The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo propõe um banco de dados de espectro baseado em medição (MSD) com distribuições de desvanecimento agrupadas para maior eficiência de armazenamento. O MSD convencional pode modelar com precisão as características reais do desvanecimento de múltiplos caminhos, traçando o histograma de dados de medição instantâneos para cada malha separada por espaço e utilizando-o em projetos de comunicação. Entretanto, se o banco de dados contiver toda uma distribuição para cada local, a quantidade de dados armazenados será extremamente grande. Como o objetivo principal do MSD é melhorar a eficiência espectral, é necessário reduzir a quantidade de dados armazenados mantendo a qualidade. O método proposto reduz a quantidade de dados armazenados estimando a distribuição da potência do sinal recebido instantâneo em cada ponto e integrando distribuições semelhantes através de clustering. Os resultados numéricos mostram que as técnicas de agrupamento podem reduzir a quantidade de dados, mantendo a precisão do MSD. Em seguida, aplicamos o método proposto à previsão da probabilidade de interrupção para a potência instantânea do sinal recebido. É revelado que a precisão da previsão é mantida mesmo quando a quantidade de dados é reduzida.
Yoji UESUGI
The University of Electro-Communications
Keita KATAGIRI
The University of Electro-Communications
Koya SATO
the Tokyo University of Science
Kei INAGE
the Tokyo Metropolitan College of Industrial Technology
Takeo FUJII
The University of Electro-Communications
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Yoji UESUGI, Keita KATAGIRI, Koya SATO, Kei INAGE, Takeo FUJII, "Clustering for Signal Power Distribution Toward Low Storage Crowdsourced Spectrum Database" in IEICE TRANSACTIONS on Communications,
vol. E104-B, no. 10, pp. 1237-1248, October 2021, doi: 10.1587/transcom.2020DSP0011.
Abstract: This paper proposes a measurement-based spectrum database (MSD) with clustered fading distributions toward greater storage efficiencies. The conventional MSD can accurately model the actual characteristics of multipath fading by plotting the histogram of instantaneous measurement data for each space-separated mesh and utilizing it in communication designs. However, if the database contains all of a distribution for each location, the amount of data stored will be extremely large. Because the main purpose of the MSD is to improve spectral efficiency, it is necessary to reduce the amount of data stored while maintaining quality. The proposed method reduces the amount of stored data by estimating the distribution of the instantaneous received signal power at each point and integrating similar distributions through clustering. Numerical results show that clustering techniques can reduce the amount of data while maintaining the accuracy of the MSD. We then apply the proposed method to the outage probability prediction for the instantaneous received signal power. It is revealed that the prediction accuracy is maintained even when the amount of data is reduced.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2020DSP0011/_p
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@ARTICLE{e104-b_10_1237,
author={Yoji UESUGI, Keita KATAGIRI, Koya SATO, Kei INAGE, Takeo FUJII, },
journal={IEICE TRANSACTIONS on Communications},
title={Clustering for Signal Power Distribution Toward Low Storage Crowdsourced Spectrum Database},
year={2021},
volume={E104-B},
number={10},
pages={1237-1248},
abstract={This paper proposes a measurement-based spectrum database (MSD) with clustered fading distributions toward greater storage efficiencies. The conventional MSD can accurately model the actual characteristics of multipath fading by plotting the histogram of instantaneous measurement data for each space-separated mesh and utilizing it in communication designs. However, if the database contains all of a distribution for each location, the amount of data stored will be extremely large. Because the main purpose of the MSD is to improve spectral efficiency, it is necessary to reduce the amount of data stored while maintaining quality. The proposed method reduces the amount of stored data by estimating the distribution of the instantaneous received signal power at each point and integrating similar distributions through clustering. Numerical results show that clustering techniques can reduce the amount of data while maintaining the accuracy of the MSD. We then apply the proposed method to the outage probability prediction for the instantaneous received signal power. It is revealed that the prediction accuracy is maintained even when the amount of data is reduced.},
keywords={},
doi={10.1587/transcom.2020DSP0011},
ISSN={1745-1345},
month={October},}
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TY - JOUR
TI - Clustering for Signal Power Distribution Toward Low Storage Crowdsourced Spectrum Database
T2 - IEICE TRANSACTIONS on Communications
SP - 1237
EP - 1248
AU - Yoji UESUGI
AU - Keita KATAGIRI
AU - Koya SATO
AU - Kei INAGE
AU - Takeo FUJII
PY - 2021
DO - 10.1587/transcom.2020DSP0011
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E104-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2021
AB - This paper proposes a measurement-based spectrum database (MSD) with clustered fading distributions toward greater storage efficiencies. The conventional MSD can accurately model the actual characteristics of multipath fading by plotting the histogram of instantaneous measurement data for each space-separated mesh and utilizing it in communication designs. However, if the database contains all of a distribution for each location, the amount of data stored will be extremely large. Because the main purpose of the MSD is to improve spectral efficiency, it is necessary to reduce the amount of data stored while maintaining quality. The proposed method reduces the amount of stored data by estimating the distribution of the instantaneous received signal power at each point and integrating similar distributions through clustering. Numerical results show that clustering techniques can reduce the amount of data while maintaining the accuracy of the MSD. We then apply the proposed method to the outage probability prediction for the instantaneous received signal power. It is revealed that the prediction accuracy is maintained even when the amount of data is reduced.
ER -