Robust Spectrum Sensing Algorithms for Cognitive Radio Application by Using Distributed Sensors

Yohannes D. ALEMSEGED; Chen SUN; Ha Nguyen TRAN; Hiroshi HARADA

doi:10.1587/transcom.E92.B.3616

Robust Spectrum Sensing Algorithms for Cognitive Radio Application by Using Distributed Sensors

Yohannes D. ALEMSEGED, Chen SUN, Ha Nguyen TRAN, Hiroshi HARADA

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Due to the advancement of software radio and RF technology, cognitive radio(CR) has become an enabling technology to realize dynamic spectrum access through its spectrum sensing and reconfiguration capability. Robust and reliable spectrum sensing is a key factor to discover spectrum opportunity. Single cognitive radios often fail to provide such reliable information because of their inherent sensitivity limitation. Primary signals that are subject to detection by cognitive radios may become weak due to several factors such as fading and shadowing. One approach to overcome this problem is to perform spectrum sensing by using multiple CRs or multiple spectrum sensors. This approach is known as distributed sensing because sensing is carried out through cooperation of spatially distributed sensors. In distributed sensing, sensors should perform spectrum sensing and forward the result to a destination where data fusion is carried out. Depending on the channel conditions between sensors (sensor-to-sensor channel) and between the sensor and the radio (user-channel), we explore different spectrum sensing algorithms where sensors provide the sensing information either cooperatively or independently. Moreover we investigate sensing schemes based on soft information combining (SC), hard information combining (HC). Finally we propose a two-stage detection scheme that uses both SC and HC. The newly proposed detection scheme is shown to provide improved performance compared to sensing based on either HC or SC alone. Computer simulation results are provided to illustrate the performances of the different sensing algorithms.

Publication: IEICE TRANSACTIONS on Communications Vol.E92-B No.12 pp.3616-3624

Publication Date: 2009/12/01

Publicized

Online ISSN: 1745-1345

DOI: 10.1587/transcom.E92.B.3616

Type of Manuscript: Special Section PAPER (Special Section on Dynamic Spectrum Access)

Category: Spectrum Sensing

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Yohannes D. ALEMSEGED, Chen SUN, Ha Nguyen TRAN, Hiroshi HARADA, "Robust Spectrum Sensing Algorithms for Cognitive Radio Application by Using Distributed Sensors" in IEICE TRANSACTIONS on Communications, vol. E92-B, no. 12, pp. 3616-3624, December 2009, doi: 10.1587/transcom.E92.B.3616.
Abstract: Due to the advancement of software radio and RF technology, cognitive radio(CR) has become an enabling technology to realize dynamic spectrum access through its spectrum sensing and reconfiguration capability. Robust and reliable spectrum sensing is a key factor to discover spectrum opportunity. Single cognitive radios often fail to provide such reliable information because of their inherent sensitivity limitation. Primary signals that are subject to detection by cognitive radios may become weak due to several factors such as fading and shadowing. One approach to overcome this problem is to perform spectrum sensing by using multiple CRs or multiple spectrum sensors. This approach is known as distributed sensing because sensing is carried out through cooperation of spatially distributed sensors. In distributed sensing, sensors should perform spectrum sensing and forward the result to a destination where data fusion is carried out. Depending on the channel conditions between sensors (sensor-to-sensor channel) and between the sensor and the radio (user-channel), we explore different spectrum sensing algorithms where sensors provide the sensing information either cooperatively or independently. Moreover we investigate sensing schemes based on soft information combining (SC), hard information combining (HC). Finally we propose a two-stage detection scheme that uses both SC and HC. The newly proposed detection scheme is shown to provide improved performance compared to sensing based on either HC or SC alone. Computer simulation results are provided to illustrate the performances of the different sensing algorithms.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E92.B.3616/_p

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@ARTICLE{e92-b_12_3616,
author={Yohannes D. ALEMSEGED, Chen SUN, Ha Nguyen TRAN, Hiroshi HARADA, },
journal={IEICE TRANSACTIONS on Communications},
title={Robust Spectrum Sensing Algorithms for Cognitive Radio Application by Using Distributed Sensors},
year={2009},
volume={E92-B},
number={12},
pages={3616-3624},
abstract={Due to the advancement of software radio and RF technology, cognitive radio(CR) has become an enabling technology to realize dynamic spectrum access through its spectrum sensing and reconfiguration capability. Robust and reliable spectrum sensing is a key factor to discover spectrum opportunity. Single cognitive radios often fail to provide such reliable information because of their inherent sensitivity limitation. Primary signals that are subject to detection by cognitive radios may become weak due to several factors such as fading and shadowing. One approach to overcome this problem is to perform spectrum sensing by using multiple CRs or multiple spectrum sensors. This approach is known as distributed sensing because sensing is carried out through cooperation of spatially distributed sensors. In distributed sensing, sensors should perform spectrum sensing and forward the result to a destination where data fusion is carried out. Depending on the channel conditions between sensors (sensor-to-sensor channel) and between the sensor and the radio (user-channel), we explore different spectrum sensing algorithms where sensors provide the sensing information either cooperatively or independently. Moreover we investigate sensing schemes based on soft information combining (SC), hard information combining (HC). Finally we propose a two-stage detection scheme that uses both SC and HC. The newly proposed detection scheme is shown to provide improved performance compared to sensing based on either HC or SC alone. Computer simulation results are provided to illustrate the performances of the different sensing algorithms.},
keywords={},
doi={10.1587/transcom.E92.B.3616},
ISSN={1745-1345},
month={December},}

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TY - JOUR
TI - Robust Spectrum Sensing Algorithms for Cognitive Radio Application by Using Distributed Sensors
T2 - IEICE TRANSACTIONS on Communications
SP - 3616
EP - 3624
AU - Yohannes D. ALEMSEGED
AU - Chen SUN
AU - Ha Nguyen TRAN
AU - Hiroshi HARADA
PY - 2009
DO - 10.1587/transcom.E92.B.3616
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E92-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2009
AB - Due to the advancement of software radio and RF technology, cognitive radio(CR) has become an enabling technology to realize dynamic spectrum access through its spectrum sensing and reconfiguration capability. Robust and reliable spectrum sensing is a key factor to discover spectrum opportunity. Single cognitive radios often fail to provide such reliable information because of their inherent sensitivity limitation. Primary signals that are subject to detection by cognitive radios may become weak due to several factors such as fading and shadowing. One approach to overcome this problem is to perform spectrum sensing by using multiple CRs or multiple spectrum sensors. This approach is known as distributed sensing because sensing is carried out through cooperation of spatially distributed sensors. In distributed sensing, sensors should perform spectrum sensing and forward the result to a destination where data fusion is carried out. Depending on the channel conditions between sensors (sensor-to-sensor channel) and between the sensor and the radio (user-channel), we explore different spectrum sensing algorithms where sensors provide the sensing information either cooperatively or independently. Moreover we investigate sensing schemes based on soft information combining (SC), hard information combining (HC). Finally we propose a two-stage detection scheme that uses both SC and HC. The newly proposed detection scheme is shown to provide improved performance compared to sensing based on either HC or SC alone. Computer simulation results are provided to illustrate the performances of the different sensing algorithms.
ER -