing ChinaSchool of Electrical & Electronic Engineering North China Electric Power University Beijing ChinaSchool of Electrical & Electronic Engineering North China Electric Power University Beijing ChinaShandong Electric Power Research Institute of the State Grid Corporation of China Jinan ChinaShandong Electric Power Research Institute of the State Grid Corporation of China Jinan ChinaAbstract In this paper, grey genetic optimization model (GGOM) is proposed for predicting insufficient channel parameters without increasing the amount of measurement data. Based on the millimetre wave 28 GHz indoor measurement data for both LOS and NLOS scenarios, the GGOM model is compared with traditional back propagation (BP) and grey model (GM) to analyse channel parameters like delay spread, excess delay and azimuth spread. Results show that the fitness of GGOM is better than the grey model in improving the stability of system. It works well with insufficient data (size less than 30) in most cases as it is set regardless of the specific scene and measurement data. This is verified by QuaDRiGa platform by generating uniformly distributed and interpolated data between the experimental measurement data. GGOM fits best with the measurement data compared with other prediction methods in channel characterization. Moreover, the mean absolute percentage error (MAPE) for GGOM is the least compared with GM and BP methods. The proposed GGOM model has good performance in modeling insufficient data of propagation channel, practically.https://doi.org/10.1049/cmu2.12156Radiowave propagationRadio links and equipmentOptimisation techniquesInterpolation and function approximation (numerical analysis)Optimisation techniquesInterpolation and function approximation (numerical analysis) |
| spellingShingle | Suiyan Geng Yang Wen Xiongwen Zhao Lei Zhang Suhong Chen Millimetre wave channel modeling based on grey genetic optimization model IET Communications Radiowave propagation Radio links and equipment Optimisation techniques Interpolation and function approximation (numerical analysis) Optimisation techniques Interpolation and function approximation (numerical analysis) |
| title | Millimetre wave channel modeling based on grey genetic optimization model |
| title_full | Millimetre wave channel modeling based on grey genetic optimization model |
| title_fullStr | Millimetre wave channel modeling based on grey genetic optimization model |
| title_full_unstemmed | Millimetre wave channel modeling based on grey genetic optimization model |
| title_short | Millimetre wave channel modeling based on grey genetic optimization model |
| title_sort | millimetre wave channel modeling based on grey genetic optimization model |
| topic | Radiowave propagation Radio links and equipment Optimisation techniques Interpolation and function approximation (numerical analysis) Optimisation techniques Interpolation and function approximation (numerical analysis) |
| url | https://doi.org/10.1049/cmu2.12156 |
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