认知无线网络功率分配算法的研究
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摘要
认知无线电技术是为了充分利用稀缺的频谱资源而提出的一种新的频谱共享技术,它可以使认知用户共享授权用户的频谱,从而提高频谱利用率。本论文旨在研究认知无线网络中认知用户的发射功率对授权用户的影响,并将博弈论应用到认知用户间功率分配的问题上。
本论文对现有的认知无线电功率分配和控制方面的理论及博弈论在认知无线电中的应用方法进行研究,并在此基础上选用合适的数学模型对认知网络中次要用户对主要用户产生的干扰功率进行建模,采用基于特征函数的数值计算方法分析计算干扰功率的分布特征。针对认知无线网络中多个次要用户共存的系统模型,本文通过设计一种有效的代价函数,提出一种基于博弈论的次要用户间分布式功率分配算法,并证明了该算法纳什均衡的存在性和唯一性。并进一步考虑到网络拓扑结构快速变化的情况,在动态网络结构下对分布式功率分配算法进行改进,最后提出一种基于迭代的分布式功率分配算法,该算法能快速收敛到纳什均衡,尽可能减少功率重新分配导致的时延。仿真结果表明该迭代算法能使认知网络的收益最大化,并提高了认知网络对动态变化的适应性,对动态网络结构下的认知网络是十分有效的。
Cognitive Radio (CR) technology is proposed as a new spectrum sharing technology to fully utilize the scarce spectrum resource. It provides cognitive user a new method to share spectrum resource allocated to authority users, thus increase efficiency of spectrum usage. This paper aims at investigate the impact of interference power at authority user caused by cognitive users, and applies game theory to power allocation issue among cognitive users.
Through the research of power allocation, power control and the application of game theory in cognitive radio, this paper selects proper mathematical model to model the interference power at a primary user generated by secondary users, and studies the distribution of the interference power using a characteristic function based numerical approach. Based on the system model in which multiple secondary users coexist in cognitive radio network, this paper proposes a game-based distributed power allocation algorithm by designing an effective cost function. The existence and uniqueness of the Nash Equilibrium was proved for the algorithm. In addition, considering the network topology is rapidly changing in practical cognitive radio networks, we improve the power allocation algorithm and present a iterative distributed power allocation algorithm. The algorithm can rapidly converge to Nash Equilibrium within a few iterative steps in order to shorten the power reallocation time. The simulation results show that the proposed algorithm can achieve the maximal profits of operators for cognitive radio networks. The algorithm can adapt to dynamic change of cognitive radio network topology and is suitable for cognitive radio network in dynamic topology.
本论文对现有的认知无线电功率分配和控制方面的理论及博弈论在认知无线电中的应用方法进行研究,并在此基础上选用合适的数学模型对认知网络中次要用户对主要用户产生的干扰功率进行建模,采用基于特征函数的数值计算方法分析计算干扰功率的分布特征。针对认知无线网络中多个次要用户共存的系统模型,本文通过设计一种有效的代价函数,提出一种基于博弈论的次要用户间分布式功率分配算法,并证明了该算法纳什均衡的存在性和唯一性。并进一步考虑到网络拓扑结构快速变化的情况,在动态网络结构下对分布式功率分配算法进行改进,最后提出一种基于迭代的分布式功率分配算法,该算法能快速收敛到纳什均衡,尽可能减少功率重新分配导致的时延。仿真结果表明该迭代算法能使认知网络的收益最大化,并提高了认知网络对动态变化的适应性,对动态网络结构下的认知网络是十分有效的。
Cognitive Radio (CR) technology is proposed as a new spectrum sharing technology to fully utilize the scarce spectrum resource. It provides cognitive user a new method to share spectrum resource allocated to authority users, thus increase efficiency of spectrum usage. This paper aims at investigate the impact of interference power at authority user caused by cognitive users, and applies game theory to power allocation issue among cognitive users.
Through the research of power allocation, power control and the application of game theory in cognitive radio, this paper selects proper mathematical model to model the interference power at a primary user generated by secondary users, and studies the distribution of the interference power using a characteristic function based numerical approach. Based on the system model in which multiple secondary users coexist in cognitive radio network, this paper proposes a game-based distributed power allocation algorithm by designing an effective cost function. The existence and uniqueness of the Nash Equilibrium was proved for the algorithm. In addition, considering the network topology is rapidly changing in practical cognitive radio networks, we improve the power allocation algorithm and present a iterative distributed power allocation algorithm. The algorithm can rapidly converge to Nash Equilibrium within a few iterative steps in order to shorten the power reallocation time. The simulation results show that the proposed algorithm can achieve the maximal profits of operators for cognitive radio networks. The algorithm can adapt to dynamic change of cognitive radio network topology and is suitable for cognitive radio network in dynamic topology.
引文
[1]周贤伟,认知无线电,北京：国防工业出版社,2008.1,P1～P12
[2]Federal Communication Commission, Facilitating Opportunities for Flexible, Efficient, and Reliable Spectrum Use Employing Cognitive Radio Technologies, NPRM & Order, ET Docket no.03-108, FCC 03-322,2003.
[3]Brodersen RW, Wolisz A, Cabric D, Mishra SM, Willkomm D. A cognitive radio approach for usage of virtual unlicensed spectrum. Berkeley Wireless Research Center (BWRC). White Paper,2004.
[4]郭彩丽,张天魁,曾志民,认知无线电关键技术及应用的研究现状,北京邮电大学,2006年7月
[5]Mitola J. Cognitive radio:Making software radios more personal. IEEE Pers. Commun.,1999,6(4):13-18.
[6]Mitola J. Cognitive radio:An integrated agent architecture for software defined radios. Doctor of Technology, Royal Inst Technol (KTH), Stockholm, Sweden, 2000.
[7]S.Haykin. Cognitive radio:Brain empowered wireless communications, IEEE Journal on Selected Area of Communications vol.23, no.2, Feb.2005: 200-220.
[8]周贤伟,孟潭,王丽娜,认知无线电研究综述,电讯技术,2006年第6期
[9]Mitola J. Cognitive radio for flexible mobile multimedia communications. Mobile Multimedia Communications 1999,1999 IEEE International Workshop on, Nov. 1999,15-17:3-10.
[10]FCC Spectrum Policy Task Force. Report of the spectrum efficiency working group. Nov.2002.
[11]周小飞,张宏刚,认知无线电原理及应用,北京：北京邮电大学出版社,2007.3.P1～P13
[12]Ghasemi, Sousa ES. Collaborative spectrum sensing for opportunistic access in fading environment. Proc. IEEE DySPAN 2005
[13]Hoven N, Sahai A. Power scaling for cognitive radio wireless networks. Wireless Communications and Mobile Computing.2005(6):250-255
[14]Clemens N, Rose C. Intelligent power allocation strategies in an unlicensed spectrum. Proc. IEEE DySPAN 2005
[15]Basar T, Olsder G J. Dynamic non-cooperative game theory.2nded. New York: Academic Press,1999
[16]蒋晶晶,谭明新,认知无线电中的功率控制,华中师范大学,2009年第4期
[17]X.Liu, W.Wang, On the characteristics of spectrum-agile communication networks. Proc. IEEE DySpan, Baltimore, MD,2005
[18]Yu W, Competition and cooperation in multi-user communication environments. Ph.D.dessertation, Stanford Univ., Stanford, CA,2002.
[19]Viral Shah, Narayan Mandayam B, David Goodman J, Power control for wireless data based on utility and pricing. IEEE Trans. Vehic. Tech.,1992, 41(3):305-311.
[20]Foschini G J, Miljanic Z, A simple distributed autonomous power contril algorithm and its convergence [J]. IEEE Transaction on Vehicular Technology, 1993,42(4):641-646.
[21]Koskie S, Gajic Z, A nash game algorithm for SIR-based power control in 3G wireless DCMA networks[J]. IEEE/ACM Transaction Networking,2005, 13(5):1017-1026.
[22]赵成林,李鹏,蒋挺,快速收敛的认知无线电功率控制算法,北京邮电大学学报,2009年第1期
[23]A PHY/MAC proposal for IEEE 802.22 WRAN systems prt2:The Cognitive MAC[DB/OL].[2006-2-23].Http://www.ieeeS02.org/22/Meeting documents/2006Mar/22-06-0003-03-0000E1rIU-FT-I2R-Motorola-Phillos.Sams un g-Thomson MAC Spec.doc.
[24]徐斌阳,李少谦,认知无线电系统中的联合功率控制,电子科技大学学报,2008年第5期
[25]Allen B. MacKenzie, Luiz A. DaSilva, Game theory for wireless engineers. www.morganclaypool.com
[26]马丁J.奥斯本,阿里尔.鲁宾斯坦,博弈论教程,中国社会科学出版社,2000.4
[27]吉本斯,博弈论基础,中国社会科学出版社,1999.3
[28]Nash J, Equilibrium points in n-person Games. Proceeding of the National Academy of Science,1950,36(1)
[29]黄涛,博弈论教程—理论应用,首都经济贸易大学出版社,2004.5
[30]张峰,论博弈逻辑,学术论坛,2006(3).
[31]J. Neel, J.H. Reed, R.P. Gilles, The Role of Game Theory in the Analysis of Software Radio Networks. SDR Forum Technical Conference November,2002.
[32]S. Ginde, R. M. Buehrer, J. Neel, A Game Theoretic Analysis of the GPRS Adaptive Modulation Schemes. Fall VTC 2003.
[33]陈雷雷,基于博弈理论的供应链项目构建研究,中国优秀硕士学位论文全文数据库,2006-11-10.
[34]S.A.Jafar, S. Srinivasa. Capacity limits of cognitive radio with distributed and dynamic spectral activity. Proc. IEEE ICC06, Turkey, June 2006, pp.5742-5747
[35]J.Bater, H.P.Tan, K.N.Brown, L.Doyle. Modeling interference temperature constraints for spectrum access in cognitive radio networks. Proc. IEEE ICC07, UK, June 2007, pp.6493-6498
[36]E.S.Sousa, Performance of a spread spectrum packet radio network link in a poisson field of interferes. IEEE Trans. Infor. Theory, vol.38, no.6, pp.1743-1754, Nov.1992
[37]J.Ilow, D.Hatzinakos. Analysis alpha-stable noise modeling in a poisson field of interferes or scatters. IEEE Trans. Singnal Processing, vol.46, no.6, pp.1601-1611, June 1998
[38]P.C.Pedro, C.C.Chong, A.Giorgetti, M.Chiani, M.Z.Win. Narrowband communications in a poisson field of untra-wideband interferers. Proc.IEEE ICUWB06, USA, Mar.2006, pp.432-437
[39]S.Mangold, A.Jarosch, C.Monney. Operator assisted cognitive radio and dynamic spectrum assignment with dual beacons-detailed evaluation. Proc. First Intl. Conf. on Commun. Systems Software and Middleware, Jan.2006, pp.1-6
[40]B.Wild, K.Ramchandran. Detecting primary receivers for cognitive radio application. Proc. IEEE DySPAN05, USA, Nov.2005, pp.124-130
[41]R.Menon, R.M.Buehrer, J.H.Reed, Outage probability based comparison of underlay and overlay spectrum sharing techniques. Proc. IEEE DySPAN05, USA, Nov.2005, pp.101-109
[42]Cabric D, Mishra SM, Brodersen RW, Implementation issues in spectrum sensing for cognitive radios. Proc,38th Asilomar Conference on Signals, Systems and Computers 2004 2005 772-776
[43]E.S.Sousa, J.A.Silvester, Optimum transmission ranges in a direct-sequence spread-spectrum multihop packet radio network. IEEE J.Select.Areas Commun., vol.8,no.5,pp.762-771,1990
[44]G.L.Stuber, Principles of Mobile Communication,2nd Edition, Boston:Kluwer Academic Publisher,2001
[45]J.Abate, W.Whitt, The fourier-series method for investing transforms of probability distribution. Queueing Systems, vol.10, pp.5-88,1992
[46]Varge R.S. Matrix Iterative Analysis,2nd.New York:Springer,2000.
[2]Federal Communication Commission, Facilitating Opportunities for Flexible, Efficient, and Reliable Spectrum Use Employing Cognitive Radio Technologies, NPRM & Order, ET Docket no.03-108, FCC 03-322,2003.
[3]Brodersen RW, Wolisz A, Cabric D, Mishra SM, Willkomm D. A cognitive radio approach for usage of virtual unlicensed spectrum. Berkeley Wireless Research Center (BWRC). White Paper,2004.
[4]郭彩丽,张天魁,曾志民,认知无线电关键技术及应用的研究现状,北京邮电大学,2006年7月
[5]Mitola J. Cognitive radio:Making software radios more personal. IEEE Pers. Commun.,1999,6(4):13-18.
[6]Mitola J. Cognitive radio:An integrated agent architecture for software defined radios. Doctor of Technology, Royal Inst Technol (KTH), Stockholm, Sweden, 2000.
[7]S.Haykin. Cognitive radio:Brain empowered wireless communications, IEEE Journal on Selected Area of Communications vol.23, no.2, Feb.2005: 200-220.
[8]周贤伟,孟潭,王丽娜,认知无线电研究综述,电讯技术,2006年第6期
[9]Mitola J. Cognitive radio for flexible mobile multimedia communications. Mobile Multimedia Communications 1999,1999 IEEE International Workshop on, Nov. 1999,15-17:3-10.
[10]FCC Spectrum Policy Task Force. Report of the spectrum efficiency working group. Nov.2002.
[11]周小飞,张宏刚,认知无线电原理及应用,北京：北京邮电大学出版社,2007.3.P1～P13
[12]Ghasemi, Sousa ES. Collaborative spectrum sensing for opportunistic access in fading environment. Proc. IEEE DySPAN 2005
[13]Hoven N, Sahai A. Power scaling for cognitive radio wireless networks. Wireless Communications and Mobile Computing.2005(6):250-255
[14]Clemens N, Rose C. Intelligent power allocation strategies in an unlicensed spectrum. Proc. IEEE DySPAN 2005
[15]Basar T, Olsder G J. Dynamic non-cooperative game theory.2nded. New York: Academic Press,1999
[16]蒋晶晶,谭明新,认知无线电中的功率控制,华中师范大学,2009年第4期
[17]X.Liu, W.Wang, On the characteristics of spectrum-agile communication networks. Proc. IEEE DySpan, Baltimore, MD,2005
[18]Yu W, Competition and cooperation in multi-user communication environments. Ph.D.dessertation, Stanford Univ., Stanford, CA,2002.
[19]Viral Shah, Narayan Mandayam B, David Goodman J, Power control for wireless data based on utility and pricing. IEEE Trans. Vehic. Tech.,1992, 41(3):305-311.
[20]Foschini G J, Miljanic Z, A simple distributed autonomous power contril algorithm and its convergence [J]. IEEE Transaction on Vehicular Technology, 1993,42(4):641-646.
[21]Koskie S, Gajic Z, A nash game algorithm for SIR-based power control in 3G wireless DCMA networks[J]. IEEE/ACM Transaction Networking,2005, 13(5):1017-1026.
[22]赵成林,李鹏,蒋挺,快速收敛的认知无线电功率控制算法,北京邮电大学学报,2009年第1期
[23]A PHY/MAC proposal for IEEE 802.22 WRAN systems prt2:The Cognitive MAC[DB/OL].[2006-2-23].Http://www.ieeeS02.org/22/Meeting documents/2006Mar/22-06-0003-03-0000E1rIU-FT-I2R-Motorola-Phillos.Sams un g-Thomson MAC Spec.doc.
[24]徐斌阳,李少谦,认知无线电系统中的联合功率控制,电子科技大学学报,2008年第5期
[25]Allen B. MacKenzie, Luiz A. DaSilva, Game theory for wireless engineers. www.morganclaypool.com
[26]马丁J.奥斯本,阿里尔.鲁宾斯坦,博弈论教程,中国社会科学出版社,2000.4
[27]吉本斯,博弈论基础,中国社会科学出版社,1999.3
[28]Nash J, Equilibrium points in n-person Games. Proceeding of the National Academy of Science,1950,36(1)
[29]黄涛,博弈论教程—理论应用,首都经济贸易大学出版社,2004.5
[30]张峰,论博弈逻辑,学术论坛,2006(3).
[31]J. Neel, J.H. Reed, R.P. Gilles, The Role of Game Theory in the Analysis of Software Radio Networks. SDR Forum Technical Conference November,2002.
[32]S. Ginde, R. M. Buehrer, J. Neel, A Game Theoretic Analysis of the GPRS Adaptive Modulation Schemes. Fall VTC 2003.
[33]陈雷雷,基于博弈理论的供应链项目构建研究,中国优秀硕士学位论文全文数据库,2006-11-10.
[34]S.A.Jafar, S. Srinivasa. Capacity limits of cognitive radio with distributed and dynamic spectral activity. Proc. IEEE ICC06, Turkey, June 2006, pp.5742-5747
[35]J.Bater, H.P.Tan, K.N.Brown, L.Doyle. Modeling interference temperature constraints for spectrum access in cognitive radio networks. Proc. IEEE ICC07, UK, June 2007, pp.6493-6498
[36]E.S.Sousa, Performance of a spread spectrum packet radio network link in a poisson field of interferes. IEEE Trans. Infor. Theory, vol.38, no.6, pp.1743-1754, Nov.1992
[37]J.Ilow, D.Hatzinakos. Analysis alpha-stable noise modeling in a poisson field of interferes or scatters. IEEE Trans. Singnal Processing, vol.46, no.6, pp.1601-1611, June 1998
[38]P.C.Pedro, C.C.Chong, A.Giorgetti, M.Chiani, M.Z.Win. Narrowband communications in a poisson field of untra-wideband interferers. Proc.IEEE ICUWB06, USA, Mar.2006, pp.432-437
[39]S.Mangold, A.Jarosch, C.Monney. Operator assisted cognitive radio and dynamic spectrum assignment with dual beacons-detailed evaluation. Proc. First Intl. Conf. on Commun. Systems Software and Middleware, Jan.2006, pp.1-6
[40]B.Wild, K.Ramchandran. Detecting primary receivers for cognitive radio application. Proc. IEEE DySPAN05, USA, Nov.2005, pp.124-130
[41]R.Menon, R.M.Buehrer, J.H.Reed, Outage probability based comparison of underlay and overlay spectrum sharing techniques. Proc. IEEE DySPAN05, USA, Nov.2005, pp.101-109
[42]Cabric D, Mishra SM, Brodersen RW, Implementation issues in spectrum sensing for cognitive radios. Proc,38th Asilomar Conference on Signals, Systems and Computers 2004 2005 772-776
[43]E.S.Sousa, J.A.Silvester, Optimum transmission ranges in a direct-sequence spread-spectrum multihop packet radio network. IEEE J.Select.Areas Commun., vol.8,no.5,pp.762-771,1990
[44]G.L.Stuber, Principles of Mobile Communication,2nd Edition, Boston:Kluwer Academic Publisher,2001
[45]J.Abate, W.Whitt, The fourier-series method for investing transforms of probability distribution. Queueing Systems, vol.10, pp.5-88,1992
[46]Varge R.S. Matrix Iterative Analysis,2nd.New York:Springer,2000.