基于博弈论的无线资源分配研究
|
摘要
正交频分复用多址(OFDMA)系统已经成为第四代移动通信的主流多址技术,将其与自适应技术相结合,能充分利用无线信道中的频率分集和多用户分集,提升无线通信系统的频谱效率。博弈论是应用数学的重要分支,是研究具有竞争性质现象的数学理论和方法。将博弈论与无线通信系统相结合,能够产生新的研究思路和资源分配算法。本博士论文的主要贡献是基于博弈论为资源分配领域提供有价值的研究成果,包括提出创新性的概念、具有普遍意义的分析思路和多个资源分配算法。
在比例公平研究领域,本论文首次将拍卖理论与比例公平准则相结合,采用最优化理论对基于比例公平准则的目标函数进行深入分析,详细讨论资源分配过程中隐含的经济学含义。结合拍卖理论给出基于优先级拍卖的理论框架,并据此提出基于优先级拍卖的资源调度方案。仿真结果表明,基于拍卖理论的资源分配算法能够满足上行OFDMA系统的比例公平调度要求,将比例公平因子设定为特定值时,所提方案还能实现纳什公平、最大-最小公平,表明了研究成果的普遍意义。
在OFDMA系统的统一资源分配策略研究领域,本论文克服前人仅局限于某个特定目标函数进行设计算法的缺陷,采用最优化理论、合作博弈论和矩阵论寻找多个目标函数共性。与前人工作最大的不同是,本文重新定义了博弈过程中参与人的概念,提出多个具有创新性的概念、结论以及博弈思路,并据此提出统一资源分配方案——同时议价方案。仿真结果表明,统一资源分配方案不仅能在大多数情况下优于已知频谱效率最高的算法,实现纳什公平准则、最大-最小公平准则的资源调度需求,并能克服前人提出的实现纳什公平算法中的不收敛问题,表明统一资源分配策略研究的广泛意义。
在基于部分信道状态信息的资源分配策略研究过程中,本论文主要针对快速衰落信道情形下的通信过程进行分析。依照中断理论中的信道建模过程,将信道分解成快速衰落和慢速衰落两部分,对信道在时间域采用二维建模。以最优化理论为分析工具,对发射功率限制进行放缩,并采用博弈论中的剩余函数对目标函数的最优解进行分析。在将上述分析过程推广到离散数据块情形,修正剩余函数的概念,设计出迭代剩余函数平衡算法,实现了非精确信道情形下的最优资源调度。剩余函数理论是本章研究的核心,为应用博弈论研究更复杂的中断问题做好了理论铺垫。
在抑制多小区干扰的研究领域,本论文重点讨论小区间不交互信息前提下的分布式干扰抑制方案。首先分析单载波情形小区间干扰对系统总容量的影响规律,继而将研究结论推广到多载波情形。根据上述理论分析成果设计出分布式干扰抑制算法,使得每个小区独立负责本小区用户的资源调度过程,并且基站控制器负责收集各个小区的分配信息。通过采用适当的方法,基站控制器对各个子载波在各个扇区的使用情况进行判断、调整,以找到合适的纳什均衡点。仿真结果表明,本文提出的算法能够显著提升传统分布式方案的频谱效率和归一化频谱效率,为无线通信系统节省更多的功率。
在认知无线电系统的频谱共享领域,本论文主要研究前人没有涉及的基于OFDM的认知无线电系统的频谱共享方案。通过采用干扰温度模型对主用户受到的干扰进行保护,并将每个认知无线电用户建模为一对收发机,本文依照主从系统之间的交互信息种类设计出三个分布式频谱共享方法。为应用经典博弈论,本文提出放缩博弈的概念以处理干扰温度限制,并论证了放缩博弈的收敛性、纳什均衡点的唯一性以及其与原博弈过程的等效性。仿真结果表明,所设计的三个分布式算法能依据交互信息种类实现认知用户的灵活频谱接入,有效解决基于OFDM的认知无线电系统的共道干扰限制问题。
OFDMA has become the key transmission technologies for 4G mobile communication systems. Combining with adaptive technologies, frequency diversity and multiuser diversity can be effectively utilized to enhance the spectral efficiency of wireless communication systems. Game theory is a good mathematical tool for researching the competing behaviors and it has been widely used in wireless communication systems. Game theory can provide innovative research thought and algorithms to enhance the performance of wirless communication systems. Based on game theory, the main contribution of this doctoral dissertation is to provide significative research results for resource allocation field, including innovative concepts, universal analytical thought and resource allocation algorithms.
In the field of proportional fairness, we introduce auction theory to research it for the first time. Based on optimization theory, the proportional fair objective function is investigated in detail, and the connotive economic meaning of the resource allocation process is also shown. Prirotiy-ranked bargaining framework is then proposed from auction theory. Simulation results show that the proposed scheme can realize proportional fairness criterion for uplink OFDMA system. When the proportional fairness index is set to proper value, the proposed scheme can realize the results of Nash fairness and max-min fairness, which embodies the universial meaning of the research work.
Considering the fact that previous work in dynamic resource allocation field mainly focus on designing algorithms for a certain objective function, we target to propose a unified resource allocation framework in this dissertation. The mathematical tools include optimization theory, non-cooperative game theory and matrix theory. The most differences between previous works are:the concept of player is revised; several innovative concepts, theories and bargaining principle are proposed. Then the unified resource allocation scheme-simultaneously bargaining algorithm is proposed. Simulation results show that simultaneously bargaining algorithm can outperform the most efficient suboptimal schemes in most cases, and it can also realize Nash fairness and max-min fairness. In addition, the divergency of previous algorithm for Nash fairness is also overcome. These facts denote the universial meaning of the proposed unified resource allocation scheme.
In the case of partial channel side information, we investigate the optimal resource allocation scheme for the case of fast fading. According to outage theory, the fading channel is modeled as two time-scale channel. Optimization theory is utilized to analyze the objective function, and surplus function in the economic field is utilized to characterize the optimal solution. By revising the concept of surplus function, the analyses are extended to discrete block case and optimial resource allocation algorithm is obtained for non-accurate fading channel. Surplus function is the key technology of this chapter's research work and it can analyze more complex outage issues by combining with more game theoretical thoughts.
In the field of multi-cell resource allocation, we dedicate to proposing distributed interference suppressing algorithm. Starting from single subcarrier case, the effect of inter-cell interference to the total capacity is discussed in detail, and then the analytical result is extended to multi-carrier case. The proposed distributed scheme allows each cell to allocate the resources independently, and the base station controller is responsible for gathering the allocation information. By checking out each subcarrier, the base station controller would evaluate each base station's sharing behavior, and adjust the Nash equilibrium to enhance the performance. Simultion results show that the proposed scheme can greatly enhance conventional distributed scheme's spectral efficiency and normalized spectral efficiency, and save transmission power for wireless communication system.
In the field of cognitive radio, we mainly investigate the distributed spectrum sharing technologies. By modeling each cognitive radio user as a pair of transmitter and receiver, we set interference temperature limit for protecting the primary system and research distributed spectrum sharing behaviors based on the kinds of exchange information. In order to utilize the classic game theory, we propose the concept of relaxed game to deal with the interference temperature limit. The convergence issue, uniqueness of Nash equilibrium and the equivalence to original game are discussed in great detail. Simulation results show that the proposed three schemes can effectively deal with co-channel interference limit issues, and allow the cognitive radio system to flexibly utilize the unlicensed spectrum and work harmoniously with the primary system.
在比例公平研究领域,本论文首次将拍卖理论与比例公平准则相结合,采用最优化理论对基于比例公平准则的目标函数进行深入分析,详细讨论资源分配过程中隐含的经济学含义。结合拍卖理论给出基于优先级拍卖的理论框架,并据此提出基于优先级拍卖的资源调度方案。仿真结果表明,基于拍卖理论的资源分配算法能够满足上行OFDMA系统的比例公平调度要求,将比例公平因子设定为特定值时,所提方案还能实现纳什公平、最大-最小公平,表明了研究成果的普遍意义。
在OFDMA系统的统一资源分配策略研究领域,本论文克服前人仅局限于某个特定目标函数进行设计算法的缺陷,采用最优化理论、合作博弈论和矩阵论寻找多个目标函数共性。与前人工作最大的不同是,本文重新定义了博弈过程中参与人的概念,提出多个具有创新性的概念、结论以及博弈思路,并据此提出统一资源分配方案——同时议价方案。仿真结果表明,统一资源分配方案不仅能在大多数情况下优于已知频谱效率最高的算法,实现纳什公平准则、最大-最小公平准则的资源调度需求,并能克服前人提出的实现纳什公平算法中的不收敛问题,表明统一资源分配策略研究的广泛意义。
在基于部分信道状态信息的资源分配策略研究过程中,本论文主要针对快速衰落信道情形下的通信过程进行分析。依照中断理论中的信道建模过程,将信道分解成快速衰落和慢速衰落两部分,对信道在时间域采用二维建模。以最优化理论为分析工具,对发射功率限制进行放缩,并采用博弈论中的剩余函数对目标函数的最优解进行分析。在将上述分析过程推广到离散数据块情形,修正剩余函数的概念,设计出迭代剩余函数平衡算法,实现了非精确信道情形下的最优资源调度。剩余函数理论是本章研究的核心,为应用博弈论研究更复杂的中断问题做好了理论铺垫。
在抑制多小区干扰的研究领域,本论文重点讨论小区间不交互信息前提下的分布式干扰抑制方案。首先分析单载波情形小区间干扰对系统总容量的影响规律,继而将研究结论推广到多载波情形。根据上述理论分析成果设计出分布式干扰抑制算法,使得每个小区独立负责本小区用户的资源调度过程,并且基站控制器负责收集各个小区的分配信息。通过采用适当的方法,基站控制器对各个子载波在各个扇区的使用情况进行判断、调整,以找到合适的纳什均衡点。仿真结果表明,本文提出的算法能够显著提升传统分布式方案的频谱效率和归一化频谱效率,为无线通信系统节省更多的功率。
在认知无线电系统的频谱共享领域,本论文主要研究前人没有涉及的基于OFDM的认知无线电系统的频谱共享方案。通过采用干扰温度模型对主用户受到的干扰进行保护,并将每个认知无线电用户建模为一对收发机,本文依照主从系统之间的交互信息种类设计出三个分布式频谱共享方法。为应用经典博弈论,本文提出放缩博弈的概念以处理干扰温度限制,并论证了放缩博弈的收敛性、纳什均衡点的唯一性以及其与原博弈过程的等效性。仿真结果表明,所设计的三个分布式算法能依据交互信息种类实现认知用户的灵活频谱接入,有效解决基于OFDM的认知无线电系统的共道干扰限制问题。
OFDMA has become the key transmission technologies for 4G mobile communication systems. Combining with adaptive technologies, frequency diversity and multiuser diversity can be effectively utilized to enhance the spectral efficiency of wireless communication systems. Game theory is a good mathematical tool for researching the competing behaviors and it has been widely used in wireless communication systems. Game theory can provide innovative research thought and algorithms to enhance the performance of wirless communication systems. Based on game theory, the main contribution of this doctoral dissertation is to provide significative research results for resource allocation field, including innovative concepts, universal analytical thought and resource allocation algorithms.
In the field of proportional fairness, we introduce auction theory to research it for the first time. Based on optimization theory, the proportional fair objective function is investigated in detail, and the connotive economic meaning of the resource allocation process is also shown. Prirotiy-ranked bargaining framework is then proposed from auction theory. Simulation results show that the proposed scheme can realize proportional fairness criterion for uplink OFDMA system. When the proportional fairness index is set to proper value, the proposed scheme can realize the results of Nash fairness and max-min fairness, which embodies the universial meaning of the research work.
Considering the fact that previous work in dynamic resource allocation field mainly focus on designing algorithms for a certain objective function, we target to propose a unified resource allocation framework in this dissertation. The mathematical tools include optimization theory, non-cooperative game theory and matrix theory. The most differences between previous works are:the concept of player is revised; several innovative concepts, theories and bargaining principle are proposed. Then the unified resource allocation scheme-simultaneously bargaining algorithm is proposed. Simulation results show that simultaneously bargaining algorithm can outperform the most efficient suboptimal schemes in most cases, and it can also realize Nash fairness and max-min fairness. In addition, the divergency of previous algorithm for Nash fairness is also overcome. These facts denote the universial meaning of the proposed unified resource allocation scheme.
In the case of partial channel side information, we investigate the optimal resource allocation scheme for the case of fast fading. According to outage theory, the fading channel is modeled as two time-scale channel. Optimization theory is utilized to analyze the objective function, and surplus function in the economic field is utilized to characterize the optimal solution. By revising the concept of surplus function, the analyses are extended to discrete block case and optimial resource allocation algorithm is obtained for non-accurate fading channel. Surplus function is the key technology of this chapter's research work and it can analyze more complex outage issues by combining with more game theoretical thoughts.
In the field of multi-cell resource allocation, we dedicate to proposing distributed interference suppressing algorithm. Starting from single subcarrier case, the effect of inter-cell interference to the total capacity is discussed in detail, and then the analytical result is extended to multi-carrier case. The proposed distributed scheme allows each cell to allocate the resources independently, and the base station controller is responsible for gathering the allocation information. By checking out each subcarrier, the base station controller would evaluate each base station's sharing behavior, and adjust the Nash equilibrium to enhance the performance. Simultion results show that the proposed scheme can greatly enhance conventional distributed scheme's spectral efficiency and normalized spectral efficiency, and save transmission power for wireless communication system.
In the field of cognitive radio, we mainly investigate the distributed spectrum sharing technologies. By modeling each cognitive radio user as a pair of transmitter and receiver, we set interference temperature limit for protecting the primary system and research distributed spectrum sharing behaviors based on the kinds of exchange information. In order to utilize the classic game theory, we propose the concept of relaxed game to deal with the interference temperature limit. The convergence issue, uniqueness of Nash equilibrium and the equivalence to original game are discussed in great detail. Simulation results show that the proposed three schemes can effectively deal with co-channel interference limit issues, and allow the cognitive radio system to flexibly utilize the unlicensed spectrum and work harmoniously with the primary system.
引文
[1]Federal Communication Commission, "Spectrum Policy Task Force," Rep. ET Docket, no.02-135, Nov.2002.
[2]Mitola, J., Ⅲ and Maguire, G.Q., Jr., "Cognitive Radio:Making Software Radios more Personal," IEEE Personal Commun., vol.6, no.4, pp.13-18, Aug.1999.
[3]Mitola J., "Cognitive Radio:An Integrated Agent Architecture for Software Defined Radio," Dissertation, Doctor of Technology, Royal Inst Technol (KTH), Stockholm, Sweden,2000.
[4]Simon Haykin, "Cognitive Radio:Brain-Empowered Wireless Communications," IEEE J. Sel. Areas Commun., vol.23, no.2, pp.201-220, Feb 2005.
[5]佟学俭,罗涛,OFDM移动通信技术原理与应用,人民邮电出版社,2003年6月.
[6]C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch, "Multiuser OFDM with Adaptive Subcarrier, Bit, and Power Allocation," IEEE J. Sel. Areas Commun., vol.17, no.10, pp.1747-1758, Oct 1999.
[7]K. Kim, Y. Han, and S. L. Kim, "Joint Subcarrier and Power Allocation in Uplink OFDMA Systems," IEEE Commun. Lett., vol.9, no.6, pp.526-528, Jun 2005.
[8]W. Yu, G. Ginis, and J. M. Cioffi, "Distributed Multiuser Power Control for Digital Subscriber Lines," IEEE J. Sel. Areas Commun., vol.20, no.5, pp. 1105-1115, Jun 2002.
[9]W. Yu and J. M. Cioffi, "FDMA Capacity of Gaussian Multiple-access Channels with ISI," IEEE Trans. Commun., vol.50, no.1, pp.102-111, Jan 2002.
[10]H. Liu and G. Li, OFDM-Based Broadband Wireless Networks, John Wiley & Sons, Inc., Hoboken, New Jersey,2005.
[11]Zhu. H, Zhu. J and Liu K.J.R, "Fair Multiuser Channel Allocation for OFDMA Networks Using Nash Bargaining Solutions and Coalitions," IEEE Trans. Commun., vol.53, no.8, pp.1366-1376, Aug 2005.
[12]W. Rhee and J. M. Cioffi, "Increase in Capacity of Multiuser OFDM System Using Dynamic Subchannel Allocation," in Proc. of IEEE VTC, pp.1085-1089, May 2000.
[13]F. P. Kelly, A. K. Maulloo, D. Tan. "Rate Control for Communication Networks Shadow Prices, Proportional Fairness and Stability," The Journal of the Operational Research Society, vol.49, no.3, pp.237-252,1998.
[14]Yaiche. H., Mazumdar. R.R., and Rosenberg. C, "A Game Theoretic Framework for Bandwidth Allocation and Pricing in Broadband Networks," IEEE/ACM Trans. Netw., vol.8, no.5, pp.667-678, Oct 2000.
[15]G. Caire, G. Taricco and E. Biglieri., "Optimum Power Control over Fading Channels," IEEE Trans. Info. Theory, vol.45, no.5, pp.1468-1489, Jul 1999.
[16]J. Papandriopoulos, J. Evans, and S. Dey, "Optimal Power Control for Rayleigh-Faded Multiuser Systems With Outage Constraints," IEEE Trans. Wireless Commun., vol.4, no.6, pp.2705-2715, Nov 2005.
[17]S. Dey. and J. Evans., "Outage Capacity and Optimal Power Allocation for Multiple Time-Scale Parallel Fading Channels," IEEE Trans. Wireless Commun., vol.6, no.7, pp.2369-2373, Jul 2007.
[18]K. K Wong and J. Chen., "Near-Optimal Power Allocation and Multiuser Scheduling with Outage Capacity Constraints Exploiting Only Channel Statistics," IEEE Trans. Wireless Commun. vol.7, no.3, pp.812-818, Mar 2008.
[19]L. Zeng, Y. Liu and etal, "Optimal Power and Subcarrier Allocation with Partial Side Information for Downlink OFDMA System," accepted by Journal of China Universities of Posts and Telecommunications, to be published.
[20]Drew Fudenberg and Jean Tirole, Game theory, The MIT Press:London, England,1998.
[21]Robert J. Barro and Xavier Sala-i-Martin, Economic Growth, MIT Press: London, England,1999.
[22]S. Kandukuri and S. Boyd, "Optimal Power Control in Interference-Limited Fading Wireless Channels with Outage-Probability Specifications," IEEE Trans. Wireless Commun., vol.1, no.1, pp.46-55, Jan 2002.
[23]G. Li and H. Liu, "Downlink Dynamic Resource Allocation for Multi-cell OFDMA System", in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol.1, pp.9-12, Nov 2003.
[24]Hojoong Kwon and Byeong Gi Lee, "Distributed Resource Allocation through Noncooperative Game Approach in Multi-cell OFDMA Systems," in Proc. of IEEE ICC, pp.4345-4350, Jun 2006.
[25]Zhu Han, Zhu Ji, and Liu K.J.R., "Non-Cooperative Resource Competition Game by Virtual Referee in Multi-Cell OFDMA Networks," IEEE J. Sel. Areas Commun., vol.25, no.6, pp.1079-1090, Aug 2007.
[26]J. Huang, Berry R.A. and Honig M.L., "Distributed Interference Compensation for Wireless Networks," IEEE J. Sel. Areas Commun., vol.24, no.5, pp. 1074-1084, May 2006.
[27]Ian F. Akyildiz, Won-Yeol Lee, Mehrnet C. Vuran, Shantidey Mohanty, "NeXt Generation/dynamic Spectrum Access/Cognitive Radio Wireless Networks:A Survey," Computer Networks, vol.50, no.13, pp.2127-2159, Sep 2006.
[28]Q. Zhao and Sadler, B.M., "A Survey of Dynamic Spectrum Access," IEEE Signal Processing Mag., vol.24, no.3, pp.79-89, May 2007.
[29]Mahmoud H., Yucek T. and Arslan H., "OFDM for Cognitive Radio:Merits and Challenges," IEEE Trans. Wireless Commun., vol.16, no.2, pp.6-14, Apr 2009.
[30]Mohanram, C. and Bhashyam, S., "A Sub-optimal Joint Subcarrier and Power Allocation Algorithm for Multiuser OFDM," IEEE Commun. Lett., vol.9, no.8, pp.685-687, Aug 2005.
[31]Z. Shen, Andrews J. G. and Evans B.L., "Adaptive Resource Allocation in Multiuser OFDM Systems with Proportional Rate Constraints," IEEE Trans. Wireless Commun., vol.4, no.6, pp.2726-2737, Nov 2005.
[32]T. Alpcan and T. Basar, "A Game-theoretic Framework for Congestion Control in General Topology Networks," in Proc. IEEE Conference on Decision Control, vol.2, pp.1218-1224, Dec 2002.
[33]A. B. MacKenzie and S. B. Wicker, "Game Theory in Communications: Motivation, Explanation, and Application to Power Control," in Proc. of IEEE Globecom, vol.2, pp.821-826, Nov 2001.
[34]A. Orda, R. Rom and N. Shimkin, "Competitive Routing in Multiuser Communication Networks," IEEE/ACM Trans. Netw, vol.1, no.5, pp.510-521, Oct 1993.
[35]E. Altman, T. Boulogne and etal, "A Survey on Networking Games in Telecommunications," Computers and Operations Research, vol.33, no.2, pp. 286-311,Feb 2006.
[36]P. D. Straffin, Game Theory and Strategy, The Mathematical Association of America:Washington DC,1993.
[37]K. Vijay, Auction Theory. Academic Press,2002.
[38]Inhyoung Kim, In-Soon Park and Lee Y.H., "Use of Linear Programming for Dynamic Subcarrier and Bit Allocation in Multiuser OFDM," IEEE Trans. Veh. Technol., vol.55, no.4, pp.1195-1207, Jul 2006.
[39]Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge, Cambridge University Press, UK,2004.
[40]Jiho Jang and Kwang Bok Lee, "Transmit Power Adaptation for Multiuser OFDM Systems," IEEE J. Sel. Areas Commun., vol.21, no.2, pp.171-178, Feb 2003.
[41]Theodore S. Rappaport, Wireless Communications:Principles & Practice, Prentice Hall Press,2002.
[42]IEEE 802.16-2001, "IEEE Standard for Local and Metropolitan Area Networks Part 16:Air Interface for Fixed Broadband Wireless Access Systems," Apr.8, 2002.
[43]Tien-Dzung Nguyen and Youngnam Han, "A Proportional Fairness Algorithm with QoS Provision in Downlink OFDMA Systems," IEEE Commun. Lett., vol. 10, no.11, pp.760-762, Nov 2005.
[44]L. Zeng, Y. Hu, G. Xie, Y. Zhao and etal, "Proportional Fair Resource Allocation for Uplink OFDMA Network Using Priority-Ranked Bargaining Model," IEICE Trans. Commun., vol. E92-B, no.8, pp.2638-2648, Aug 2009.
[45]H. Kim and Y. Han, "A Proportional Fair Scheduling for Multicarrier Transmission Systems," IEEE Commun. Lett., no.9, vol.3, pp.210-212, Mar 2005.
[46]T. M. Cover and J. A. Thomas, Elements of information theory, New York:John Wiley & Sons,1991.
[47]R. Jain and etal. "A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems," Technical Report TR-301, DEC Research Report,1984.
[48]J. Huang, H. Zhu, Mu. Chiang and H. V Poor, "Auction-Based Resource Allocation for Cooperative Communications," IEEE J. Sel. Areas Commun., vol. 26, no.7, pp.1226-1237, Sep 2008.
[49]G. Owen, Game Theory,3rd Ed. New York:Academic,2001.
[50]Zhu. J and Liu K. J. R, "Dynamic Spectrum Sharing:A Game Theoretical Overview," IEEE Commun. Mag., vol.45, no.5, pp.88-94, May 2007.
[51]X. Feng and Z. Zhang, "The Rank of a Random Matrix," Applied Mathematics and Computation, vol.185, no.11, pp.689-694,2007.
[52]Zhao Yi, Zeng Lingkang, Xie Gang and etal, "Fairness Based Resource Allocation for Uplink OFDMA Systems," Journal of China Universities of Posts and Telecommunications, vol.15, no.2, pp.50-55, Feb 2008.
[53]S. Dey and J. Evans, "Optimal Power Control over Multiple Time-scale Fading Channels with Service Outage Constraints," IEEE Trans. Commun., vol.53, no.4, pp.708-717, Apr 2005.
[54]W. Yu, W. Rhee, S. Boyd, and J.M. Cioffi, "Iterative Water-filling for Gaussian Vector Multiple Access Channels", IEEE Trans. Info. Theory, vol.50, no.1, pp. 145-151,Jan 2004.
[55]Y. D. Yates, "A Framework for Uplink Power Control in Cellular Radio. Systems", IEEE J. Sel. Areas Commun., vol.13, no.7, pp.1341-1347, Sep 1995.
[56]Jens Zander, "Performance of Optimum Transmitter Power Control in Cellular Radio Systems," IEEE Trans. Veh. Technol., vol.41, no.1, pp.57-62, Feb 2006.
[57]H. J. Su and E. Geraniotis, "A Distributed Power Allocation Algorithm with Adaptive Modulation for Multi-cell OFDM Sytems," in 5th IEEE International Symposium on Spread Spectrum Techniques and Applications, vol.2, pp. 474-478, Sun City, South Africa, Sep 1998.
[58]Saraydar, C.U., Mandayam, N.B. and Goodman, D.J., "Pricing and Power Control in A Multicell Wireless Data Network", IEEE J. Sel. Areas Commun., vol. 19, no.10, pp.1883-1892, Oct 2001.
[59]D. M. Topkis, Supermodularity and Complementarity, Princeton, NJ:Princeton Univ. Press,1998.
[60]T. Clancy, "Formalizing the Interference Temperature Model," Wiley Wireless Communications and Mobile Computing, vol.7, no.9, pp.1077-1086, Nov 2007.
[61]Yiping Xing, Chetan N. Mathur and etal, "Dynamic Spectrum Access with QoS and Interference Temperature Constraints," IEEE Trans. Mobile Computing., vol. 6, no.4, pp.423-433, Apr 2007.
[62]Jia, J. and Zhang, Q., "A Non-Cooperative Power Control Game for Secondary Spectrum Sharing," in Proc. of IEEE ICC, pp.5933-5938, Jun 2007.
[63]Mitran. P, L. Long and etal, "Resource Allocation for Downlink Spectrum Sharing in Cognitive Radio Networks," in Proc. of IEEE VTC, pp.1-5, Sep 2008.
[64]Y. Zhang and C. Leung, "Resource Allocation in an OFDM-Based Cognitive Radio System," IEEE Trans. Commun., vol.57, no.7, pp.1928-1931, Jul 2007.
[65]G. Bansal, M.J. Hossain and V.K. Bhargava, "Optimal and Suboptimal Power Allocation Schemes for OFDM-based Cognitive Radio Systems," IEEE Trans. Wireless. Commun., vol.7, no.11, pp.4710-4718, Nov 2008.
[66]P. Cheng, Z. Zhang, H.-H. Chen and P. Qiu, "Optimal Distributed Joint Frequency, Rate and Power Allocation in Cognitive OFDMA Systems," IET Communications., vol.2, no.6, pp.815-826, Jul 2008.
[67]Batra, A., Lingam, S. and Balakrishnan, J., "Multi-band OFDM:A Cognitive Radio for UWB," in Proc. of IEEE ISCAS, pp.4094-4097, May 2006.
[68]Moy, C., Bisiaux, A., and Paquelet, S., "An Ultra-Wide Band Umbilical Cord for Cognitive Radio Systems," in Proc. of IEEE PIMRC, vol.2, pp.775-779, Sep 2005.
[69]H. Zhang, X. Zhou, K.Y. Yazdandoost and I. Chlamtac, "Multiple Signal Waveforms Adaptation in Cognitive Ultra-Wideband Radio Evolution," IEEE J. Sel. Areas Commun., vol.24, no.4, pp.878-884, Apr 2006.
[70]T. Weiss, and F. K. Jondral, "Spectrum Pooling:An Innovative Strategy for the Enhancement of Spectrum Efficiency," IEEE Commun. Mag., vol.43, no.3, pp. S8-S14, Mar 2004.
[71]T. Weiss, J. Hillenbrand, A. Krohn, and F. K. Jondral, "Mutual Interference in OFDM-based Spectrum Pooling Systems," in Proc. of IEEE VTC, vol.4, pp. 1873-1877, May 2004.
[72]E. Sofer, R. Khalona, W. Hu, I. Kitroser, and etal, P. Piggin. Wireless RANs, OFDMA Single Channel Parameters, IEEE 802.22-06/0092r0, available online at: http://ieee802.org/22/, Jun 2006.
[73]Chandrasekhar, V., Andrews, J., and Gatherer, A., "Femtocell Networks:A Survey," IEEE Commun. Mag., vol.46, no.9, pp.59-67, Sep 2008.
[74]D. Lopez-Perez, A. Valcarce, G. de la Roche and J. Zhang, "OFDMA Femtocells:A Roadmap on Interference Avoidance," IEEE Commun. Mag., vol. 47, no.9, pp.59-48, Sep 2009.
[75]D. Fugenberg and J. Tirole, Game Theory, MIT Press, Cambridge, MA,1991.
[76]J. B. Rosen., "Existence and Uniqueness of Equilibrium Points for Concave N-Person Games," Econometrica, vol.33, no.3, pp.520-534, Jul 1965.
[77]L. A. Wolsey, Integer Programming. New York:Wiley,1998.
[78]Carl D. Meyer, Matrix Analysis and Applied Linear Algebra, Society for Industrial and Applied Mathematics, Feb 2001.
[2]Mitola, J., Ⅲ and Maguire, G.Q., Jr., "Cognitive Radio:Making Software Radios more Personal," IEEE Personal Commun., vol.6, no.4, pp.13-18, Aug.1999.
[3]Mitola J., "Cognitive Radio:An Integrated Agent Architecture for Software Defined Radio," Dissertation, Doctor of Technology, Royal Inst Technol (KTH), Stockholm, Sweden,2000.
[4]Simon Haykin, "Cognitive Radio:Brain-Empowered Wireless Communications," IEEE J. Sel. Areas Commun., vol.23, no.2, pp.201-220, Feb 2005.
[5]佟学俭,罗涛,OFDM移动通信技术原理与应用,人民邮电出版社,2003年6月.
[6]C. Y. Wong, R. S. Cheng, K. B. Letaief, and R. D. Murch, "Multiuser OFDM with Adaptive Subcarrier, Bit, and Power Allocation," IEEE J. Sel. Areas Commun., vol.17, no.10, pp.1747-1758, Oct 1999.
[7]K. Kim, Y. Han, and S. L. Kim, "Joint Subcarrier and Power Allocation in Uplink OFDMA Systems," IEEE Commun. Lett., vol.9, no.6, pp.526-528, Jun 2005.
[8]W. Yu, G. Ginis, and J. M. Cioffi, "Distributed Multiuser Power Control for Digital Subscriber Lines," IEEE J. Sel. Areas Commun., vol.20, no.5, pp. 1105-1115, Jun 2002.
[9]W. Yu and J. M. Cioffi, "FDMA Capacity of Gaussian Multiple-access Channels with ISI," IEEE Trans. Commun., vol.50, no.1, pp.102-111, Jan 2002.
[10]H. Liu and G. Li, OFDM-Based Broadband Wireless Networks, John Wiley & Sons, Inc., Hoboken, New Jersey,2005.
[11]Zhu. H, Zhu. J and Liu K.J.R, "Fair Multiuser Channel Allocation for OFDMA Networks Using Nash Bargaining Solutions and Coalitions," IEEE Trans. Commun., vol.53, no.8, pp.1366-1376, Aug 2005.
[12]W. Rhee and J. M. Cioffi, "Increase in Capacity of Multiuser OFDM System Using Dynamic Subchannel Allocation," in Proc. of IEEE VTC, pp.1085-1089, May 2000.
[13]F. P. Kelly, A. K. Maulloo, D. Tan. "Rate Control for Communication Networks Shadow Prices, Proportional Fairness and Stability," The Journal of the Operational Research Society, vol.49, no.3, pp.237-252,1998.
[14]Yaiche. H., Mazumdar. R.R., and Rosenberg. C, "A Game Theoretic Framework for Bandwidth Allocation and Pricing in Broadband Networks," IEEE/ACM Trans. Netw., vol.8, no.5, pp.667-678, Oct 2000.
[15]G. Caire, G. Taricco and E. Biglieri., "Optimum Power Control over Fading Channels," IEEE Trans. Info. Theory, vol.45, no.5, pp.1468-1489, Jul 1999.
[16]J. Papandriopoulos, J. Evans, and S. Dey, "Optimal Power Control for Rayleigh-Faded Multiuser Systems With Outage Constraints," IEEE Trans. Wireless Commun., vol.4, no.6, pp.2705-2715, Nov 2005.
[17]S. Dey. and J. Evans., "Outage Capacity and Optimal Power Allocation for Multiple Time-Scale Parallel Fading Channels," IEEE Trans. Wireless Commun., vol.6, no.7, pp.2369-2373, Jul 2007.
[18]K. K Wong and J. Chen., "Near-Optimal Power Allocation and Multiuser Scheduling with Outage Capacity Constraints Exploiting Only Channel Statistics," IEEE Trans. Wireless Commun. vol.7, no.3, pp.812-818, Mar 2008.
[19]L. Zeng, Y. Liu and etal, "Optimal Power and Subcarrier Allocation with Partial Side Information for Downlink OFDMA System," accepted by Journal of China Universities of Posts and Telecommunications, to be published.
[20]Drew Fudenberg and Jean Tirole, Game theory, The MIT Press:London, England,1998.
[21]Robert J. Barro and Xavier Sala-i-Martin, Economic Growth, MIT Press: London, England,1999.
[22]S. Kandukuri and S. Boyd, "Optimal Power Control in Interference-Limited Fading Wireless Channels with Outage-Probability Specifications," IEEE Trans. Wireless Commun., vol.1, no.1, pp.46-55, Jan 2002.
[23]G. Li and H. Liu, "Downlink Dynamic Resource Allocation for Multi-cell OFDMA System", in Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol.1, pp.9-12, Nov 2003.
[24]Hojoong Kwon and Byeong Gi Lee, "Distributed Resource Allocation through Noncooperative Game Approach in Multi-cell OFDMA Systems," in Proc. of IEEE ICC, pp.4345-4350, Jun 2006.
[25]Zhu Han, Zhu Ji, and Liu K.J.R., "Non-Cooperative Resource Competition Game by Virtual Referee in Multi-Cell OFDMA Networks," IEEE J. Sel. Areas Commun., vol.25, no.6, pp.1079-1090, Aug 2007.
[26]J. Huang, Berry R.A. and Honig M.L., "Distributed Interference Compensation for Wireless Networks," IEEE J. Sel. Areas Commun., vol.24, no.5, pp. 1074-1084, May 2006.
[27]Ian F. Akyildiz, Won-Yeol Lee, Mehrnet C. Vuran, Shantidey Mohanty, "NeXt Generation/dynamic Spectrum Access/Cognitive Radio Wireless Networks:A Survey," Computer Networks, vol.50, no.13, pp.2127-2159, Sep 2006.
[28]Q. Zhao and Sadler, B.M., "A Survey of Dynamic Spectrum Access," IEEE Signal Processing Mag., vol.24, no.3, pp.79-89, May 2007.
[29]Mahmoud H., Yucek T. and Arslan H., "OFDM for Cognitive Radio:Merits and Challenges," IEEE Trans. Wireless Commun., vol.16, no.2, pp.6-14, Apr 2009.
[30]Mohanram, C. and Bhashyam, S., "A Sub-optimal Joint Subcarrier and Power Allocation Algorithm for Multiuser OFDM," IEEE Commun. Lett., vol.9, no.8, pp.685-687, Aug 2005.
[31]Z. Shen, Andrews J. G. and Evans B.L., "Adaptive Resource Allocation in Multiuser OFDM Systems with Proportional Rate Constraints," IEEE Trans. Wireless Commun., vol.4, no.6, pp.2726-2737, Nov 2005.
[32]T. Alpcan and T. Basar, "A Game-theoretic Framework for Congestion Control in General Topology Networks," in Proc. IEEE Conference on Decision Control, vol.2, pp.1218-1224, Dec 2002.
[33]A. B. MacKenzie and S. B. Wicker, "Game Theory in Communications: Motivation, Explanation, and Application to Power Control," in Proc. of IEEE Globecom, vol.2, pp.821-826, Nov 2001.
[34]A. Orda, R. Rom and N. Shimkin, "Competitive Routing in Multiuser Communication Networks," IEEE/ACM Trans. Netw, vol.1, no.5, pp.510-521, Oct 1993.
[35]E. Altman, T. Boulogne and etal, "A Survey on Networking Games in Telecommunications," Computers and Operations Research, vol.33, no.2, pp. 286-311,Feb 2006.
[36]P. D. Straffin, Game Theory and Strategy, The Mathematical Association of America:Washington DC,1993.
[37]K. Vijay, Auction Theory. Academic Press,2002.
[38]Inhyoung Kim, In-Soon Park and Lee Y.H., "Use of Linear Programming for Dynamic Subcarrier and Bit Allocation in Multiuser OFDM," IEEE Trans. Veh. Technol., vol.55, no.4, pp.1195-1207, Jul 2006.
[39]Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Cambridge, Cambridge University Press, UK,2004.
[40]Jiho Jang and Kwang Bok Lee, "Transmit Power Adaptation for Multiuser OFDM Systems," IEEE J. Sel. Areas Commun., vol.21, no.2, pp.171-178, Feb 2003.
[41]Theodore S. Rappaport, Wireless Communications:Principles & Practice, Prentice Hall Press,2002.
[42]IEEE 802.16-2001, "IEEE Standard for Local and Metropolitan Area Networks Part 16:Air Interface for Fixed Broadband Wireless Access Systems," Apr.8, 2002.
[43]Tien-Dzung Nguyen and Youngnam Han, "A Proportional Fairness Algorithm with QoS Provision in Downlink OFDMA Systems," IEEE Commun. Lett., vol. 10, no.11, pp.760-762, Nov 2005.
[44]L. Zeng, Y. Hu, G. Xie, Y. Zhao and etal, "Proportional Fair Resource Allocation for Uplink OFDMA Network Using Priority-Ranked Bargaining Model," IEICE Trans. Commun., vol. E92-B, no.8, pp.2638-2648, Aug 2009.
[45]H. Kim and Y. Han, "A Proportional Fair Scheduling for Multicarrier Transmission Systems," IEEE Commun. Lett., no.9, vol.3, pp.210-212, Mar 2005.
[46]T. M. Cover and J. A. Thomas, Elements of information theory, New York:John Wiley & Sons,1991.
[47]R. Jain and etal. "A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems," Technical Report TR-301, DEC Research Report,1984.
[48]J. Huang, H. Zhu, Mu. Chiang and H. V Poor, "Auction-Based Resource Allocation for Cooperative Communications," IEEE J. Sel. Areas Commun., vol. 26, no.7, pp.1226-1237, Sep 2008.
[49]G. Owen, Game Theory,3rd Ed. New York:Academic,2001.
[50]Zhu. J and Liu K. J. R, "Dynamic Spectrum Sharing:A Game Theoretical Overview," IEEE Commun. Mag., vol.45, no.5, pp.88-94, May 2007.
[51]X. Feng and Z. Zhang, "The Rank of a Random Matrix," Applied Mathematics and Computation, vol.185, no.11, pp.689-694,2007.
[52]Zhao Yi, Zeng Lingkang, Xie Gang and etal, "Fairness Based Resource Allocation for Uplink OFDMA Systems," Journal of China Universities of Posts and Telecommunications, vol.15, no.2, pp.50-55, Feb 2008.
[53]S. Dey and J. Evans, "Optimal Power Control over Multiple Time-scale Fading Channels with Service Outage Constraints," IEEE Trans. Commun., vol.53, no.4, pp.708-717, Apr 2005.
[54]W. Yu, W. Rhee, S. Boyd, and J.M. Cioffi, "Iterative Water-filling for Gaussian Vector Multiple Access Channels", IEEE Trans. Info. Theory, vol.50, no.1, pp. 145-151,Jan 2004.
[55]Y. D. Yates, "A Framework for Uplink Power Control in Cellular Radio. Systems", IEEE J. Sel. Areas Commun., vol.13, no.7, pp.1341-1347, Sep 1995.
[56]Jens Zander, "Performance of Optimum Transmitter Power Control in Cellular Radio Systems," IEEE Trans. Veh. Technol., vol.41, no.1, pp.57-62, Feb 2006.
[57]H. J. Su and E. Geraniotis, "A Distributed Power Allocation Algorithm with Adaptive Modulation for Multi-cell OFDM Sytems," in 5th IEEE International Symposium on Spread Spectrum Techniques and Applications, vol.2, pp. 474-478, Sun City, South Africa, Sep 1998.
[58]Saraydar, C.U., Mandayam, N.B. and Goodman, D.J., "Pricing and Power Control in A Multicell Wireless Data Network", IEEE J. Sel. Areas Commun., vol. 19, no.10, pp.1883-1892, Oct 2001.
[59]D. M. Topkis, Supermodularity and Complementarity, Princeton, NJ:Princeton Univ. Press,1998.
[60]T. Clancy, "Formalizing the Interference Temperature Model," Wiley Wireless Communications and Mobile Computing, vol.7, no.9, pp.1077-1086, Nov 2007.
[61]Yiping Xing, Chetan N. Mathur and etal, "Dynamic Spectrum Access with QoS and Interference Temperature Constraints," IEEE Trans. Mobile Computing., vol. 6, no.4, pp.423-433, Apr 2007.
[62]Jia, J. and Zhang, Q., "A Non-Cooperative Power Control Game for Secondary Spectrum Sharing," in Proc. of IEEE ICC, pp.5933-5938, Jun 2007.
[63]Mitran. P, L. Long and etal, "Resource Allocation for Downlink Spectrum Sharing in Cognitive Radio Networks," in Proc. of IEEE VTC, pp.1-5, Sep 2008.
[64]Y. Zhang and C. Leung, "Resource Allocation in an OFDM-Based Cognitive Radio System," IEEE Trans. Commun., vol.57, no.7, pp.1928-1931, Jul 2007.
[65]G. Bansal, M.J. Hossain and V.K. Bhargava, "Optimal and Suboptimal Power Allocation Schemes for OFDM-based Cognitive Radio Systems," IEEE Trans. Wireless. Commun., vol.7, no.11, pp.4710-4718, Nov 2008.
[66]P. Cheng, Z. Zhang, H.-H. Chen and P. Qiu, "Optimal Distributed Joint Frequency, Rate and Power Allocation in Cognitive OFDMA Systems," IET Communications., vol.2, no.6, pp.815-826, Jul 2008.
[67]Batra, A., Lingam, S. and Balakrishnan, J., "Multi-band OFDM:A Cognitive Radio for UWB," in Proc. of IEEE ISCAS, pp.4094-4097, May 2006.
[68]Moy, C., Bisiaux, A., and Paquelet, S., "An Ultra-Wide Band Umbilical Cord for Cognitive Radio Systems," in Proc. of IEEE PIMRC, vol.2, pp.775-779, Sep 2005.
[69]H. Zhang, X. Zhou, K.Y. Yazdandoost and I. Chlamtac, "Multiple Signal Waveforms Adaptation in Cognitive Ultra-Wideband Radio Evolution," IEEE J. Sel. Areas Commun., vol.24, no.4, pp.878-884, Apr 2006.
[70]T. Weiss, and F. K. Jondral, "Spectrum Pooling:An Innovative Strategy for the Enhancement of Spectrum Efficiency," IEEE Commun. Mag., vol.43, no.3, pp. S8-S14, Mar 2004.
[71]T. Weiss, J. Hillenbrand, A. Krohn, and F. K. Jondral, "Mutual Interference in OFDM-based Spectrum Pooling Systems," in Proc. of IEEE VTC, vol.4, pp. 1873-1877, May 2004.
[72]E. Sofer, R. Khalona, W. Hu, I. Kitroser, and etal, P. Piggin. Wireless RANs, OFDMA Single Channel Parameters, IEEE 802.22-06/0092r0, available online at: http://ieee802.org/22/, Jun 2006.
[73]Chandrasekhar, V., Andrews, J., and Gatherer, A., "Femtocell Networks:A Survey," IEEE Commun. Mag., vol.46, no.9, pp.59-67, Sep 2008.
[74]D. Lopez-Perez, A. Valcarce, G. de la Roche and J. Zhang, "OFDMA Femtocells:A Roadmap on Interference Avoidance," IEEE Commun. Mag., vol. 47, no.9, pp.59-48, Sep 2009.
[75]D. Fugenberg and J. Tirole, Game Theory, MIT Press, Cambridge, MA,1991.
[76]J. B. Rosen., "Existence and Uniqueness of Equilibrium Points for Concave N-Person Games," Econometrica, vol.33, no.3, pp.520-534, Jul 1965.
[77]L. A. Wolsey, Integer Programming. New York:Wiley,1998.
[78]Carl D. Meyer, Matrix Analysis and Applied Linear Algebra, Society for Industrial and Applied Mathematics, Feb 2001.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |