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Sensors 2016, 16(10), 1675; doi:10.3390/s16101675

A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks

1
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2
Beijing Huawei Digital Technologies Co., Ltd., Beijing 100032, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 15 July 2016 / Revised: 3 September 2016 / Accepted: 7 October 2016 / Published: 12 October 2016
(This article belongs to the Section Sensor Networks)
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Abstract

Cognitive radio sensor networks are one of the kinds of application where cognitive techniques can be adopted and have many potential applications, challenges and future research trends. According to the research surveys, dynamic spectrum access is an important and necessary technology for future cognitive sensor networks. Traditional methods of dynamic spectrum access are based on spectrum holes and they have some drawbacks, such as low accessibility and high interruptibility, which negatively affect the transmission performance of the sensor networks. To address this problem, in this paper a new initialization mechanism is proposed to establish a communication link and set up a sensor network without adopting spectrum holes to convey control information. Specifically, firstly a transmission channel model for analyzing the maximum accessible capacity for three different polices in a fading environment is discussed. Secondly, a hybrid spectrum access algorithm based on a reinforcement learning model is proposed for the power allocation problem of both the transmission channel and the control channel. Finally, extensive simulations have been conducted and simulation results show that this new algorithm provides a significant improvement in terms of the tradeoff between the control channel reliability and the efficiency of the transmission channel. View Full-Text
Keywords: dynamic spectrum access; control channel; power allocation; reinforcement learning dynamic spectrum access; control channel; power allocation; reinforcement learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lin, Y.; Wang, C.; Wang, J.; Dou, Z. A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks. Sensors 2016, 16, 1675.

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