An Adaptive Cooperative Spectrum Sensing Scheme Using Reinforcement Learning for Cognitive Radio Sensor Networks

Thuc KIEU-XUAN; Insoo KOO

doi:10.1587/transcom.E94.B.1456

An Adaptive Cooperative Spectrum Sensing Scheme Using Reinforcement Learning for Cognitive Radio Sensor Networks

Thuc KIEU-XUAN, Insoo KOO

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This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.

Publication: IEICE TRANSACTIONS on Communications Vol.E94-B No.5 pp.1456-1459

Publication Date: 2011/05/01

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Online ISSN: 1745-1345

DOI: 10.1587/transcom.E94.B.1456

Type of Manuscript: LETTER

Category: Network

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Thuc KIEU-XUAN, Insoo KOO, "An Adaptive Cooperative Spectrum Sensing Scheme Using Reinforcement Learning for Cognitive Radio Sensor Networks" in IEICE TRANSACTIONS on Communications, vol. E94-B, no. 5, pp. 1456-1459, May 2011, doi: 10.1587/transcom.E94.B.1456.
Abstract: This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.
URL: https://globals.ieice.org/en_transactions/communications/10.1587/transcom.E94.B.1456/_p

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@ARTICLE{e94-b_5_1456,
author={Thuc KIEU-XUAN, Insoo KOO, },
journal={IEICE TRANSACTIONS on Communications},
title={An Adaptive Cooperative Spectrum Sensing Scheme Using Reinforcement Learning for Cognitive Radio Sensor Networks},
year={2011},
volume={E94-B},
number={5},
pages={1456-1459},
abstract={This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.},
keywords={},
doi={10.1587/transcom.E94.B.1456},
ISSN={1745-1345},
month={May},}

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TY - JOUR
TI - An Adaptive Cooperative Spectrum Sensing Scheme Using Reinforcement Learning for Cognitive Radio Sensor Networks
T2 - IEICE TRANSACTIONS on Communications
SP - 1456
EP - 1459
AU - Thuc KIEU-XUAN
AU - Insoo KOO
PY - 2011
DO - 10.1587/transcom.E94.B.1456
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E94-B
IS - 5
JA - IEICE TRANSACTIONS on Communications
Y1 - May 2011
AB - This letter proposes a novel decision fusion algorithm for cooperative spectrum sensing in cognitive radio sensor networks where a reinforcement learning algorithm is utilized at the fusion center to estimate the sensing performance of local spectrum sensing nodes. The estimates are then used to determine the weights of local decisions for the final decision making process that is based on the Chair-Vashney optimal decision fusion rule. Simulation results show that the sensing accuracy of the proposed scheme is comparable to that of the Chair-Vashney optimal decision fusion based scheme even though it does not require any knowledge of prior probabilities and local sensing performance of spectrum sensing nodes.
ER -