基于遗传算法的认知无线电决策引擎研究
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摘要
目前的频谱按照固定的方式被分配成授权频段和非授权频段。其中授权频段占了其中大部分资源。由于无线通信技术的飞速发展,非授权频段的频谱日益紧张,频谱资源问题成为制约其发展的瓶颈；同时,授权频段的频谱资源利用率无论是在时间上还是空间上都十分低下。认知无线电技术就是为了解决这个问题被提出的。
本文首先介绍了认知无线电技术的定义和提出的背景,并将整个认知无线电技术按照主要技术方向划分为频谱感知,频谱剪裁,频谱分配,参数设定四个方面,分别介绍了实现认知无线电的基本需求,以及当前所研究的各种技术和成果。由于认知无线电的核心在于智能的调整通信方式,保证主用户通信的条件下提高频谱使用率,而这种智能调整方式本质上可以抽象成一个在巨大解空间中寻优的问题,因此本文第二章主要介绍了非常适合解决这类问题的遗传算法以及其相关知识,通过仿真和实验论证了各种算子的应用环境和使用特点。本文的第三章介绍了基于认知无线电决策引擎的认知无线电实现方式,介绍了认知无线电决策引擎的设计,基于基本遗传算法实现了一个认知无线电决策引擎。最后,在本文的第四章,通过分析当前对于认知无线电研究的不足,基于本文第二章的相关研究,设计了两个可以提高认知引擎性能的遗传算子,并通过仿真说明了这两种算子的性能。此外,还通过理论分析,说明了这两种算子可以广泛适用于目前已有的各种基于遗传算法的认知无线电决策引擎。
Currently, spectrum is allocated into authorized and unauthorized frequency bands, and most pre-allocated parts are the authorized frequency bands. As the rapidly development of wireless communication technology, the shortage of non-authorized band spectrum is becoming a bottleneck restricting the development of wireless technology. On the other hand, resource utilization of licensed spectrum is very low both in time dimension and spatial dimension. Cognitive radio technology is proposed to solve this problem.
This paper introduces the background and the definition of cognitive radio technology. Dividing the main technology into four areas, spectrum sensing, spectrum tailoring, spectrum allocation and parameter setting, how to achieve cognitive radio's basic needs is introduced, as well as the current study in this area.'The core of cognitive radio is to adjustment means of communication intelligently to improve spectrum utilization under the conditions of insuring the main user communications, so it can be abstract as a optimization problem from a huge solution space in nature.
The second chapter introduces the ideal method to solve such problems, genetic algorithm, and shows the characteristics and environmental conditions of the genetic operators with simulation experiments. In the third part, a implement with cognitive radio decision engine is introduced. After describing the design of cognitive radio decision engine, a decision engine based on standard genetic is implemented as an example. In Chapter IV, through analysis of the lack of current research on cognitive, based on the relevant research in Chapter II, two genetic operators are designed to enhance the performance of cognitive radio decision engine. The simulation shows the performance.of these two operators and theoretical analysis shows that they can be widely applied to variety of current cognitive radio decision engine based on genetic algorithm.
本文首先介绍了认知无线电技术的定义和提出的背景,并将整个认知无线电技术按照主要技术方向划分为频谱感知,频谱剪裁,频谱分配,参数设定四个方面,分别介绍了实现认知无线电的基本需求,以及当前所研究的各种技术和成果。由于认知无线电的核心在于智能的调整通信方式,保证主用户通信的条件下提高频谱使用率,而这种智能调整方式本质上可以抽象成一个在巨大解空间中寻优的问题,因此本文第二章主要介绍了非常适合解决这类问题的遗传算法以及其相关知识,通过仿真和实验论证了各种算子的应用环境和使用特点。本文的第三章介绍了基于认知无线电决策引擎的认知无线电实现方式,介绍了认知无线电决策引擎的设计,基于基本遗传算法实现了一个认知无线电决策引擎。最后,在本文的第四章,通过分析当前对于认知无线电研究的不足,基于本文第二章的相关研究,设计了两个可以提高认知引擎性能的遗传算子,并通过仿真说明了这两种算子的性能。此外,还通过理论分析,说明了这两种算子可以广泛适用于目前已有的各种基于遗传算法的认知无线电决策引擎。
Currently, spectrum is allocated into authorized and unauthorized frequency bands, and most pre-allocated parts are the authorized frequency bands. As the rapidly development of wireless communication technology, the shortage of non-authorized band spectrum is becoming a bottleneck restricting the development of wireless technology. On the other hand, resource utilization of licensed spectrum is very low both in time dimension and spatial dimension. Cognitive radio technology is proposed to solve this problem.
This paper introduces the background and the definition of cognitive radio technology. Dividing the main technology into four areas, spectrum sensing, spectrum tailoring, spectrum allocation and parameter setting, how to achieve cognitive radio's basic needs is introduced, as well as the current study in this area.'The core of cognitive radio is to adjustment means of communication intelligently to improve spectrum utilization under the conditions of insuring the main user communications, so it can be abstract as a optimization problem from a huge solution space in nature.
The second chapter introduces the ideal method to solve such problems, genetic algorithm, and shows the characteristics and environmental conditions of the genetic operators with simulation experiments. In the third part, a implement with cognitive radio decision engine is introduced. After describing the design of cognitive radio decision engine, a decision engine based on standard genetic is implemented as an example. In Chapter IV, through analysis of the lack of current research on cognitive, based on the relevant research in Chapter II, two genetic operators are designed to enhance the performance of cognitive radio decision engine. The simulation shows the performance.of these two operators and theoretical analysis shows that they can be widely applied to variety of current cognitive radio decision engine based on genetic algorithm.
引文
[1]周贤伟,王建萍,王春江.认知无线电[M].北京：国防工业出版社,2008.
[2]FCC. ET Docket No 03-222, Notice of Proposed Rule Making and Order, December,2003.
[3]AKYILD F, ALTUNBASAK Y, FEKRI Y. Adapt Net:adaptive protocol suite for next generation wireless internet[J]. IEEE Communications Magazine,2004,42(3):128-138.
[4]MITOLA J, Cognitive radio:an integrated agent architecture for software defined Radio [D], Sweden:Royal Institute of Technology,2000.
[5]AKYILDIZ I F, LEE W Y, VURAN M C, et al. Next generation dynamic spectrum access cognitive radio wireless networks:a survey [J]. Computer networks journal (Elsevier), 2006,1(50):2127-2159.
[6]URKOWITZ H. Energy detection of unknown detection signals [J]. Proceedings of IEEE, 1967,55:523-231.
[7]特里斯.检测、估值与调制理论[M].北京：电子工业出版社,2003.
[8]WILD B, Ramehandran K. Detecting primary receivers for cognitive radio application [C]. New frontiers in dynamic spectrum access networks, Baltimore, MA, USA, November, 2005.
[9]THOMAS C C. Dynamic spectrum access in cognitive radio network [D]. Maryland: University of Maryland,2006.
[10]BRUCE A F. Cognitive Radio Technology [M]. Burlington, USA:Elsevier Inc.,2006.
[11]周小飞,张宏纲.认知无线电原理及应用[M].北京邮电大学出版社,2007.
[12]ZHENG HAITAO, PENG CHUNYI. Collaboration and Fairness in Opportunistic Spectrum Access[C]. In Proc.40th annual IEEE International Conference on Communications,[出版者不详],June 2005.
[13]MEHDI R, AHMAD R. SHARAFAT, et al. Pareto-Efficient and Goal Driven Power Control in Wireless Networks:A Game-Theoretic Approach With a Novel Pricing Scheme[J]. IEEE/ACM Transactions On Networking, April 2009,17(2):8-13.
[14]MITOLA J. Cognitive Radio for Flexible Mobile Multimedia Communications[C]. IEEE international Workshop on Mobile Multimedia Communications, San Diego:[出版者不详],USA,1999,3-10.
[15]KLOECK C, JAEKEL H, JONDRAL F K. Dynamic and local combined pricing, allocation and billing system with cognitive radios [C], The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Baltimore, USA,[出版者不详], 2005,73-81.
[16]李敏强,寇纪松.遗传算法的基本原理与应用[M].北京：科学出版社,2002.
[17]MELANIE M. An introduction to genetic algorithms [M].Cambridge, MA:The MIT Press, 1996.
[18]ERVIN L著.闵家胤译.进化-广义综合理论[M],北京：社会科学文献出版社,1988.
[19]GOLDBERG. Genetic algorithms in search optimization machine learning [M], MA:Addison — Wesley Publishing Company,1989.
[20]GOLDBERG. Computer-aided gas pipeline operation using genetic algorithm and rule leaning [D]. Michigan:University of Michigan,1983.
[21]DE J. An analysis of the behavior of a class of genetic adaptive systems[D], Michigan: University of Michigan,1975.
[22]GREFENSTETTE J. Optimization of control parameters for genetic algorithms [J]. IEEE Transaction on Systems, Man, and Cybernetics,1986,16(1):122-128.
[23]SCHAFFER J D. A study of control parameters affecting on-line performance of genetic algorithms for function optimization [C]. In Proceedings of the Third International Conference on Genetic Algorithms, San Mateo:Morgan Kaufmann Publishers,1989,51-60.
[24]吴祥兴,陈忠.混沌学导论[M].上海：上海科学技术文献出版社,1996.
[25]LORENZ E. Deterministic non-periodic flow [J]. Journal of Atmos Sci,1963,20:98-101.
[26]LI TIANYAN, YORKE J. Period three implies chaos[J], Amer. Math. Monthly,1975, 82:985-992.
[27]李兵,蒋慰孙.混沌优化方法及其应用[J].控制理论与应用,1997,14(4)：613-615.
[28]王允建,江锋锁.混沌遗传算法在函数优化中的应用[J].测控技术,2005,4(2)：65-66.
[29]张海燕,潘成欣.一种新型混沌遗传优化算法的研究[J].中国海洋大学学报,2006,36(4)：555-558.
[30]王煦法,张显俊.一种基于免疫原理的遗传算法[J].小型微型计算机系统,1999,20(2)：117-120.
[31]JIAO LICHENG, WANG LEI. A novel genetic algorithm based on immunity[J]. Systems and Humans,2000,30(5):553-561.
[32]段玉波,任伟建,董宏丽.一种新的免疫遗传算法及其应用[J].控制与决策,2005,20(10)：1185-1188.
[33]NEWMAN T R, BRETT A B. Cognitive engine implementation for wireless multi carrier transceivers[J], WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, Wirel. Commun. MobComput.2007,7:1129-1142.
[34]PROAKIS J G. Digital Communications (4th edn)[M]. McGraw-Hill:New York,2000.
[35]郑仕链,赵知劲.基于量子遗传算法的认知无线电决策引擎研究[J],物理学报,2007,11(56)：6760-6767.
[36]NEWMAN T R, RAJBANSHI R. Population Adaptation for Genetic algorithm-based Cognitive Radios, Mobile Networks & Applications,2008,13(5):279-284.
[37]郑仕链,赵知劲.基于模拟退火遗传算法的认知无线电决策引擎研究,计算机仿真,2008,1(25)：192-196.
[38]赵知劲,徐世宇.基于二进制粒子群算法的认知无线电决策引擎[J],物理学报,2009,7(58)：5118-5126.
[39]WANG GUOQIANG, LI JINLONG, ZHANG MIN, et al. Solving performance optimization problem of cognitive radio with multi objective evolutionary algorithm [J]. Computer Engineering and Applications,2007,43(20):159-162.
[40]LIU YONG, JIANG HONG, HUANG YUQING. Design of Cognitive Radio Wireless Parameters Based on Multi-objective Immune Genetic Algorithm[C],2009 International Conference on Communications and Mobile Computing, Yunnan:[出版者不详],2009,92-96.
[2]FCC. ET Docket No 03-222, Notice of Proposed Rule Making and Order, December,2003.
[3]AKYILD F, ALTUNBASAK Y, FEKRI Y. Adapt Net:adaptive protocol suite for next generation wireless internet[J]. IEEE Communications Magazine,2004,42(3):128-138.
[4]MITOLA J, Cognitive radio:an integrated agent architecture for software defined Radio [D], Sweden:Royal Institute of Technology,2000.
[5]AKYILDIZ I F, LEE W Y, VURAN M C, et al. Next generation dynamic spectrum access cognitive radio wireless networks:a survey [J]. Computer networks journal (Elsevier), 2006,1(50):2127-2159.
[6]URKOWITZ H. Energy detection of unknown detection signals [J]. Proceedings of IEEE, 1967,55:523-231.
[7]特里斯.检测、估值与调制理论[M].北京：电子工业出版社,2003.
[8]WILD B, Ramehandran K. Detecting primary receivers for cognitive radio application [C]. New frontiers in dynamic spectrum access networks, Baltimore, MA, USA, November, 2005.
[9]THOMAS C C. Dynamic spectrum access in cognitive radio network [D]. Maryland: University of Maryland,2006.
[10]BRUCE A F. Cognitive Radio Technology [M]. Burlington, USA:Elsevier Inc.,2006.
[11]周小飞,张宏纲.认知无线电原理及应用[M].北京邮电大学出版社,2007.
[12]ZHENG HAITAO, PENG CHUNYI. Collaboration and Fairness in Opportunistic Spectrum Access[C]. In Proc.40th annual IEEE International Conference on Communications,[出版者不详],June 2005.
[13]MEHDI R, AHMAD R. SHARAFAT, et al. Pareto-Efficient and Goal Driven Power Control in Wireless Networks:A Game-Theoretic Approach With a Novel Pricing Scheme[J]. IEEE/ACM Transactions On Networking, April 2009,17(2):8-13.
[14]MITOLA J. Cognitive Radio for Flexible Mobile Multimedia Communications[C]. IEEE international Workshop on Mobile Multimedia Communications, San Diego:[出版者不详],USA,1999,3-10.
[15]KLOECK C, JAEKEL H, JONDRAL F K. Dynamic and local combined pricing, allocation and billing system with cognitive radios [C], The First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Baltimore, USA,[出版者不详], 2005,73-81.
[16]李敏强,寇纪松.遗传算法的基本原理与应用[M].北京：科学出版社,2002.
[17]MELANIE M. An introduction to genetic algorithms [M].Cambridge, MA:The MIT Press, 1996.
[18]ERVIN L著.闵家胤译.进化-广义综合理论[M],北京：社会科学文献出版社,1988.
[19]GOLDBERG. Genetic algorithms in search optimization machine learning [M], MA:Addison — Wesley Publishing Company,1989.
[20]GOLDBERG. Computer-aided gas pipeline operation using genetic algorithm and rule leaning [D]. Michigan:University of Michigan,1983.
[21]DE J. An analysis of the behavior of a class of genetic adaptive systems[D], Michigan: University of Michigan,1975.
[22]GREFENSTETTE J. Optimization of control parameters for genetic algorithms [J]. IEEE Transaction on Systems, Man, and Cybernetics,1986,16(1):122-128.
[23]SCHAFFER J D. A study of control parameters affecting on-line performance of genetic algorithms for function optimization [C]. In Proceedings of the Third International Conference on Genetic Algorithms, San Mateo:Morgan Kaufmann Publishers,1989,51-60.
[24]吴祥兴,陈忠.混沌学导论[M].上海：上海科学技术文献出版社,1996.
[25]LORENZ E. Deterministic non-periodic flow [J]. Journal of Atmos Sci,1963,20:98-101.
[26]LI TIANYAN, YORKE J. Period three implies chaos[J], Amer. Math. Monthly,1975, 82:985-992.
[27]李兵,蒋慰孙.混沌优化方法及其应用[J].控制理论与应用,1997,14(4)：613-615.
[28]王允建,江锋锁.混沌遗传算法在函数优化中的应用[J].测控技术,2005,4(2)：65-66.
[29]张海燕,潘成欣.一种新型混沌遗传优化算法的研究[J].中国海洋大学学报,2006,36(4)：555-558.
[30]王煦法,张显俊.一种基于免疫原理的遗传算法[J].小型微型计算机系统,1999,20(2)：117-120.
[31]JIAO LICHENG, WANG LEI. A novel genetic algorithm based on immunity[J]. Systems and Humans,2000,30(5):553-561.
[32]段玉波,任伟建,董宏丽.一种新的免疫遗传算法及其应用[J].控制与决策,2005,20(10)：1185-1188.
[33]NEWMAN T R, BRETT A B. Cognitive engine implementation for wireless multi carrier transceivers[J], WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, Wirel. Commun. MobComput.2007,7:1129-1142.
[34]PROAKIS J G. Digital Communications (4th edn)[M]. McGraw-Hill:New York,2000.
[35]郑仕链,赵知劲.基于量子遗传算法的认知无线电决策引擎研究[J],物理学报,2007,11(56)：6760-6767.
[36]NEWMAN T R, RAJBANSHI R. Population Adaptation for Genetic algorithm-based Cognitive Radios, Mobile Networks & Applications,2008,13(5):279-284.
[37]郑仕链,赵知劲.基于模拟退火遗传算法的认知无线电决策引擎研究,计算机仿真,2008,1(25)：192-196.
[38]赵知劲,徐世宇.基于二进制粒子群算法的认知无线电决策引擎[J],物理学报,2009,7(58)：5118-5126.
[39]WANG GUOQIANG, LI JINLONG, ZHANG MIN, et al. Solving performance optimization problem of cognitive radio with multi objective evolutionary algorithm [J]. Computer Engineering and Applications,2007,43(20):159-162.
[40]LIU YONG, JIANG HONG, HUANG YUQING. Design of Cognitive Radio Wireless Parameters Based on Multi-objective Immune Genetic Algorithm[C],2009 International Conference on Communications and Mobile Computing, Yunnan:[出版者不详],2009,92-96.