WiMAX OFDMA系统中的无线资源分配算法的研究
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摘要
本文在对无线资源管理发展现状研究的基础上,针对无线资源管理技术的发展需求,对无线资源分配算法进行了研究。基于效用函数与博弈论,对WiMAX OFDMA系统中的无线资源分配算法进行了优化。
本文首先系统地介绍了OFDMA原理与系统模型,并对WiMAX OFDMA系统中的无线资源分配机制进行了讨论。在现有的算法的基础,结合微观经济学的效用函数与博弈论的理论知识,研究了两种优化算法。两种优化算法在WiMAX上行链路请求-准许调度机制基础上,分别结合效用函数与博弈论的特点,在简单的接纳控制后,合理分配无线资源,使网络总效用最大化。
最后,通过OPNET与MATLAB仿真工具对两种算法的性能进行了仿真,包括吞吐量、时延与公平性方面的比较验证,仿真结果表明:不但提高了系统吞吐量,降低了时延,而且用户数据流的公平性也得到了保证。
Based on the research of the present development and the demand of radio resource management, the allocation algorithm in radio resource management is researched. Based on the utility function and game theory, two improved algorithm are researched in the thesis.
This thesis first introduces the OFDMA system theory and system model, and discusses the wireless resource allocation mechanism in WiMAX OFDMA system. Then on the basis of the existing algorithms, combined with the utility function and game theory of microeconomics, two improved algorithms are researched. Through WiMAX request-grant scheduling mechanism and a simple admission control, they maximize the total system utility, based on the combination of QoS mechanisms and utility function and game theory.
Finally, this thesis introduces a simulation by OPNET and MATLAB, and analyses the performance of the researched algorithms, including throughput, delay and fair comparison. The result of simulation show that the algorithms can improve system throughput, reduced latency, and the fairness of the user data stream has also been assurance.
本文首先系统地介绍了OFDMA原理与系统模型,并对WiMAX OFDMA系统中的无线资源分配机制进行了讨论。在现有的算法的基础,结合微观经济学的效用函数与博弈论的理论知识,研究了两种优化算法。两种优化算法在WiMAX上行链路请求-准许调度机制基础上,分别结合效用函数与博弈论的特点,在简单的接纳控制后,合理分配无线资源,使网络总效用最大化。
最后,通过OPNET与MATLAB仿真工具对两种算法的性能进行了仿真,包括吞吐量、时延与公平性方面的比较验证,仿真结果表明:不但提高了系统吞吐量,降低了时延,而且用户数据流的公平性也得到了保证。
Based on the research of the present development and the demand of radio resource management, the allocation algorithm in radio resource management is researched. Based on the utility function and game theory, two improved algorithm are researched in the thesis.
This thesis first introduces the OFDMA system theory and system model, and discusses the wireless resource allocation mechanism in WiMAX OFDMA system. Then on the basis of the existing algorithms, combined with the utility function and game theory of microeconomics, two improved algorithms are researched. Through WiMAX request-grant scheduling mechanism and a simple admission control, they maximize the total system utility, based on the combination of QoS mechanisms and utility function and game theory.
Finally, this thesis introduces a simulation by OPNET and MATLAB, and analyses the performance of the researched algorithms, including throughput, delay and fair comparison. The result of simulation show that the algorithms can improve system throughput, reduced latency, and the fairness of the user data stream has also been assurance.
引文
[1]佟学俭,罗涛. OFDM移动通信技术原理与应用.北京.人民邮电出版社, 2003年6月.pp.10-14.
[2]王文博,郑侃.宽带无线通信OFDM技术.人民邮电出版社, 2003年11月. 8-12. pp.21-92.
[3] IEEE Standard 802.16 TM-2004 for Local and metropolitan area networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems. 2004. pp.1-324.
[4] Y.J.Zhang and K.B.Letaief. Multiuser adaptive subcarrier-and-bit allocation with adaptive cell selection for OFDM systems. IEEE Transactions on Wireless Communications, 2004. pp.1566-1575.
[5] W.Rhee and J.M.Cioffi. Increase in capacity of multiuser OFDM system using dynamic subchannel allocation.In Proceedings. IEEE Vehicular Technology Conference, 2000, pp.1085-1089.
[6] G. Song and Y. (G.) Li, Cross-layer optimization for OFDM wireless network—PartⅠ: Theoretical Framework. IEEE Trans. Wireless Commun, 2005, vol. 4, no. 2.pp. 614–624.
[7] G. Song and Y. (G.) Li, Cross-layer optimization for OFDM wireless network—Part II: Algorithm development. IEEE Trans. Wireless Commun, 2005, vol. 4, no. 2. pp. 625–634.
[8] Juncai Shi and Aiqun Hu. Maximum Utility-Based Resource Allocation Algorithm in the IEEE 802.16 OFDMA System. ICC’08, 2008. pp.311-316.
[9]金浩等.微观经济学.天津:南开大学出版社, 2004. pp.265-378.
[10]赵艳梅.基于效用的网络资源分配研究.重庆邮电大学硕士学位论文, 2006年4月.
[11] Hahne E L. Round-robin scheduling for max-min fairness in data networks. IEEE Journal on Selected Areas in Communications, 1991,9(7), pp.1024-1039.
[12] Kellyf P, Maullooa, and Tan D. Rate control for communication networks: Shadow prices, proportional fairness and stability. Jaurnal fo the Operational Reserch Society, 1998, 49(3), pp.237-252.
[13] N. Lu and J. Bigham. Utility-based Adaptive Bandwidth Allocation for Multi-Class Traffic in Wireless Networks. Proceeding of the 19th International Teletraffice Congress(ITC’19),2005, pp.879-888.
[14] Juhee Kim,Eunkyung Kim and Kyung Soo Kim. A New Efficient BS Scheduler and Scheduling Algorithm in WiBro Systems, proc.ICACT2006. 2006, vol.3, pp.1467-1470.
[15]陈敏. OPNET网络仿真.北京.清华大学出版社, 2004年4月, pp.2-5.
[16]王文博,张金文. OPNET Modeler与网络仿真.北京:人民邮电出版社, 2003年10月, 4-11, pp.151-168.
[17]张维迎.博弈论与经济学.上海.上海人民出版社, 2004年11月, pp.51-154.
[18]卫萌菡.基于博弈论的无线多媒体网络协作资源管理研究.西南交通大学生硕士学位论文, 2007年5月.
[19]冯·诺依曼,摩根斯坦著.王文玉,王宇译.博弈论与经济行为.生活·读书·新知三联书店, 2004年12月.
[20] J.Nash. The bargaining problem. Econometrics 1950 (18): pp.155-162.
[21] H. Ekstrom, A. Furuskar, J. Karlsson. Technical Solutions for the 3G Long-Term Evolution. Communications Magazine. IEEE, 2006. 44(3), pp.38–45.
[22] C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch. Multicarrier OFDM with adaptive subcarrier, bit, and power allocation. IEEE J. Sel. Areas Commun.vol. 17, no. 10, Oct. 1999, pp.1747–1758.
[23] Jang and Lee,“Transmit power adaptation for multiuser OFDM systems,”IEEE J. Sel. Areas Commun. 2003. 21(2), pp. 171–178.
[24] C. Y. Wong, C. Y. Tsui, R. S. Cheng, and K. B. Letaief. A real-time sub-carrier allocation scheme for multiple access downlink OFDM transmission. Proc. of IEEE VTC’99, 1999, pp.1124-1128.
[25] Y.J.Zhang and K.B.Letaief. Multiuser adaptive subcarrier-and-bit allocation with adaptive cell selection for OFDM systems. IEEE Transactions on Wireless Communications, 2004, pp.1566-1575.
[26]赵力强,郭乐,张国鹏.基于博弈论的无线传感器网络优化MAC协议.计算机工程, 2009年3月, pp.1-6.
[27] Hahne E L. Round-robin scheduling for max-min fairness in data networks. IEEE Journal on Selected Areas in Communications, 1991. 9(7),pp.1024-1039.
[28] Guopeng Zhang, Hailin Zhang. Adapative resource allocation for downlink OFDMA networks using cooperative game theory. Communication systems, 2008, ICCS Nov. 2008, pp. 98– 103.
[29] H. Yaiche, R.R. Mazumdar, C.Rosenbarg. A game theoretic framework for bandwidth allocation and pricing in broadband networks. IEEE/ACM Trans. Network, August 2000, pp.667-677.
[30] Chee, T.K, Cheng-Chew Lim, Jinho Chooi. A Cooperative Game Theoretic Framework for Resource Allocation in OFDMA Systems. Communication systems, 2006. ICCS Oct. 2006, pp.1– 5.
[31] Jayaparvathy. R, Geetha. S. Resource Allocation and Game Theoretic Scheduling in IEEE 802.16 Fixed Broadband Wireless Access Systems. IIT Nov. 2007, pp.63– 67.
[32] Zhu. Han, Zhu. Ji, Liu. K.J.R. Low-complexity OFDMA channels allocation with Nash bargaining solution fairness. GLOBECOM '04. IEEE, Dec. 2004, pp.3726– 3731.
[33] Yaiche, H, Mazumdar, R.R, Rosenberg, C. A game theoretic framework for bandwidth allocation and pricing in broadband networks. IEEE/ACM transactions on networking, Oct. 2000, vol.8, No.5, pp. 667 - 678.
[34] ITU-R Recommendation M.1225. Guidelines for evaluation of radio transmission technologies for IMT-2000, 1997.
[2]王文博,郑侃.宽带无线通信OFDM技术.人民邮电出版社, 2003年11月. 8-12. pp.21-92.
[3] IEEE Standard 802.16 TM-2004 for Local and metropolitan area networks Part 16: Air Interface for Fixed Broadband Wireless Access Systems. 2004. pp.1-324.
[4] Y.J.Zhang and K.B.Letaief. Multiuser adaptive subcarrier-and-bit allocation with adaptive cell selection for OFDM systems. IEEE Transactions on Wireless Communications, 2004. pp.1566-1575.
[5] W.Rhee and J.M.Cioffi. Increase in capacity of multiuser OFDM system using dynamic subchannel allocation.In Proceedings. IEEE Vehicular Technology Conference, 2000, pp.1085-1089.
[6] G. Song and Y. (G.) Li, Cross-layer optimization for OFDM wireless network—PartⅠ: Theoretical Framework. IEEE Trans. Wireless Commun, 2005, vol. 4, no. 2.pp. 614–624.
[7] G. Song and Y. (G.) Li, Cross-layer optimization for OFDM wireless network—Part II: Algorithm development. IEEE Trans. Wireless Commun, 2005, vol. 4, no. 2. pp. 625–634.
[8] Juncai Shi and Aiqun Hu. Maximum Utility-Based Resource Allocation Algorithm in the IEEE 802.16 OFDMA System. ICC’08, 2008. pp.311-316.
[9]金浩等.微观经济学.天津:南开大学出版社, 2004. pp.265-378.
[10]赵艳梅.基于效用的网络资源分配研究.重庆邮电大学硕士学位论文, 2006年4月.
[11] Hahne E L. Round-robin scheduling for max-min fairness in data networks. IEEE Journal on Selected Areas in Communications, 1991,9(7), pp.1024-1039.
[12] Kellyf P, Maullooa, and Tan D. Rate control for communication networks: Shadow prices, proportional fairness and stability. Jaurnal fo the Operational Reserch Society, 1998, 49(3), pp.237-252.
[13] N. Lu and J. Bigham. Utility-based Adaptive Bandwidth Allocation for Multi-Class Traffic in Wireless Networks. Proceeding of the 19th International Teletraffice Congress(ITC’19),2005, pp.879-888.
[14] Juhee Kim,Eunkyung Kim and Kyung Soo Kim. A New Efficient BS Scheduler and Scheduling Algorithm in WiBro Systems, proc.ICACT2006. 2006, vol.3, pp.1467-1470.
[15]陈敏. OPNET网络仿真.北京.清华大学出版社, 2004年4月, pp.2-5.
[16]王文博,张金文. OPNET Modeler与网络仿真.北京:人民邮电出版社, 2003年10月, 4-11, pp.151-168.
[17]张维迎.博弈论与经济学.上海.上海人民出版社, 2004年11月, pp.51-154.
[18]卫萌菡.基于博弈论的无线多媒体网络协作资源管理研究.西南交通大学生硕士学位论文, 2007年5月.
[19]冯·诺依曼,摩根斯坦著.王文玉,王宇译.博弈论与经济行为.生活·读书·新知三联书店, 2004年12月.
[20] J.Nash. The bargaining problem. Econometrics 1950 (18): pp.155-162.
[21] H. Ekstrom, A. Furuskar, J. Karlsson. Technical Solutions for the 3G Long-Term Evolution. Communications Magazine. IEEE, 2006. 44(3), pp.38–45.
[22] C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch. Multicarrier OFDM with adaptive subcarrier, bit, and power allocation. IEEE J. Sel. Areas Commun.vol. 17, no. 10, Oct. 1999, pp.1747–1758.
[23] Jang and Lee,“Transmit power adaptation for multiuser OFDM systems,”IEEE J. Sel. Areas Commun. 2003. 21(2), pp. 171–178.
[24] C. Y. Wong, C. Y. Tsui, R. S. Cheng, and K. B. Letaief. A real-time sub-carrier allocation scheme for multiple access downlink OFDM transmission. Proc. of IEEE VTC’99, 1999, pp.1124-1128.
[25] Y.J.Zhang and K.B.Letaief. Multiuser adaptive subcarrier-and-bit allocation with adaptive cell selection for OFDM systems. IEEE Transactions on Wireless Communications, 2004, pp.1566-1575.
[26]赵力强,郭乐,张国鹏.基于博弈论的无线传感器网络优化MAC协议.计算机工程, 2009年3月, pp.1-6.
[27] Hahne E L. Round-robin scheduling for max-min fairness in data networks. IEEE Journal on Selected Areas in Communications, 1991. 9(7),pp.1024-1039.
[28] Guopeng Zhang, Hailin Zhang. Adapative resource allocation for downlink OFDMA networks using cooperative game theory. Communication systems, 2008, ICCS Nov. 2008, pp. 98– 103.
[29] H. Yaiche, R.R. Mazumdar, C.Rosenbarg. A game theoretic framework for bandwidth allocation and pricing in broadband networks. IEEE/ACM Trans. Network, August 2000, pp.667-677.
[30] Chee, T.K, Cheng-Chew Lim, Jinho Chooi. A Cooperative Game Theoretic Framework for Resource Allocation in OFDMA Systems. Communication systems, 2006. ICCS Oct. 2006, pp.1– 5.
[31] Jayaparvathy. R, Geetha. S. Resource Allocation and Game Theoretic Scheduling in IEEE 802.16 Fixed Broadband Wireless Access Systems. IIT Nov. 2007, pp.63– 67.
[32] Zhu. Han, Zhu. Ji, Liu. K.J.R. Low-complexity OFDMA channels allocation with Nash bargaining solution fairness. GLOBECOM '04. IEEE, Dec. 2004, pp.3726– 3731.
[33] Yaiche, H, Mazumdar, R.R, Rosenberg, C. A game theoretic framework for bandwidth allocation and pricing in broadband networks. IEEE/ACM transactions on networking, Oct. 2000, vol.8, No.5, pp. 667 - 678.
[34] ITU-R Recommendation M.1225. Guidelines for evaluation of radio transmission technologies for IMT-2000, 1997.