Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems
Summary :
In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E95-B No.10 pp.3365-3369
- Publication Date
- 2012/10/01
- Publicized
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.E95.B.3365
- Type of Manuscript
- LETTER
- Category
- Terrestrial Wireless Communication/Broadcasting Technologies
Authors
Keyword
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Sunyoung LEE, Kae Won CHOI, Seong-Lyun KIM, "Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems" in IEICE TRANSACTIONS on Communications,
vol. E95-B, no. 10, pp. 3365-3369, October 2012, doi: 10.1587/transcom.E95.B.3365.
Abstract: In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E95.B.3365/_p
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@ARTICLE{e95-b_10_3365,
author={Sunyoung LEE, Kae Won CHOI, Seong-Lyun KIM, },
journal={IEICE TRANSACTIONS on Communications},
title={Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems},
year={2012},
volume={E95-B},
number={10},
pages={3365-3369},
abstract={In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.},
keywords={},
doi={10.1587/transcom.E95.B.3365},
ISSN={1745-1345},
month={October},}
Copy
TY - JOUR
TI - Maximum Likelihood Detection of Random Primary Networks for Cognitive Radio Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 3365
EP - 3369
AU - Sunyoung LEE
AU - Kae Won CHOI
AU - Seong-Lyun KIM
PY - 2012
DO - 10.1587/transcom.E95.B.3365
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E95-B
IS - 10
JA - IEICE TRANSACTIONS on Communications
Y1 - October 2012
AB - In this letter, we focus on detecting a random primary user (PU) network for cognitive radio systems in a cooperative manner by using maximum likelihood (ML) detection. Different from traditional PU network models, the random PU network model in this letter considers the randomness in the PU network topology, and so is better suited for describing the infrastructure-less PU network such as an ad hoc network. Since the joint pdf required for the ML detection is hard to obtain in a closed form, we derive approximate ones from the Gaussian approximation. The performance of the proposed algorithm is comparable to the optimal one.
ER -
English
