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

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

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 co...

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Bibliographic Details
Main Authors: Yun Lin, Chao Wang, Jiaxing Wang, Zheng Dou
Format: Article
Language:English
Published: MDPI AG 2016-10-01
Series:Sensors
Subjects:
dynamic spectrum access
control channel
power allocation
reinforcement learning
Online Access:http://www.mdpi.com/1424-8220/16/10/1675
id doaj-62d2f2bb90624094a4dc332f267d1af0
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spelling doaj-62d2f2bb90624094a4dc332f267d1af02020-11-25T02:30:51ZengMDPI AGSensors1424-82202016-10-011610167510.3390/s16101675s16101675A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor NetworksYun Lin0Chao Wang1Jiaxing Wang2Zheng Dou3College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaBeijing Huawei Digital Technologies Co., Ltd., Beijing 100032, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, ChinaCognitive 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.http://www.mdpi.com/1424-8220/16/10/1675dynamic spectrum accesscontrol channelpower allocationreinforcement learning
collection DOAJ
language English
format Article
sources DOAJ
author Yun Lin
Chao Wang
Jiaxing Wang
Zheng Dou
spellingShingle Yun Lin
Chao Wang
Jiaxing Wang
Zheng Dou
A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks
Sensors
dynamic spectrum access
control channel
power allocation
reinforcement learning
author_facet Yun Lin
Chao Wang
Jiaxing Wang
Zheng Dou
author_sort Yun Lin
title A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks
title_short A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks
title_full A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks
title_fullStr A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks
title_full_unstemmed A Novel Dynamic Spectrum Access Framework Based on Reinforcement Learning for Cognitive Radio Sensor Networks
title_sort novel dynamic spectrum access framework based on reinforcement learning for cognitive radio sensor networks
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2016-10-01
description 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.
topic dynamic spectrum access
control channel
power allocation
reinforcement learning
url http://www.mdpi.com/1424-8220/16/10/1675
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AT chaowang anoveldynamicspectrumaccessframeworkbasedonreinforcementlearningforcognitiveradiosensornetworks
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AT zhengdou anoveldynamicspectrumaccessframeworkbasedonreinforcementlearningforcognitiveradiosensornetworks
AT yunlin noveldynamicspectrumaccessframeworkbasedonreinforcementlearningforcognitiveradiosensornetworks
AT chaowang noveldynamicspectrumaccessframeworkbasedonreinforcementlearningforcognitiveradiosensornetworks
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AT zhengdou noveldynamicspectrumaccessframeworkbasedonreinforcementlearningforcognitiveradiosensornetworks
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