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

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概要

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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.

• （社）電子情報通信学会の論文
• 2011-05-01

著者

• Koo Insoo

  School Of Electrical Engineering University Of Ulsan

• Kieu-xuan Thuc

  School Of Electrical Engineering University Of Ulsan

関連論文

• An Efficient RSS-Based Localization Scheme with Calibration in Wireless Sensor Networks
• An Efficient Weight-Based Cooperative Spectrum Sensing Scheme in Cognitive Radio Systems
• A Censor-Based Cooperative Spectrum Sensing Scheme Using Fuzzy Logic for Cognitive Radio Sensor Networks
• An Adaptive Cooperative Spectrum Sensing Scheme Using Reinforcement Learning for Cognitive Radio Sensor Networks
• A Robust Cooperative Spectrum Sensing Based on Kullback-Leibler Divergence

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