基于博弈论的认知无线电网络跨层资源分配

伍春; 江虹; 尤晓建

doi:10.7498/aps.63.088801

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摘要

针对多跳认知无线电网络的多层资源分配问题，提出了协作去耦合方法和跨层联合方法. 协作去耦合方法首先单独完成路径选择任务，随后进行信道与功率的博弈分配；跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配. 两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则，引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择，设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择，设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能. 从促进收敛角度，选择序贯博弈并设计了具体的博弈过程，此外还分析了博弈的纳什均衡，讨论了两种算法的复杂度. 仿真结果表明，协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法.

关键词:

作者及机构信息

1.
西安电子科技大学, 综合业务网理论及关键技术国家重点实验室, 西安 710071;

2.
西南科技大学国防科技学院, 绵阳 621000

基金项目: 国家自然科学基金（批准号：61379005）和国家重点基础研究发展计划（批准号：2009CB320403）资助的课题.

参考文献

[1]	Mitola J, Maguire G Q 1999 IEEE Pers. Commun. 6 13
[2]	Rondeau T W, Le B, Rieser C J, Bostian C W 2004 Software Defined Radio Forum Technical Conference Phoenix, USA, November 15-18, 2004 pC3
[3]	Zhao Z J, Zheng S L, Shang J N, Kong X Z 2007 Acta Phys. Sin. 56 6760 (in Chinese) [赵知劲, 郑仕链, 尚俊娜, 孔宪正 2007 物理学报 56 6760]
[4]	Zhou J, Zu Y X 2010 Acta Phys. Sin. 59 7508 (in Chinese) [周杰, 俎云霄 2010 物理学报 59 7508]
[5]	Zu Y X, Zhou J, Zeng C 2010 Chin. Phys. B 19 119501
[6]	Zu Y X, Zhou J 2012 Chin. Phys. B 21 019501
[7]	Wang B, Wu Y, Liu K J R 2011 Comput. Net. 54 2537
[8]	Zhou P, Chang Y, Copeland J A 2012 IEEE J. Sel. Areas Commun. 30 54
[9]	Suris J E, Dasilva L A, Zhu H, Mackenzie A B 2007 IEEE International Conference on Communications Glasgow, Scotland June 24-28, 2007 p5282
[10]	Canales M, Gallego J R, Ciria R 2011 IEEE Vehicular Technology Conference San Francisco, USA September 5-8, 2011 p1
[11]	Wang Q, Zheng H 2006 IEEE Consumer Communications and Networking Conference Las Vegas, USA Ja-nuary 8-10, 2006 p1
[12]	Canales M, Ortin J, Gallego J R 2012 IEEE Commun. Lett. 16 654
[13]	Jiang H, Liu C B, Wu C 2013 Acta Phys. Sin. 62 038804 (in Chinese) [江虹, 刘从彬, 伍春 2013 物理学报 62 038804]
[14]	Ganchev I, Zhanlin Ji, O’Droma M 2012 2nd Baltic Congress on Future Internet Communications Vilnius, Lithuania April 25-27, 2012 p19
[15]	Stavroulaki V, Tsagkaris K, Demestichas P, Gebert J, Mueck M, Schmidt A, Ferrus R, Sallent O, Filo M, Mouton C, Rakotoharison L 2012 IEEE Commun. Mag. 50 96
[16]	Gupta P, Kumar P R 2000 IEEE Trans. Inform. Theor. 46 388
[17]	Canales M, Gallego J R, Ciria R 2011 74th IEEE Vehicular Technology Conference San Francisco, USA September 5-8, 2011 p1
[18]	Attar A, Nakhai M R, Aghvami A H 2009 IEEE Trans. Wireless Commun. 8 2121
[19]	Li Z J, Cheng C T, Huang F X, Li X 2006 J. Software 17 2373 (in Chinese) [李志洁, 程春田, 黄飞雪, 李欣 2006 软件学报 17 2373]

施引文献

[1]	Mitola J, Maguire G Q 1999 IEEE Pers. Commun. 6 13
[2]	Rondeau T W, Le B, Rieser C J, Bostian C W 2004 Software Defined Radio Forum Technical Conference Phoenix, USA, November 15-18, 2004 pC3
[3]	Zhao Z J, Zheng S L, Shang J N, Kong X Z 2007 Acta Phys. Sin. 56 6760 (in Chinese) [赵知劲, 郑仕链, 尚俊娜, 孔宪正 2007 物理学报 56 6760]
[4]	Zhou J, Zu Y X 2010 Acta Phys. Sin. 59 7508 (in Chinese) [周杰, 俎云霄 2010 物理学报 59 7508]
[5]	Zu Y X, Zhou J, Zeng C 2010 Chin. Phys. B 19 119501
[6]	Zu Y X, Zhou J 2012 Chin. Phys. B 21 019501
[7]	Wang B, Wu Y, Liu K J R 2011 Comput. Net. 54 2537
[8]	Zhou P, Chang Y, Copeland J A 2012 IEEE J. Sel. Areas Commun. 30 54
[9]	Suris J E, Dasilva L A, Zhu H, Mackenzie A B 2007 IEEE International Conference on Communications Glasgow, Scotland June 24-28, 2007 p5282
[10]	Canales M, Gallego J R, Ciria R 2011 IEEE Vehicular Technology Conference San Francisco, USA September 5-8, 2011 p1
[11]	Wang Q, Zheng H 2006 IEEE Consumer Communications and Networking Conference Las Vegas, USA Ja-nuary 8-10, 2006 p1
[12]	Canales M, Ortin J, Gallego J R 2012 IEEE Commun. Lett. 16 654
[13]	Jiang H, Liu C B, Wu C 2013 Acta Phys. Sin. 62 038804 (in Chinese) [江虹, 刘从彬, 伍春 2013 物理学报 62 038804]
[14]	Ganchev I, Zhanlin Ji, O’Droma M 2012 2nd Baltic Congress on Future Internet Communications Vilnius, Lithuania April 25-27, 2012 p19
[15]	Stavroulaki V, Tsagkaris K, Demestichas P, Gebert J, Mueck M, Schmidt A, Ferrus R, Sallent O, Filo M, Mouton C, Rakotoharison L 2012 IEEE Commun. Mag. 50 96
[16]	Gupta P, Kumar P R 2000 IEEE Trans. Inform. Theor. 46 388
[17]	Canales M, Gallego J R, Ciria R 2011 74th IEEE Vehicular Technology Conference San Francisco, USA September 5-8, 2011 p1
[18]	Attar A, Nakhai M R, Aghvami A H 2009 IEEE Trans. Wireless Commun. 8 2121
[19]	Li Z J, Cheng C T, Huang F X, Li X 2006 J. Software 17 2373 (in Chinese) [李志洁, 程春田, 黄飞雪, 李欣 2006 软件学报 17 2373]

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基于博弈论的认知无线电网络跨层资源分配

1. 西安电子科技大学, 综合业务网理论及关键技术国家重点实验室, 西安 710071;

2. 西南科技大学国防科技学院, 绵阳 621000

基金项目:

国家自然科学基金（批准号：61379005）和国家重点基础研究发展计划（批准号：2009CB320403）资助的课题.

收稿日期: 2013-11-04

修回日期: 2014-01-22

刊出日期: 2014-04-05

摘要: 针对多跳认知无线电网络的多层资源分配问题，提出了协作去耦合方法和跨层联合方法. 协作去耦合方法首先单独完成路径选择任务，随后进行信道与功率的博弈分配；跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配. 两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则，引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择，设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择，设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能. 从促进收敛角度，选择序贯博弈并设计了具体的博弈过程，此外还分析了博弈的纳什均衡，讨论了两种算法的复杂度. 仿真结果表明，协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法.

关键词:

Cross-layer resource allocation in cognitive radio networks based on game theory

1.
State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an 710071, China;

2.
School of National Defense Science and Technology, Southwest University of Science and Technology, Mianyang 621000, China

Fund Project:

Project supported by the National Natural Science Foundation of China (Grant No. 61379005) and the National Basic Research Program of China (Grant No. 2009CB320403).

Received Date: 04 November 2013

Accepted Date: 22 January 2014

Published Online: 05 April 2014

Abstract: In this paper, we propose a cooperative decoupling method and a cross-layer joint method for multi-layer resource allocation in multi-hop cognitive radio networks. In cooperative decoupling method, the task of path choosing is accomplished independently, and then the game of channel and power allocations is implemented. In cross-layer joint method, the three-layer resource of path, channel and power is allocated simultaneously by process of game. The heuristic principles of network layer, media access control layer and physical layer are employed synthetically in two methods. The degree of receiving interference and the degree of sending interference are adopted to assist path choosing. The Boltzmann exploration based on the width of permitting power is designed to select the channel and power. The means of replacement and elimination of long link or bottleneck link are proposed to further enhance network performance. The sequential game process instead of simultaneous game process is chosen because the former has better convergence property in current scenario, and the concrete process of game is provided. Moreover, the Nash equilibrium of the games and the complexity of the algorithms are analyzed and discussed. Simulation results show that the cooperative decoupling method and the cross-layer joint method have better performances in the number of success flows, the achievable data transmission rate and power consumption than the cooperative link game and the local flow game with simple decoupling.

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