Learning-Based, Distributed Spectrum Observation System for Dynamic Spectrum Sharing in the 5G Era and Beyond
Summary :
Dynamic sharing of limited radio spectrum resources is expected to satisfy the increasing demand for spectrum resources in the upcoming 5th generation mobile communication system (5G) era and beyond. Distributed real-time spectrum sensing is a key enabler of dynamic spectrum sharing, but the costs incurred in observed-data transmission are a critical problem, especially when massive numbers of spectrum sensors are deployed. To cope with this issue, the proposed spectrum sensors learn the ambient radio environment in real-time and create a time-spectral model whose parameters are shared with servers operating in the edge-computing layer. This process makes it possible to significantly reduce the communication cost of the sensors because frequent data transmission is no longer needed while enabling the edge servers to keep up on the current status of the radio environment. On the basis of the created time-spectral model, sharable spectrum resources are dynamically harvested and allocated in terms of geospatial, temporal, and frequency-spectral domains when accepting an application for secondary-spectrum use. A web-based prototype spectrum management system has been implemented using ten servers and dozens of sensors. Measured results show that the proposed approach can reduce data traffic between the sensors and servers by 97%, achieving an average data rate of 10 kilobits per second (kbps). In addition, the basic operation flow of the prototype has been verified through a field experiment conducted at a manufacturing facility and a proof-of-concept experiment of dynamic-spectrum sharing using wireless local-area-network equipment.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E102-B No.8 pp.1526-1537
- Publication Date
- 2019/08/01
- Publicized
- 2019/02/20
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2018TTP0003
- Type of Manuscript
- Special Section PAPER (Special Section on Technology Trials and Proof-of-Concept Activities for 5G and Beyond)
- Category
Authors
Masaki KITSUNEZUKA
NEC Corporation
Kenta TSUKAMOTO
NEC Corporation
Jun SAKAI
NEC Corporation
Taichi OHTSUJI
NEC Corporation
Kazuaki KUNIHIRO
NEC Corporation
Keyword
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Masaki KITSUNEZUKA, Kenta TSUKAMOTO, Jun SAKAI, Taichi OHTSUJI, Kazuaki KUNIHIRO, "Learning-Based, Distributed Spectrum Observation System for Dynamic Spectrum Sharing in the 5G Era and Beyond" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 8, pp. 1526-1537, August 2019, doi: 10.1587/transcom.2018TTP0003.
Abstract: Dynamic sharing of limited radio spectrum resources is expected to satisfy the increasing demand for spectrum resources in the upcoming 5th generation mobile communication system (5G) era and beyond. Distributed real-time spectrum sensing is a key enabler of dynamic spectrum sharing, but the costs incurred in observed-data transmission are a critical problem, especially when massive numbers of spectrum sensors are deployed. To cope with this issue, the proposed spectrum sensors learn the ambient radio environment in real-time and create a time-spectral model whose parameters are shared with servers operating in the edge-computing layer. This process makes it possible to significantly reduce the communication cost of the sensors because frequent data transmission is no longer needed while enabling the edge servers to keep up on the current status of the radio environment. On the basis of the created time-spectral model, sharable spectrum resources are dynamically harvested and allocated in terms of geospatial, temporal, and frequency-spectral domains when accepting an application for secondary-spectrum use. A web-based prototype spectrum management system has been implemented using ten servers and dozens of sensors. Measured results show that the proposed approach can reduce data traffic between the sensors and servers by 97%, achieving an average data rate of 10 kilobits per second (kbps). In addition, the basic operation flow of the prototype has been verified through a field experiment conducted at a manufacturing facility and a proof-of-concept experiment of dynamic-spectrum sharing using wireless local-area-network equipment.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018TTP0003/_p
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@ARTICLE{e102-b_8_1526,
author={Masaki KITSUNEZUKA, Kenta TSUKAMOTO, Jun SAKAI, Taichi OHTSUJI, Kazuaki KUNIHIRO, },
journal={IEICE TRANSACTIONS on Communications},
title={Learning-Based, Distributed Spectrum Observation System for Dynamic Spectrum Sharing in the 5G Era and Beyond},
year={2019},
volume={E102-B},
number={8},
pages={1526-1537},
abstract={Dynamic sharing of limited radio spectrum resources is expected to satisfy the increasing demand for spectrum resources in the upcoming 5th generation mobile communication system (5G) era and beyond. Distributed real-time spectrum sensing is a key enabler of dynamic spectrum sharing, but the costs incurred in observed-data transmission are a critical problem, especially when massive numbers of spectrum sensors are deployed. To cope with this issue, the proposed spectrum sensors learn the ambient radio environment in real-time and create a time-spectral model whose parameters are shared with servers operating in the edge-computing layer. This process makes it possible to significantly reduce the communication cost of the sensors because frequent data transmission is no longer needed while enabling the edge servers to keep up on the current status of the radio environment. On the basis of the created time-spectral model, sharable spectrum resources are dynamically harvested and allocated in terms of geospatial, temporal, and frequency-spectral domains when accepting an application for secondary-spectrum use. A web-based prototype spectrum management system has been implemented using ten servers and dozens of sensors. Measured results show that the proposed approach can reduce data traffic between the sensors and servers by 97%, achieving an average data rate of 10 kilobits per second (kbps). In addition, the basic operation flow of the prototype has been verified through a field experiment conducted at a manufacturing facility and a proof-of-concept experiment of dynamic-spectrum sharing using wireless local-area-network equipment.},
keywords={},
doi={10.1587/transcom.2018TTP0003},
ISSN={1745-1345},
month={August},}
Copy
TY - JOUR
TI - Learning-Based, Distributed Spectrum Observation System for Dynamic Spectrum Sharing in the 5G Era and Beyond
T2 - IEICE TRANSACTIONS on Communications
SP - 1526
EP - 1537
AU - Masaki KITSUNEZUKA
AU - Kenta TSUKAMOTO
AU - Jun SAKAI
AU - Taichi OHTSUJI
AU - Kazuaki KUNIHIRO
PY - 2019
DO - 10.1587/transcom.2018TTP0003
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 8
JA - IEICE TRANSACTIONS on Communications
Y1 - August 2019
AB - Dynamic sharing of limited radio spectrum resources is expected to satisfy the increasing demand for spectrum resources in the upcoming 5th generation mobile communication system (5G) era and beyond. Distributed real-time spectrum sensing is a key enabler of dynamic spectrum sharing, but the costs incurred in observed-data transmission are a critical problem, especially when massive numbers of spectrum sensors are deployed. To cope with this issue, the proposed spectrum sensors learn the ambient radio environment in real-time and create a time-spectral model whose parameters are shared with servers operating in the edge-computing layer. This process makes it possible to significantly reduce the communication cost of the sensors because frequent data transmission is no longer needed while enabling the edge servers to keep up on the current status of the radio environment. On the basis of the created time-spectral model, sharable spectrum resources are dynamically harvested and allocated in terms of geospatial, temporal, and frequency-spectral domains when accepting an application for secondary-spectrum use. A web-based prototype spectrum management system has been implemented using ten servers and dozens of sensors. Measured results show that the proposed approach can reduce data traffic between the sensors and servers by 97%, achieving an average data rate of 10 kilobits per second (kbps). In addition, the basic operation flow of the prototype has been verified through a field experiment conducted at a manufacturing facility and a proof-of-concept experiment of dynamic-spectrum sharing using wireless local-area-network equipment.
ER -
English
