宽带无线通信系统资源分配策略研究
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摘要
OFDM已经成为未来宽带无线通信系统的关键物理层技术,在B3G系统(如LTE、WiMAX)中得到广泛应用。虽然作为一种物理层多载波调制技术,OFDM的研究相对成熟,但作为一种多址接入方式,链路层及网络层的多用户OFDM资源分配还需要更加深入的探讨。
相对于单用户OFDM系统,多用户OFDM系统能够获得多重分集增益,如频率分集、时间分集、多用户分集、空间分集(配置多天线)等。但是,实际分集增益的大小主要取决于资源分配策略的选择。因此,灵活而实用的资源分配策略是未来宽带无线通信系统提高系统频谱效率、保证用户QoS性能的关键。
本论文的研究围绕多用户OFDM系统的资源分配而展开。众所周知,移动通信系统中的很多关键技术均是为了解决三重动态性:信道动态性、用户动态性、业务动态性。本论文的研究也与它们相关,第2章讨论小尺度多径衰落信道动态性下的资源分配方案;第3章则研究信道量化对小尺度多径衰落信道动态性的影响,继而对系统和速率容量的影响,并为系统选择合适的信道量化方案;第4章将信道动态性扩展到大尺度传播损耗、阴影衰落及小尺度多径衰落的联合效果,分析单小区内如何进行有效的OFDM系统资源分配;第5章在前章的基础上引入分布式多天线技术,大大缩小了信道动态性,改善了信道条件,提高了OFDM系统资源分配的性能;第6章则在前章的基础上再引入用户及业务的动态性,考虑在该场景下的OFDM资源分配方案制定问题。
本论文的研究对象从OFDM系统到OFDM DAS系统,考虑的因素逐渐增多,研究的问题和模型渐趋复杂。现将本文的主要工作和得到的相应结论详细叙述如下:
第2章研究了相同路径损耗下OFDM系统资源分配,研究内容分为上行和下行两个方面。对于下行资源分配,提出了迭代功率转移算法,并证明了算法的收敛性。该算法能够获得与最优功率分配算法几乎相似的容量,但具有低得多的时间复杂度,如果配合已有的分组子载波分配算法,可以形成一种低复杂度下行资源分配方案。论文还通过仿真分析了各种信道参数,如:多径数目、最大时延扩展、子载波带宽等,如何影响方案性能,为方案实施时的参数选择提供重要参考。对于上行资源分配,本章分析发现了用户容量饱和问题,并通过动态地改变待分配资源用户集避免饱和用户一直占用资源现象出现,有效提高了系统总容量。
第3章讨论部分信道信息下的资源分配问题。理想情况下,信道信息设计与资源分配应当联合优化,才能达到整体性能的最优。但现有的大部分文献都考虑部分信道信息特性已知情况下的最优资源分配问题,本章则从另一个角度研究该问题,分析最大信噪比资源分配方案下的最优信道量化设计,并通过等功率分配假设及理论推导,成功提出了一种多用户信道概率量化方案。该方案在相同上行反馈带宽的前提下,能够获得比等幅量化高得多的容量。所以,它实现了上行反馈带宽的大量减少,推进了OFDM系统的应用。
第4章探讨小区内的OFDM系统资源分配问题。现有的文献几乎毫无例外地假设每子载波具备相等功率,但小区中各用户所处的位置及周围的环境差异很大,很难保证各用户具有相等的中短期信道增益均值,必须为每用户子载波设定不同的初始功率消除信道条件差异,以改善用户间的公平性。本章引入了一个初始功率分配优化模型,并使用数值方法求解该模型,然后执行子载波分配和交换,有效改善了子载波分配阶段获得的公平性。仿真结果表明:本章子载波分配算法结合最优功率分配能够明显提高系统容量,改善系统频谱效率,是一种有效的OFDM系统小区内资源分配方案。
第5章研究OFDM DAS系统资源分配问题。通过在小区内布置分布式天线,OFDM DAS系统明显减小了信道动态性,改善了用户信道条件。本章分析得到了最优资源分配算法,并推导了单用户OFDM DAS和CAS系统的容量。仿真结果与理论推导相当吻合。仿真得到的结论有:1)OFDM DAS能显著提升系统和速率容量;2)与子载波注水功率分配相比,等功率分配为OFDM DAS系统带来的性能损失可以忽略不计;3)OFDM DAS容量与天线数目、天线位置密切相关,实际系统天线布置参数应根据用户密度灵活选择,达到性价比最大。
第6章继续研究OFDM DAS系统资源分配问题,但系统与信道模型中引入了用户及业务的动态性。本章提出了一种适合具备最小速率保证尽力而为业务的资源分配方案。该方案的算法分为两个部分,前面部分目的是满足用户的最小速率需求,后面部分则主要为了提高系统容量。本章方案每帧执行一次,可以获得时间、频率、空间、用户四维分集增益,有效满足了用户的业务需求,且支持业务特性方面比基于时隙的分配方案更优。仿真结果同样显示了OFDM DAS系统支持用户业务行为能力比CAS系统更强,可以说:分布式天线技术是未来系统一种强有力的增加容量、提升性能手段。
未来通信系统的容量瓶颈在于无线接入网络,OFDM技术的提出正是为了突破接入网的容量限制,而OFDM系统容量的实际提升主要由资源分配策略决定。所以,OFDM系统资源分配策略的选取对于未来通信系统提供高速业务非常重要。本论文的研究正是在如此背景下而开展,相关成果在IEEE会议及国内外期刊发表,并有部分成果已经申请了国家专利。
OFDM (Orthogonal Frequency Division Multiplexing) has become one key physical-layer technology in the future BWCS (broadband wireless communication system), and has been applied in various B3G systems, such as LTE, WiMAX. Lots of research is carried out on utilizing OFDM as a physical-layer multi-carrier modulation technology, but more deep discussion on OFDM resource allocation is still required because it employs OFDM as a multiple-access manner.
Compared to the single-user OFDM system, the multi-user one is able to exploit multi-dimensional diversity gain, including frequency diversity, time diversity, multi-user diversity, space diversity (deploying multi-antenna) and so on. However, the amount of actual gain is decided by the selection for resource allocation strategies. Therefore, only flexible and practical resource allocation strategies can guarantee high frequency efficiency for system and excellent QoS (Quality of Service) performance for users in the future BWCS.
This dissertation focuses on multi-user OFDM resource allocation. It is well known that a lot of key technologies in mobile communication system are proposed to combat threefold dynamics: channel dynamics, user dynamics and traffic dynamics. The research of this dissertation is also related to them. Chapter 2 discusses resource allocation schemes in case of small-scale multi-path channel dynamics; Chapter 3 investigates how channel quantization affects small-scale muti-path channel dynamics, and hence degrades system capacity. Then, this chapter chooses an appropriate channel quantization scheme to maximize the total system capacity; Chapter 4 extends channel dynamics to the joint effect of large-scale propagation loss, shadow fading as well as small-scale multi-path fading, and analyzes how to effectively allocate OFDM resource in a single cell. Chapter 5 introduces distributed multi-antenna into the cell, which decreases channel dynamics, improves channel condition, and thus enhances the resource allocation performance in OFDM system. Chapter 6 also designs resource allocation schemes in OFDM DAS (distributed antenna system) while taking both user and traffic dynamics into consideration.
The contents of this dissertation range from resource allocation in OFDM system to that in OFDM DAS. With the deployment of distributed multi-antenna, system model becomes more complicated. The major work and conclusions of this dissertation can be detailedly described as follows:
Chapter 2 studies uplink/downlink resource allocation in the special OFDM system, where different users have equal path loss. For downlink, a power transfer algorithm is proposed, and the convergence is proved. The proposed algorithm can achieve almost the same capacity as optimal power allocation, but holds a lower computational complexity. A low-complexity downlink resource allocation scheme can be formed by combining grouped subcarrier assignment and the proposed algorithm. This chapter also simulates the effect of different channel parameters, such as the number of multi-path, maximum delay spread, subcarrier bandwidth, on the scheme performance. This is capable of guiding parameter selection when realizing the scheme in the actual system. For uplink, this chapter finds the user capacity-saturation problem, and avoids that saturated users always take up resource by dynamically changing the set of users who are waiting for resource allocation. It helps to raise the total system capacity.
Chapter 3 discusses OFDM resource allocation problem in condition of partial CSI (channel state information). Ideally, CSI design and resource allocation should be optimized jointly to reach the optimum of the system performance. Nevertheless, the majority of existing papers consider resource allocation optimization problem with partial CSI. This chapter attempts to investigate the problem from another point of view, and tries to address channel quantization design problem while selecting maximum SNR (signal-to-noise ratio) as resource allocation scheme. A multi-user channel probability quantization scheme is proposed by equal power assumption and theoretical derivation. The proposed scheme can obtain much higher capacity than equal amplitude quantization if provided the same uplink feedback bandwidth. Thus, this scheme succeeds in reducing feedback bandwidth, and boosts the application of OFDM system.
Chapter 4 investigates resource allocation problem in a cell of OFDM system. Even though the existing papers all assume equal power allocation for each subcarrier on the overall bandwidth, it is difficult to ensure that different users hold the same short/middle-term average of channel gain since wireless environment and location vary from users to users. Different initial power settings for different users are needed to eliminate channel condition difference and improve inter-user fairness. This chapter introduces an initial-power optimization model, solves the model by numerical method, and then implements subcarrier assignment and swapping based on obtained initial-power allocation. These steps construct the proposed subcarrier algorithm, which significantly betters fairness among users. Simulation results show that the proposed algorithm combined with optimal power allocation greatly enlarges system capacity, increases frequency efficiency, and is a cost-efficient resource allocation scheme in a cell of OFDM system.
Chapter 5 studies resource allocation in OFDM DAS. By deploying distributed antennas in the cell, OFDM DAS remarkably lowers channel dynamics, and upgrades users' channel condition. This chapter achieves optimal resource allocation algorithm, and derives the capacity of the single-user OFDM DAS and OFDM CAS. Simulated results maintain consistent with theoretical derivation. The following conclusions can be drawn from simulation results: 1) OFDM DAS considerably increases system rate-sum capacity; 2) Compared to water-filling, equal power allocation results in negligible capacity loss for OFDM DAS; 3) the capacity of OFDM DAS has close relations with the number of antennas and the site of antennas. How antennas are deployed in actual system should adapt to user density distribution in order to attain a cost-effective scheme.
Chapter 6 further investigates resource allocation in OFDM DAS with introducing both user and traffic dynamics to system and channel model. This chapter proposes a resource allocation scheme which is suitable for min-rate guaranteed best-effort services. The scheme consists of two parts: the former aims to satisfy users' min-rate requirements, whereas the objective of the latter is to increase system capacity. The scheme proposed by this chapter needs implementing once every frame, and can achieve time, frequency, space, user diversity gain. It effectively fulfills users' service demands, and surpasses timeslot-based resource allocation in terms of supporting users' traffic dynamics. Simulation results also show that OFDM DAS outperforms OFDM CAS when adapting to traffic characteristics. It could conclude that distributed antenna is a powerful technology to enhance the performace in the future communication system.
Capacity bottleneck for the future BWCS lies in wireless AN (access network), and OFDM technology is ready for breaking capacity restriction for AN. However, actual capacity rise in OFDM system is mainly determined by resource allocation strategies. That is the reason why resource allocation strategies selection for OFDM system is of great importance for providing high-rate services in the future BWCS. Work of this dissertation starts in the aforementioned context. Related research productions have published in IEEE conferences or international/domestic journals, and some of them have applied for patents.
相对于单用户OFDM系统,多用户OFDM系统能够获得多重分集增益,如频率分集、时间分集、多用户分集、空间分集(配置多天线)等。但是,实际分集增益的大小主要取决于资源分配策略的选择。因此,灵活而实用的资源分配策略是未来宽带无线通信系统提高系统频谱效率、保证用户QoS性能的关键。
本论文的研究围绕多用户OFDM系统的资源分配而展开。众所周知,移动通信系统中的很多关键技术均是为了解决三重动态性:信道动态性、用户动态性、业务动态性。本论文的研究也与它们相关,第2章讨论小尺度多径衰落信道动态性下的资源分配方案;第3章则研究信道量化对小尺度多径衰落信道动态性的影响,继而对系统和速率容量的影响,并为系统选择合适的信道量化方案;第4章将信道动态性扩展到大尺度传播损耗、阴影衰落及小尺度多径衰落的联合效果,分析单小区内如何进行有效的OFDM系统资源分配;第5章在前章的基础上引入分布式多天线技术,大大缩小了信道动态性,改善了信道条件,提高了OFDM系统资源分配的性能;第6章则在前章的基础上再引入用户及业务的动态性,考虑在该场景下的OFDM资源分配方案制定问题。
本论文的研究对象从OFDM系统到OFDM DAS系统,考虑的因素逐渐增多,研究的问题和模型渐趋复杂。现将本文的主要工作和得到的相应结论详细叙述如下:
第2章研究了相同路径损耗下OFDM系统资源分配,研究内容分为上行和下行两个方面。对于下行资源分配,提出了迭代功率转移算法,并证明了算法的收敛性。该算法能够获得与最优功率分配算法几乎相似的容量,但具有低得多的时间复杂度,如果配合已有的分组子载波分配算法,可以形成一种低复杂度下行资源分配方案。论文还通过仿真分析了各种信道参数,如:多径数目、最大时延扩展、子载波带宽等,如何影响方案性能,为方案实施时的参数选择提供重要参考。对于上行资源分配,本章分析发现了用户容量饱和问题,并通过动态地改变待分配资源用户集避免饱和用户一直占用资源现象出现,有效提高了系统总容量。
第3章讨论部分信道信息下的资源分配问题。理想情况下,信道信息设计与资源分配应当联合优化,才能达到整体性能的最优。但现有的大部分文献都考虑部分信道信息特性已知情况下的最优资源分配问题,本章则从另一个角度研究该问题,分析最大信噪比资源分配方案下的最优信道量化设计,并通过等功率分配假设及理论推导,成功提出了一种多用户信道概率量化方案。该方案在相同上行反馈带宽的前提下,能够获得比等幅量化高得多的容量。所以,它实现了上行反馈带宽的大量减少,推进了OFDM系统的应用。
第4章探讨小区内的OFDM系统资源分配问题。现有的文献几乎毫无例外地假设每子载波具备相等功率,但小区中各用户所处的位置及周围的环境差异很大,很难保证各用户具有相等的中短期信道增益均值,必须为每用户子载波设定不同的初始功率消除信道条件差异,以改善用户间的公平性。本章引入了一个初始功率分配优化模型,并使用数值方法求解该模型,然后执行子载波分配和交换,有效改善了子载波分配阶段获得的公平性。仿真结果表明:本章子载波分配算法结合最优功率分配能够明显提高系统容量,改善系统频谱效率,是一种有效的OFDM系统小区内资源分配方案。
第5章研究OFDM DAS系统资源分配问题。通过在小区内布置分布式天线,OFDM DAS系统明显减小了信道动态性,改善了用户信道条件。本章分析得到了最优资源分配算法,并推导了单用户OFDM DAS和CAS系统的容量。仿真结果与理论推导相当吻合。仿真得到的结论有:1)OFDM DAS能显著提升系统和速率容量;2)与子载波注水功率分配相比,等功率分配为OFDM DAS系统带来的性能损失可以忽略不计;3)OFDM DAS容量与天线数目、天线位置密切相关,实际系统天线布置参数应根据用户密度灵活选择,达到性价比最大。
第6章继续研究OFDM DAS系统资源分配问题,但系统与信道模型中引入了用户及业务的动态性。本章提出了一种适合具备最小速率保证尽力而为业务的资源分配方案。该方案的算法分为两个部分,前面部分目的是满足用户的最小速率需求,后面部分则主要为了提高系统容量。本章方案每帧执行一次,可以获得时间、频率、空间、用户四维分集增益,有效满足了用户的业务需求,且支持业务特性方面比基于时隙的分配方案更优。仿真结果同样显示了OFDM DAS系统支持用户业务行为能力比CAS系统更强,可以说:分布式天线技术是未来系统一种强有力的增加容量、提升性能手段。
未来通信系统的容量瓶颈在于无线接入网络,OFDM技术的提出正是为了突破接入网的容量限制,而OFDM系统容量的实际提升主要由资源分配策略决定。所以,OFDM系统资源分配策略的选取对于未来通信系统提供高速业务非常重要。本论文的研究正是在如此背景下而开展,相关成果在IEEE会议及国内外期刊发表,并有部分成果已经申请了国家专利。
OFDM (Orthogonal Frequency Division Multiplexing) has become one key physical-layer technology in the future BWCS (broadband wireless communication system), and has been applied in various B3G systems, such as LTE, WiMAX. Lots of research is carried out on utilizing OFDM as a physical-layer multi-carrier modulation technology, but more deep discussion on OFDM resource allocation is still required because it employs OFDM as a multiple-access manner.
Compared to the single-user OFDM system, the multi-user one is able to exploit multi-dimensional diversity gain, including frequency diversity, time diversity, multi-user diversity, space diversity (deploying multi-antenna) and so on. However, the amount of actual gain is decided by the selection for resource allocation strategies. Therefore, only flexible and practical resource allocation strategies can guarantee high frequency efficiency for system and excellent QoS (Quality of Service) performance for users in the future BWCS.
This dissertation focuses on multi-user OFDM resource allocation. It is well known that a lot of key technologies in mobile communication system are proposed to combat threefold dynamics: channel dynamics, user dynamics and traffic dynamics. The research of this dissertation is also related to them. Chapter 2 discusses resource allocation schemes in case of small-scale multi-path channel dynamics; Chapter 3 investigates how channel quantization affects small-scale muti-path channel dynamics, and hence degrades system capacity. Then, this chapter chooses an appropriate channel quantization scheme to maximize the total system capacity; Chapter 4 extends channel dynamics to the joint effect of large-scale propagation loss, shadow fading as well as small-scale multi-path fading, and analyzes how to effectively allocate OFDM resource in a single cell. Chapter 5 introduces distributed multi-antenna into the cell, which decreases channel dynamics, improves channel condition, and thus enhances the resource allocation performance in OFDM system. Chapter 6 also designs resource allocation schemes in OFDM DAS (distributed antenna system) while taking both user and traffic dynamics into consideration.
The contents of this dissertation range from resource allocation in OFDM system to that in OFDM DAS. With the deployment of distributed multi-antenna, system model becomes more complicated. The major work and conclusions of this dissertation can be detailedly described as follows:
Chapter 2 studies uplink/downlink resource allocation in the special OFDM system, where different users have equal path loss. For downlink, a power transfer algorithm is proposed, and the convergence is proved. The proposed algorithm can achieve almost the same capacity as optimal power allocation, but holds a lower computational complexity. A low-complexity downlink resource allocation scheme can be formed by combining grouped subcarrier assignment and the proposed algorithm. This chapter also simulates the effect of different channel parameters, such as the number of multi-path, maximum delay spread, subcarrier bandwidth, on the scheme performance. This is capable of guiding parameter selection when realizing the scheme in the actual system. For uplink, this chapter finds the user capacity-saturation problem, and avoids that saturated users always take up resource by dynamically changing the set of users who are waiting for resource allocation. It helps to raise the total system capacity.
Chapter 3 discusses OFDM resource allocation problem in condition of partial CSI (channel state information). Ideally, CSI design and resource allocation should be optimized jointly to reach the optimum of the system performance. Nevertheless, the majority of existing papers consider resource allocation optimization problem with partial CSI. This chapter attempts to investigate the problem from another point of view, and tries to address channel quantization design problem while selecting maximum SNR (signal-to-noise ratio) as resource allocation scheme. A multi-user channel probability quantization scheme is proposed by equal power assumption and theoretical derivation. The proposed scheme can obtain much higher capacity than equal amplitude quantization if provided the same uplink feedback bandwidth. Thus, this scheme succeeds in reducing feedback bandwidth, and boosts the application of OFDM system.
Chapter 4 investigates resource allocation problem in a cell of OFDM system. Even though the existing papers all assume equal power allocation for each subcarrier on the overall bandwidth, it is difficult to ensure that different users hold the same short/middle-term average of channel gain since wireless environment and location vary from users to users. Different initial power settings for different users are needed to eliminate channel condition difference and improve inter-user fairness. This chapter introduces an initial-power optimization model, solves the model by numerical method, and then implements subcarrier assignment and swapping based on obtained initial-power allocation. These steps construct the proposed subcarrier algorithm, which significantly betters fairness among users. Simulation results show that the proposed algorithm combined with optimal power allocation greatly enlarges system capacity, increases frequency efficiency, and is a cost-efficient resource allocation scheme in a cell of OFDM system.
Chapter 5 studies resource allocation in OFDM DAS. By deploying distributed antennas in the cell, OFDM DAS remarkably lowers channel dynamics, and upgrades users' channel condition. This chapter achieves optimal resource allocation algorithm, and derives the capacity of the single-user OFDM DAS and OFDM CAS. Simulated results maintain consistent with theoretical derivation. The following conclusions can be drawn from simulation results: 1) OFDM DAS considerably increases system rate-sum capacity; 2) Compared to water-filling, equal power allocation results in negligible capacity loss for OFDM DAS; 3) the capacity of OFDM DAS has close relations with the number of antennas and the site of antennas. How antennas are deployed in actual system should adapt to user density distribution in order to attain a cost-effective scheme.
Chapter 6 further investigates resource allocation in OFDM DAS with introducing both user and traffic dynamics to system and channel model. This chapter proposes a resource allocation scheme which is suitable for min-rate guaranteed best-effort services. The scheme consists of two parts: the former aims to satisfy users' min-rate requirements, whereas the objective of the latter is to increase system capacity. The scheme proposed by this chapter needs implementing once every frame, and can achieve time, frequency, space, user diversity gain. It effectively fulfills users' service demands, and surpasses timeslot-based resource allocation in terms of supporting users' traffic dynamics. Simulation results also show that OFDM DAS outperforms OFDM CAS when adapting to traffic characteristics. It could conclude that distributed antenna is a powerful technology to enhance the performace in the future communication system.
Capacity bottleneck for the future BWCS lies in wireless AN (access network), and OFDM technology is ready for breaking capacity restriction for AN. However, actual capacity rise in OFDM system is mainly determined by resource allocation strategies. That is the reason why resource allocation strategies selection for OFDM system is of great importance for providing high-rate services in the future BWCS. Work of this dissertation starts in the aforementioned context. Related research productions have published in IEEE conferences or international/domestic journals, and some of them have applied for patents.
引文
[1]吴伟陵,牛凯 移动通信原理 电子工业出版社 2005年1月 1-7。
[2] Letaief K.B., Zhang Ying Jun Dynamic multiuser resource allocation and adaptation for wireless systems IEEE Wireless Communications 13(4) Aug. 2006 38-47.
[3] Jang Jiho, Lee Kwang Bok Transmit power adaptation for multiuser OFDM systems IEEE Journal on Selected Areas in Communications 21(2) Feb. 2003 171-178.
[4] Wong C. Y., Cheng R. S., Letaief K. B. et al. Multicarrier OFDM with adaptive subcarrier, bit, and power allocation IEEE Journal on Selected Areas in Communications 17(10) Oct. 1999 1747-1758.
[5] Zhang Guodong Subcarrier and bit allocation for real-time services in multiuser OFDM systems in proceedings of 2004 IEEE International Conference on Communicafions Paris, France June 2004 2985-2989.
[6] Wang Yongxue, Chen Fangjiong, Wei Gang Adaptive subcarrier and bit allocation for multiuser OFDM system based on genetic algorithm in proceedings of 2005 IEEE International Conference on Communications, Circuits and Systems Hong Kong, China May 2005 242-246.
[7] Wang Qian, Xu Dan, Xu Jing et al. A Grouped and Proportional-Fair Subcarrier Allocation Scheme for Multiuser OFDM Systems in proceedings of IPCCC 2006 Phoenix, AZ, United States April 2006 97-101.
[8] Rhee W., Cioffi J.M. Increase in capacity of multiuser OFDM system using dynamic subchannel allocation in proceedings of IEEE 51th VTC Tokyo, Japan May 2000 1085-1089.
[9] Shi Jing, Yu Guanding, Zhang Zhaoyang et al. Resource Allocation in OFDM based Multihop Wireless Networks in proceedings of VTC 2006-Spring Melbourne, Australia 2006 319-323.
[10] Wong I. C., Shen Zukang, Evans B.L., Andrews J.G. A low complexity algorithm for proportional resource allocation in OFDMA systems in proceedings of IEEE SIPS 2004 Austin, TX, United States 2004 1-6.
[11] Shen Zukang, Andrews J.G., Evans B.L. Optimal power allocation in multiuser OFDM systems in proceedings of IEEE GLOBECOM 2003 San Francisco Dec. 2003 337-341.
[12] Shen Zukang, Andrews J.G., Evans B.L. Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints IEEE Transactions on Wireless Communications 4(6) Nov. 2005 2726-2737.
[13] Mohanram C., Bhashyam S. A Sub-optimal Joint Subcarrier and Power Allocation Algorithm for Multiuser OFDM IEEE Commun. Lett. 9(8) Aug.2005 685 - 687.
[14] Seo H., Lee B. G. Proportional-Fair Power Allocation with CDF-Based Scheduling for Fair and Efficient Multiuser OFDM Systems IEEE Transactions on Wireless Communications 5(5) May 2006 978-983.
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[17] Kim Keunyoung, Kim Hoon, Han Youngnam Subcarrier and power allocation in OFDMA systems in proceedings of IEEE VTC 2004 Fall Los Angeles, California, USA Sept. 2004 1058-1062.
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[19] Choe Kwang Don, Lim Yeon Ju, Park Sang Kyu Subcarrier allocation with low-complexity in multiuser OFDM systems in proceedings of IEEE MILCOM 2004 Monterey, CA, United States 2004 822-826.
[20] Zhang X., Wang W., Liu Y. An efficient multiuser frequency-time grid (FTG) allocation algorithm for OFDM-based broadband wireless systems in proceedings of PIMRC Barcelona, Spain Sept. 2004 2092-2096.
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[22] Zhang Xing, Wang Yirong, Wang Wenbo Capacity analysis of adaptive multiuser frequency-time domain radio resource allocation in OFDMA systems in proceedings of IEEE ISCAS 2006 Kos,Greece May 2006 21-24.
[23] Chemaly R., Letaief K.B., Zeghlache D. Adaptive resource allocation for multiuser MIMO/OFDM networks based on partial channel state information in proceedings of IEEE GLOBECOM St. Louis, MO, United States Dec. 2005 3922-3926.
[24] Lau V., Wu Tianyu Optimal transmission and feedback design for OFDM/MIMO systems in frequency selective fading channels with limited feedback in proceedings of Vehicular Technology Conference 2005 Fall Dallas, Texas USA Sept. 2005 2124-2128.
[25] Mielczarek Bartosz , Krzymien Witold Flexible channel feedback quantization in multiple antenna systems in proceedings of IEEE Vehicular Technology Conference 2005 Spring Stockholm, Sweden 2005 620-624.
[26] Torabi M., Soleymani M.R., Aissa S. On the performance of MIMO-OFDM systems with imperfect channel information in proceedings of International Conference on Wireless Networks, Communications and Mobile Computing 2005 Maui, HI June 2005 600-605.
[27] Torabi M., Aissa S., Soleymani M.R. MIMO-OFDM Systems with Imperfect Channel Information: Capacity, Outage and BER Performance in proceedings of IEEE ICC Istanbul, Turkey June 2006 5342-5347.
[28] Lau Vincent K. N., Wu Tianyu Optimal transmission and limited feedback design for OFDM/MIMO systems in frequency selective block fading channels IEEE Transactions on Wireless Communications 6(5) May, 2007 1569-1573.
[29] Jiang Z., Ge Y., Li Y. Max-utility wireless resource management for best-effort traffic IEEE Transactions on Wireless Communications 4(1) Jan. 2005 100-111.
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[31] Damji N., Le-Ngoc T. Dynamic resource allocation for delay-tolerant services in downlink OFDM wireless cellular systems in proceedings of IEEE ICC Seoul, South Korea May 2005 3095-3099.
[32] Wu Heng-Yi, Chang Min-Kuan, Chang Chia-Chung The Switch-based Subcarrier Allocation Policies in Multi-service OFDM Systems in proceedings of IEEE VTC Melbourne, Australia May 2006 1328-1332.
[33] Damji, N., Le-Ngoc T. Dynamic downlink OFDM resource allocation with interference mitigation and macro diversity for multimedia services in wireless cellular systems in proceedings of IEEE Wireless Communications and Networking Conference 2005 New Orleans, LA, United States March 2005 13-17.
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[36] Xergias S., Passas N., Salkintzis A.K. Centralized Resource Allocation for Multimedia Traffic in IEEE 802.16 Mesh Networks Proceedings of the IEEE 96(1) Jan. 2008 54-63.
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[39] Makarevitch B. Distributed Scheduling for WIMAX Mesh Network IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications Helsinki, Finland Sept. 2006 1-5.
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[41] Suh Changho, Park Seunghoon, Cho Youngkwon Efficient algorithm for proportional fairness scheduling in multicast OFDM systems in proceedings of IEEE Vehicular Technology Conference 2005 May-Jun. 2005 Stockholm, Sweden 1880-1884.
[42] Seo Hanbyul, Kwack Seoshin, Lee Byeong Gi Channel structuring and subchannel allocation for efficient multicast and unicast services integration in wireless OFDM systems in proceedings of IEEE Global Telecommunications Conference Washington, D.C. 2007 4488-4493.
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[45] Pan Chengkang, Cai Yueming, Xu Youyun Adaptive subcarrier and power allocation for multiuser MIMO-OFDM systems in proceedings of IEEE ICC Seoul, South Korea May 2005 2631-2635.
[46] Zhang Ying Jun, Letaief K.B. An efficient resource-allocation scheme for spatial multiuser access in MIMO/OFDM systems IEEE Transactions on Communications 53(1) Jan. 2005 107-116.
[47] Zhang Ying Jun, Letaief K.B. Adaptive resource allocation for multiaccess MIMO/OFDM systems with matched filtering IEEE Transactions on Communications 53(11) Nov. 2005 1810-1816.
[48] Tang Jia, Zhang Xi Cross-layer design of dynamic resource allocation with diverse QoS guarantees for MIMO-OFDM wireless networks in proceedings of IEEE WoWMoM Taormina, Giardini June 2005 205-212.
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[50] Zhou Shidong, Zhao Ming, Xu Xibin et al. Distributed wireless communication system: A new architecture for future pubilc wireless access IEEE Communications Magazine 41(3) Mar. 2003 108-113.
[51] Peng Chen, Weiling Wu QoS-Guaranteed Downlink Transmission Optimization for Multi-Service Distributed Antenna System in proceedings of IEEE GLOBECOM 2005 St. Louis, MO, United States Dec. 2005 3826-3830.
[52] Peng Chen, Weiling Wu, Benteng Ma Performance Analysis for CDMA Distributed Antena Systems in an Imperfect Multipath Environment in proceedings of IEEE ICC 2005 Seoul, South Korea May 2005 3412-3416.
[53] Xu Wenjun, He Zhiqiang, Niu Kai, Wu Weiling A Grouped Resource Allocation Scheme in OFDMA Systems with Proportional Rate Constraints in 2007 World Wireless Congress Proceeding Stanford, California May 2007 238-243.
[54] Xu Wenjun, He Zhiqiang, Niu Kai, Wu Weiling Joint Power and Subcarrier Allocation for an Uplink OFDMA System with Proportional Rate Constraints in 2007 World Wireless Congress Proceeding Stanford, California May 2007 250-254.
[55] Xu Wenjun, Niu Kai, He Zhiqiang A Probabilistic Channel Quantization Scheme Based on Max-SNR Resource Allocation in Multiuser OFDM Systems IEEE Communications Letters. (已录用)
[56] Xu Wenjun, He Zhiqiang, Wu Weiling Subcarrier assignment considering path loss for multiuser OFDM systems with proportional fairness The Journal of China Universities of Posts and Telecommunications. (已录用)
[57] Xu Wenjun, Niu Kai, He Zhiqiang, Wu Weiling Resource Allocation in Multiuser OFDM Distributed Antenna Systems in Proceedings of IEEE 66th Vehicular Technology Conference Baltimore Oct. 2007 1797-1801.
[58] Xu Wenjun, Niu Kai, He Zhiqiang, Wu Weiling Time-Frequency Resource Allocation for Min-Rate Guaranteed Services in OFDM Distributed Antenna Systems in Proceedings of IEEE Global Telecommunications Conference Washington, D.C. Nov. 2007 3714-3718.
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[60]许文俊,贺志强,牛凯,吴伟陵 第三代移动通信系统网络层性能的快速仿真方法 中国 发明专利 申请号:200610057094.1 公开日:2006.08.16。
[61]许文俊,牛凯,贺志强,吴伟陵 第三代移动通信系统网络层性能的动态仿真装置和方法 中国 发明专利 申请号:200610082843.6 公开日:2006.12.27。
[62]Ghorashi S.A.,Homayounvala E.,Said F.,et al.Dynamic simulator for studying WCDMA based hierarchical cell structures in proceedings of PIMRC 2001 California 2001D-32-D-37.
[63]Haring L.,Chalise B.K.,Czylwik A.Dynamic system level simulations of downlink beamforming for UMTS FDD in proceedings of GLOBECOM 2003 San Francisco 2003 492-496.
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[4]Wong C.Y.,Cheng R.S.,Letaief K.B.et al.Multicarrier OFDM with adaptive subcarrier,bit,and power allocation IEEE Journal on Selected Areas in Communications 17(10)Oct.1999 1747-1758.
[5]Jang Jiho,Lee Kwang Bok Transmit power adaptation for multiuser OFDM systems IEEE Journal on Selected Areas in Communications 21(2)Feb.2003 171-178.
[6]Shen Zukang,Andrews J.G,Evans B.L.Optimal power allocation in multiuser OFDM systems in proceedings of IEEE GLOBECOM 2003 San Francisco Dec.2003337-341.
[7]Wong I.C.,Shen Zukang,Evans B.L.,Andrews J.G.A low complexity algorithm for proportional resource allocation in OFDMA systems in proceedings of IEEE SIPS 2004Austin,TX,United States 2004 1- 6.
[8]Shen Zukang,Andrews J.G.,Evans B.L.Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints IEEE Transactions on Wireless Communications 4(6) Nov. 2005 2726-2737.
[9] Mohanram C., Bhashyam S. A Sub-optimal Joint Subcarrier and Power Allocation Algorithm for Multiuser OFDM IEEE Commun. Lett. 9(8) Aug.2005 685-687.
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[17] Choi Jung Min, Lee Hyojin, Chung Hyun Kyu et al. Sounding method for proportional fair scheduling in ofdma/fdd uplink in proceedings of VTC2007-Spring Dublin, Ireland 2007 2732-2735.
[18] Kim Keunyoung, Kim Hoon, Han Youngnam Subcarrier and power allocation in OFDMA systems in proceedings of IEEE VTC 2004 Fall Los Angeles, California, USA Sept. 2004 1058-1062.
[19] Kim Keunyoung, Kim Hoon, Han Youngnam et al. Iterative and greedy resource allocation in an uplink OFDMA system in proceedings of IEEE PIMRC Barcelona, Spain Sept. 2004 2377-2381.
[20]Xu Wenjun,He Zhiqiang,Niu Kai,Wu Weiling A Grouped Resource Allocation Scheme in OFDMA Systems with Proportional Rate Constraints in 2007 World Wireless Congress Proceeding Stanford,California May 2007 238-243.
[21]Xu Wenjun,He Zhiqiang,Niu Kai,Wu Weiling Joint Power and Subcarrier Allocation for an Uplink OFDMA System with Proportional Rate Constraints in 2007 World Wireless Congress Proceeding Stanford,California May 2007 250-254.
[22]Wang Qian,Xu Dan,Xu Jing et al.A Grouped and Proportional-Fair Subcarrier Allocation Scheme for Multiuser OFDM Systems in proceedings of IPCCC 2006 Phoenix,AZ,United States April 2006 97-101.
[23]IEEE Working Group 802.20 IEEE 802.20 PD-08 Channel Models Document for IEEE 802.20 MBWA System Simulations IEEE Press Sep.2005.
[24]Baldick R.Optimization of Engineering Systems Course Notes Austin,TX:Univ.Texas.[Online].Available:http://www.ece.utexas.edu/-baldick/.
[1]Jang Jiho,Lee Kwang Bok Transmit power adaptation for multiuser OFDM systems IEEE Journal on Selected Areas in Communications 21(2)Feb.2003 171-178.
[2]Mohanram C.,Bhashyam S.A Sub-optimal Joint Subcarrier and Power Allocation Algorithm for Multiuser OFDM IEEE Commun. Lett. 9(8) Aug.2005 685-687.
[3] Shen Z., Andrews J. G., Evans B. L. Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints IEEE Transactions on Wireless Communications 4(6) Nov. 2005 2726-2737.
[4] Prince K., Krongold B., Dey, S. Efficient Rate-Power Allocation for OFDM in a Realistic Fading Environment in proceedings of VTC2007-Spring Dublin, Ireland April 2007 3076-3080.
[5] Wong I.C., Evans B.L. Optimal OFDMA Subcarrier, Rate, and Power Allocation for Ergodic Rates Maximization with Imperfect Channel Knowledge in proceedings of ICASSP 2007 Honolulu, Hawaii, U.S.A. April 2007 111-89 -111-92.
[6] Xia Pengfei, Zhou Shengli, Giannakis Georgios B. Adaptive MIMO-OFDM Based on Partial Channel State Information IEEE Transactions on Signal Processing 52(1) January, 2004 202-213.
[7] Pan Ya-Han, Letaief Khaled Ben, Cao Zhigang Dynamic resource allocation with adaptive beamforming for MIMO/OFDM systems under perfect and imperfect CSI in proceedings of 2004 IEEE Wireless Communications and Networking Conference Atlanta, GA, United States 2004 93-97.
[8] Chemaly R., Letaief K.B., Zeghlache D. Adaptive resource allocation for multiuser MIMO/OFDM networks based on partial channel state information in proceedings of IEEE GLOBECOM St. Louis, MO, United States Dec. 2005 3922-3926.
[9] Lau V., Wu Tianyu Optimal transmission and feedback design for OFDM/MIMO systems in frequency selective fading channels with limited feedback in proceedings of Vehicular Technology Conference 2005 Fall Dallas, Texas USA Sept. 2005 2124-2128.
[10] Torabi M., Soleymani M.R., Aissa S. On the performance of MIMO-OFDM systems with imperfect channel information in proceedings of International Conference on Wireless Networks, Communications and Mobile Computing 2005 Maui, HI June 2005 600-605.
[11] Torabi M., Aissa S., Soleymani M.R. MIMO-OFDM Systems with Imperfect Channel Information: Capacity, Outage and BER Performance in proceedings of IEEE ICC Istanbul, Turkey June 2006 5342-5347.
[12] Huang Weilan, Letaief K.B., Zhang Ying Jun CSIT-Adaptive Multiuser Resource Management for Space-Time Coded MIMO/OFDM Systems in proceedings of 2006 IEEE International Conference on Communications Istanbul, Turkey June 2006 4397 - 4402.
[13] Pan Ya-Han, Aissa S. Dynamic resource allocation with beamforming for MIMO OFDM systems: performance and effects of imperfect CSI IEEE Transactions on Wireless Communications 6(12) December 2007 4249-4255.
[14] Yu Jiang, Cai Yueming, Ma Yuehuai et al. A cross-layer design of packet scheduling and resource allocation for multiuser MIMO-OFDM systems in proceedings of 2007 Global Mobile Congress Shanghai, China Oct. 2007 65-70.
[15] Lau V. K. N., Wu T. Optimal transmission and limited feedback design for OFDM/MIMO systems in frequency selective block fading channels IEEE Transactions on Wireless Communications 6(5) May, 2007 1569-1573.
[16] Mielczarek B., Krzymien W. Flexible channel feedback quantization in multiple antenna systems in proceedings of IEEE Vehicular Technology Conference 2005 Spring Stockholm, Sweden 2005 620-624.
[17] Xu Wenjun, Niu Kai, He Zhiqiang A Probabilistic Channel Quantization Scheme Based on Max-SNR Resource Allocation in Multiuser OFDM Systems IEEE Communications Letters. (已录用)
[18] Clark A., Smith P. J., Taylor D. P. Instantaneous Capacity of OFDM on Rayleigh-Fading Channels IEEE Transactions on Information Theory 53(1) Jan. 2007 355-361.
[19] Baldick R. Optimization of Engineering Systems Course Notes Austin, TX: Univ. Texas. [Online]. Available: http://www.ece.utexas.edu/-baldick/.
[20] IEEE Working Group 802.20 IEEE 802.20 PD-08 Channel Models Document for IEEE 802.20 MB WA System Simulations IEEE Press Sep. 2005.
[1]Wong C Y,Cheng R S,Letaief K B,et al.Multicarrier OFDM with adaptive subcarrier,bit,and power allocation IEEE Journal on Selected Areas in Communications 17(10)Oct. 1999 1747-1758.
[2] Rhee W., Cioffi J. M. Increase in capacity of multiuser OFDM system using dynamic subchannel allocation in proceedings of IEEE 51th VTC Tokyo, Japan May 2000 1085-1089.
[3] LU Yan-hui, LUO Tao, YIN Chang-chuan, et al. Adaptive radio resource allocation for multiple traffic OFDMA broadband wireless access system The Journal of China Universities of Posts and Telecommunications 13(4) 2006 1-6.
[4] Wong I. C., Shen Zukang, Evans B.L., Andrews J.G. A low complexity algorithm for proportional resource allocation in OFDMA systems in proceedings of IEEE SIPS 2004 Austin, TX, United States 2004 1-6.
[5] Shen Zukang, Andrews J. G., Evans B.L. Optimal power allocation in multiuser OFDM systems in proceedings of IEEE GLOBECOM 2003 San Francisco Dec. 2003 337-341.
[6] Shen Zukang, Andrews J. G., Evans B. L. Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints IEEE Transactions on Wireless Communications 4(6) Nov. 2005 2726-2737.
[7] Mohanram C., Bhashyam S. A Sub-optimal Joint Subcarrier and Power Allocation Algorithm for Multiuser OFDM IEEE Commun. Lett. 9(8) Aug.2005 685-687.
[8] Xu Wenjun, He Zhiqiang, Wu Weiling Subcarrier assignment considering path loss for multiuser OFDM systems with proportional fairness The Journal of China Universities of Posts and Telecommunications. (已录用)
[9] Jang Jiho, Lee Kwang Bok Transmit power adaptation for multiuser OFDM systems IEEE Journal on Selected Areas in Communications 21(2) Feb. 2003 171-178.
[10] Zhang Guodong Subcarrier and bit allocation for real-time services in multiuser OFDM systems in proceedings of 2004 IEEE International Conference on Communications Paris, France June 2004 2985-2989.
[11] Wang Yongxue, Chen Fangjiong, Wei Gang Adaptive subcarrier and bit allocation for multiuser OFDM system based on genetic algorithm in proceedings of 2005 IEEE International Conference on Communications, Circuits and Systems Hong Kong, China May 2005 242-246.
[12] Baldick R. Optimization of Engineering Systems Course Notes Austin, TX: Univ. Texas. [Online], Available: http://www.ece.utexas.edu/-baldick/.
[13] IEEE Working Group 802.20 IEEE 802.20 PD-08 Channel Models Document for IEEE 802.20 MBWA System Simulations IEEE Press Sep. 2005.
[14]许文俊,贺志强,牛凯,吴伟陵 第三代移动通信系统网络层性能的快速仿真方法 中国 发明专利 申请号:200610057094.1 公开日:2006.08.16。
[15]许文俊,牛凯,贺志强,吴伟陵 第三代移动通信系统网络层性能的动态仿真装置和方法 中国 发明专利 申请号:200610082843.6 公开日:2006.12.27。
[16]许文俊,刘德昌,贺志强,牛凯,吴伟陵 一种低计算复杂度WCDMA功率控制仿真算法 北京邮电大学学报 30(4)2007年8月 93-97。
[17]Ghorashi S.A.,Homayounvala E.,Said F.,et al.Dynamic simulator for studying WCDMA based hierarchical cell structures in proceedings of PIMRC 2001 California 2001D-32-D-37.
[18]Haring L.,Chalise B.K.,Czylwik A.Dynamic system level simulations of downlink beamforming for UMTS FDD in proceedings of GLOBECOM 2003 San Francisco 2003 492-496.
[1] Jang J., Lee K. B. Transmit power adaptation for multiuser OFDM systems IEEE Journal on Selected Areas in Communications 21(2) Feb. 2003 171-178.
[2] Shen Z., Andrews J. G., Evans B. L. Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints IEEE Transactions on Wireless Communications 4(6) Nov. 2005 2726-2737.
[3] Song G., Li Y. Cross-layer optimization for OFDM wireless networks—Part I: theoretical framework IEEE Trans, on Wireless Communications 4(2) March 2005 614-624.
[4] Letaief K. B., Ying J. Jun Dynamic multiuser resource allocation and adaptation for wireless systems IEEE Wireless Communications 13(4) Aug. 2006 38-47.
[5] Zhang Ying J., Letaief K. B. An efficient resource-allocation scheme for spatial multiuser access in MIMO/OFDM systems IEEE Transactions on Communications 53(1) Jan. 2005 107-116.
[6] Zhang Ying J., Letaief K. B. Adaptive resource allocation for multiaccess MIMO/OFDM systems with matched filtering IEEE Transactions on Communications 53(11) Nov. 2005 1810-1816.
[7] Zhou S., Zhao M., Xu X. et al. Distributed wireless communication system: A new architecture for future pubilc wireless access IEEE Communications Magazine 41(3) Mar. 2003 108-113.
[8] Chen P., Wu W., Ma B. Performance Analysis for CDMA Distributed Antena Systems in an Imperfect Multipath Environment in proceedings of IEEE ICC 2005 Seoul, South Korea May 2005 3412-3416.
[9] Chen P., Fu J., Kim Y, Park Y. Reverse-link macrodiversity in CDMA distributed antenna systems with imperfect channel estimation in proceedings of Wireless Communications and Networking Conference Las Vegas, NV, United States Apr. 2006 1899 - 1903,
[10] Briso-Rodriguez C., Cruz J.M., Alonso J.I. Measurements and Modeling of Distributed Antenna Systems in Railway Tunnels IEEE Transactions on Vehicular Technology 56(5) Sept. 2007 2870 - 2879.
[11] Marsch P., Khattak S., Fettweis G. A Framework for Determining Realistic Capacity Bounds for Distributed Antenna Systems IEEE Information Theory Workshop 2006 Punta del Este, Uruguay Oct. 2006 571-575.
[12] Wang Xiao-Dong, Rui Guo-Sheng, Xu Hao-Yu et al. Delay Spread Statistics in a Two-Path Distributed Antenna System with Gamma-Lognormal Fading Channels IEEE Communications Letters 11(8)August 2007 656 - 658.
[13]Shen Ying,Tang Youxi,Kong Ting Optimal antenna location for STBC-OFDM downlink with distributed transmit antennas in linear cells IEEE Communications Letters 11(5)May 2007 387- 389.
[14]Liu Shenfa,He ZhiQiang,Wu Weiling Transmit Diversity Method with User's Power Constraint for Distributed Antenna System in proceedings of ISWPC 2007 San Juan,PR,United States Feb.2007 247-251.
[15]Xu Wenjun,Niu Kai,He Zhiqiang,Wu Weiling Resource Allocation in Multiuser OFDM Distributed Antenna Systems in Proceedings of IEEE 66th Vehicular Technology Conference Baltimore Oct.2007 1797-1801.
[16]Clark A.,Smith P.J.,Taylor D.P.Instantaneous Capacity of OFDM on Rayleigh-Fading Channels IEEE Transactions on Information Theory 53(1)Jan.2007 355-361.
[17]IEEE Working Group 802.20 IEEE 802.20 PD-08 Channel Models Document for IEEE 802.20 MBWA System Simulations IEEE Press Sep.2005.
[18]Rhee W.,Cioffi J.M.Increase in capacity of multiuser OFDM system using dynamic subchannel allocation in proceedings of IEEE 51th VTC Tokyo,Japan May 20001085-1089.
[19]许文俊,贺志强,牛凯,吴伟陵 第三代移动通信系统网络层性能的快速仿真方法 中国 发明专利 申请号:200610057094.1 公开日:2006.08.16。
[20]许文俊,牛凯,贺志强,吴伟陵 第三代移动通信系统网络层性能的动态仿真装置和方法 中国 发明专利 申请号:200610082843.6 公开日:2006.12.27。
[21]许文俊,刘德昌,贺志强,牛凯,吴伟陵 一种低计算复杂度WCDMA功率控制仿真算法 北京邮电大学学报 30(4)2007年8月 93-97。
[22]Ghorashi S.A.,Homayounvala E.,Said F.,et al.Dynamic simulator for studying WCDMA based hierarchical cell structures in proceedings of PIMRC 2001 California 2001D-32-D-37.
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[2] Letaief K.B., Zhang Ying Jun Dynamic multiuser resource allocation and adaptation for wireless systems IEEE Wireless Communications 13(4) Aug. 2006 38-47.
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[1]吴伟陵,牛凯 移动通信原理 电子工业出版社 2005年1月 1-7。
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[3]3GPP 25.814 Physical Layer Aspects for Evolved Universal Terrestrial Radio Access (UTRA)(Release 7)2006.
[4]3GPP R1-050841 Further Analysis of Soft Frequency Reuse Scheme 2005.
[5]3GPP R1-051059 Inter-Cell Interference Mitigation for EUTRA 2005.
[6]3GPP R1-060291 OFDMA Downlink inter-cell interference mitigation 2006.
[7]3GPP R1-062375 Downlink Interference Coordination 2006.