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
Dalam rangka kerja penderiaan spektrum berkala di mana setiap bingkai terdiri daripada blok penderiaan dan blok pemancar data, peningkatan tempoh penderiaan mengurangkan kebarangkalian kedua-dua peluang terlepas dan gangguan dengan pengguna utama, tetapi meningkatkan tempoh penderiaan juga mengurangkan kecekapan tenaga dan kecekapan penghantaran rangkaian kognitif. Oleh itu, tempoh penderiaan untuk digunakan adalah pertukaran antara prestasi penderiaan dan kecekapan sistem. Hubungan antara tempoh penderiaan dan kebarangkalian peralihan keadaan dianalisis terlebih dahulu, apabila saluran berlesen masing-masing kekal dalam keadaan melahu dan sibuk. Kemudian algoritma pengoptimuman tempoh penderiaan berdasarkan kebarangkalian peralihan keadaan dicadangkan, yang boleh mengoptimumkan tempoh penderiaan setiap bingkai secara dinamik dalam keadaan melahu/sibuk semasa dengan meramalkan kebarangkalian peralihan keadaan setiap bingkai pada permulaan keadaan semasa. Hasil analisis dan simulasi mendedahkan bahawa tempoh penderiaan optimum yang berubah-ubah masa meningkat apabila kebarangkalian peralihan keadaan meningkat dan berbanding dengan kaedah sedia ada, algoritma yang dicadangkan boleh menggunakan tempoh penderiaan sesedikit mungkin dalam setiap bingkai untuk memenuhi kekangan prestasi penderiaan supaya memaksimumkan tenaga dan kecekapan penghantaran rangkaian kognitif.
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.
Salinan
Jin-long WANG, Xiao ZHANG, Qihui WU, "State Transition Probability Based Sensing Duration Optimization Algorithm in Cognitive Radio" in IEICE TRANSACTIONS on Communications,
vol. E93-B, no. 12, pp. 3258-3265, December 2010, doi: 10.1587/transcom.E93.B.3258.
Abstract: In a periodic spectrum sensing framework where each frame consists of a sensing block and a data transmitting block, increasing sensing duration decreases the probabilities of both missed opportunity and interference with primary users, but increasing sensing duration also decreases the energy efficiency and the transmitting efficiency of the cognitive network. Therefore, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization algorithm is proposed, which can dynamically optimize the sensing duration of each frame in the current idle/busy state by predicting each frame's state transition probability at the beginning of the current state. Analysis and simulation results reveal that the time-varying optimal sensing duration increases as the state transition probability increases and compared to the existing method, the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.E93.B.3258/_p
Salinan
@ARTICLE{e93-b_12_3258,
author={Jin-long WANG, Xiao ZHANG, Qihui WU, },
journal={IEICE TRANSACTIONS on Communications},
title={State Transition Probability Based Sensing Duration Optimization Algorithm in Cognitive Radio},
year={2010},
volume={E93-B},
number={12},
pages={3258-3265},
abstract={In a periodic spectrum sensing framework where each frame consists of a sensing block and a data transmitting block, increasing sensing duration decreases the probabilities of both missed opportunity and interference with primary users, but increasing sensing duration also decreases the energy efficiency and the transmitting efficiency of the cognitive network. Therefore, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization algorithm is proposed, which can dynamically optimize the sensing duration of each frame in the current idle/busy state by predicting each frame's state transition probability at the beginning of the current state. Analysis and simulation results reveal that the time-varying optimal sensing duration increases as the state transition probability increases and compared to the existing method, the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.},
keywords={},
doi={10.1587/transcom.E93.B.3258},
ISSN={1745-1345},
month={December},}
Salinan
TY - JOUR
TI - State Transition Probability Based Sensing Duration Optimization Algorithm in Cognitive Radio
T2 - IEICE TRANSACTIONS on Communications
SP - 3258
EP - 3265
AU - Jin-long WANG
AU - Xiao ZHANG
AU - Qihui WU
PY - 2010
DO - 10.1587/transcom.E93.B.3258
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E93-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 2010
AB - In a periodic spectrum sensing framework where each frame consists of a sensing block and a data transmitting block, increasing sensing duration decreases the probabilities of both missed opportunity and interference with primary users, but increasing sensing duration also decreases the energy efficiency and the transmitting efficiency of the cognitive network. Therefore, the sensing duration to use is a trade-off between sensing performance and system efficiencies. The relationships between sensing duration and state transition probability are analyzed firstly, when the licensed channel stays in the idle and busy states respectively. Then a state transition probability based sensing duration optimization algorithm is proposed, which can dynamically optimize the sensing duration of each frame in the current idle/busy state by predicting each frame's state transition probability at the beginning of the current state. Analysis and simulation results reveal that the time-varying optimal sensing duration increases as the state transition probability increases and compared to the existing method, the proposed algorithm can use as little sensing duration in each frame as possible to satisfy the sensing performance constraints so as to maximize the energy and transmitting efficiencies of the cognitive networks.
ER -