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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.

- Publication
- IEICE TRANSACTIONS on Communications Vol.E104-B No.10 pp.1237-1248

- Publication Date
- 2021/10/01

- Publicized
- 2021/03/30

- Online ISSN
- 1745-1345

- DOI
- 10.1587/transcom.2020DSP0011

- Type of Manuscript
- Special Section PAPER (Special Section on Dynamic Spectrum Sharing for Future Wireless Systems)

- Category

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

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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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 -