基于OFDMA的宽带无线通信系统资源分配研究
|
摘要
随着人们对高速数据通信需求的迅速增长,无线通信系统中的频谱资源变得越来越稀缺,促使人们去开发能够有效地利用无线资源的新技术。OFDMA技术可以改善系统的频谱效率,并且能够有效对抗由于多径效应等引起的窄带干扰,因此成为宽带无线通信系统的关键技术。本学位论文的研究工作主要围绕OFDMA系统的资源分配问题展开,利用凸优化以及智能计算等技术手段,分别提出了在蜂窝网络、中继蜂窝网络以及认知网络等三种网络架构下的OFDMA系统的资源分配算法,具体的研究内容与成果如下:
(1)研究了多用户OFDMA系统中基于速率最大化的RA(速率自适应)问题。提出了一种基于对偶分解的分布式资源分配方法,把一个复杂的优化问题分解为若干个关于用户的子问题进行并行求解。各个用户只需要利用自身的CSI的值以及基站提供的相关参数,就可以通过对子问题的求解获得各自的子载波以及功率的分配方案。所提算法具有较低计算的复杂度,并且能减少基站的计算负荷。仿真结果表明,所提算法能够在较少的迭代步数内得到一个近似最优解,并且与最优解的性能非常接近。
(2)研究了多用户OFDMA系统中基于功率最小化的MA(余量自适应)问题。提出了一种基于蚁群算法的资源分配方案,首先将MA问题建模成一个二部图,而对原问题的求解等效为在图中寻找一条最优路径的问题,然后利用蚁群算法使蚂蚁在信息素及启发式信息的引导下构建最优的路径。由于蚁群算法具有信息正反馈以及启发式搜索等特点,使得算法可以通过较少的迭代收敛到一个近似最优解。仿真结果表明,所提算法的性能与最优值相比仅相差1dB,而与静态子载波分配算法相比性能可以提高1~2dB。
(3)研究了多跳中继的OFDMA系统的资源分配。建立了一个在每个接入节点功率受限,以及满足每个用户最小速率要求的条件下,最大化系统容量的数学模型。提出一种基于对偶分解的资源分配算法,将该问题分解成若干个关于各个子载波的子问题。通过对各子问题的求解,可以获得最优的中继节点的选择方案以及功率、子载波的分配方案。仿真结果表明,该算法收敛速度较快,并且能够在保障不同用户速率需求的前提下,有效地提高系统的容量。
(4)研究了解码转发中继OFDMA系统的资源分配。提出了一种基于比例公平的资源分配算法,有效地解决了系统容量与用户公平性之间的矛盾,并且采用子载波配对的方式进一步提高系统的容量。根据比例公平的准则,建立了一个在系统总功率受限的条件下,最大化各用户的速率对数和的数学模型。为降低求解的复杂度,将优化问题分为两个步骤,首先在等功率分配的情况下求出最优的子载波分配以及中继节点选择的方案,然后再根据注水定理进行功率分配。仿真结果表明,所提算法能够在保证用户公平性的条件下,有效的提高系统的容量。
(5)研究了认知网络OFDMA系统中的资源分配。在实际的系统中,认知网络往往不能获得授权网络中主用户的信道状态信息的准确值,因此文中研究在非完美信道状态信息下的资源分配算法。首先将该问题建模为一个在主用户的中断概率小于限定值的条件下,最大化认知用户系统容量的数学模型。然后采用基于对偶分解的资源分配算法求出了最优的功率以及子载波的分配方案。最后通过仿真分析了非完美的CSI值对系统性能的影响,并且验证了所提算法的有效性。
With the fast growing demand for high data rate communication, the spectrum resource in wireless communication systems becomes more and more scarce. This stimulates the development of new technology to achieve efficient utilization of radio resources. OFDMA is capable of enhancing the spectrum efficiency of the system. Furthermore, it can combat the inter-symbol interference causing by the multi-path effect. Therefore, OFMDA has become one of the key technologies in broadband wireless systems. This thesis focuses on the problem of resource allocation in the OFDMA systems. By utilizing the tools of convex optimization and intelligent computing, the resource allocation algorithms in three kinds of networks including the cellular network, the relay cellular network and the cognitive network based on OFDMA are proposed. The main work and contributions of this thesis are as follows:
1) The problem of Rate Adaptive (RA) in multiuser OFDMA system is studied. A distributed resource allocation algorithm based on dual decomposition is proposed. The complicated problem is decoupled into several subproblems with regard to the users that can be solved parallelly. Each user can obtain its own subcarriers and power allocation scheme by solving the subproblem according to its local channel state information and the corresponding parameters provided by the base station. The proposed algorithm has a low computational complexity, and is able to reduce the calculation payload of the base station. Simulation results show that the proposed algorithm can converge quickly to an approximated optimal solution in a small number of iterations.
2) The problem of Margin Adaptive (MA) in multiuser OFDMA system is studied. An ant colony algorithm based adaptive resource allocation is proposed. The problem is modelled as finding a minimum cost path in a graph. The ants’solution construction is guided by pheromone trail and heuristic information. Due to the unique heuristic searching mechanism of the ant colony algorithm, the proposed algorithm is guaranteed to converge quickly to an approximately optimal solution. Simulation results show that the proposed algorithm only loses 1 dB in comparison with the optimal solution, and gains 1-2dB in comparison with the fixed allocation scheme.
3) Resource allocation in multihop relay OFDMA systems is studied. The problem is formulated as a sum rate maximization problem subject to each access node’s transmission power and each user’s minimum rate constraints. A dual decomposition based resource allocation algorithm is proposed. The problem is decomposed into several subproblems with regard to the subcarriers. By solving the subproblems, the optimal relay selection scheme, power allocation and subcarrier assignment is obtained. Simulation results show that the proposed algorithm can efficiently improve the capacity of the system, and guarantee each user’s QoS requirement at the same time.
4) Resource allocation in Decode and Forward (DF) relay OFDMA systems is studied. A proportional fairness based resource allocation algorithm is proposed. The proposed algorithm performs a good tradeoff between system capacity and fairness. The system capacity is further improved by subcarrier matching. According to the proportional fairness criterion, the problem is formulated as a sum logarithmic rate maximization problem subject to total transmission power. In order to reduce the computational complexity, the original optimization problem is decomposed into two steps. In the first step, the subcarrier assignment and relay selection scheme are determined based on equal power allocation. In the second step, the power allocation is achieved by waterfilling theorem. Simulation results show that the proposed algorithm can efficiently improve the capacity of the system, and guarantee the fairness among users simultaneously.
5) Resource allocation in cognitive OFDMA system is studied. In practical systems, it may not be possible for the cognitive transmitter to know the perfect channel state information value of the primary user in the authorized network. So the resource allocation with imperfect channel state information is studied in this thesis. The problem is formulated as a weighted sum rate maximization problem subjected to each primary user’s outage possibility constrains. A dual decomposition based resource allocation algorithm is proposed to obtain the optimal power and subcarrier allocation scheme. The influence of imperfect channel state information to the system is analyzed through simulation, and the efficiency of the proposed algorithm is also proved.
(1)研究了多用户OFDMA系统中基于速率最大化的RA(速率自适应)问题。提出了一种基于对偶分解的分布式资源分配方法,把一个复杂的优化问题分解为若干个关于用户的子问题进行并行求解。各个用户只需要利用自身的CSI的值以及基站提供的相关参数,就可以通过对子问题的求解获得各自的子载波以及功率的分配方案。所提算法具有较低计算的复杂度,并且能减少基站的计算负荷。仿真结果表明,所提算法能够在较少的迭代步数内得到一个近似最优解,并且与最优解的性能非常接近。
(2)研究了多用户OFDMA系统中基于功率最小化的MA(余量自适应)问题。提出了一种基于蚁群算法的资源分配方案,首先将MA问题建模成一个二部图,而对原问题的求解等效为在图中寻找一条最优路径的问题,然后利用蚁群算法使蚂蚁在信息素及启发式信息的引导下构建最优的路径。由于蚁群算法具有信息正反馈以及启发式搜索等特点,使得算法可以通过较少的迭代收敛到一个近似最优解。仿真结果表明,所提算法的性能与最优值相比仅相差1dB,而与静态子载波分配算法相比性能可以提高1~2dB。
(3)研究了多跳中继的OFDMA系统的资源分配。建立了一个在每个接入节点功率受限,以及满足每个用户最小速率要求的条件下,最大化系统容量的数学模型。提出一种基于对偶分解的资源分配算法,将该问题分解成若干个关于各个子载波的子问题。通过对各子问题的求解,可以获得最优的中继节点的选择方案以及功率、子载波的分配方案。仿真结果表明,该算法收敛速度较快,并且能够在保障不同用户速率需求的前提下,有效地提高系统的容量。
(4)研究了解码转发中继OFDMA系统的资源分配。提出了一种基于比例公平的资源分配算法,有效地解决了系统容量与用户公平性之间的矛盾,并且采用子载波配对的方式进一步提高系统的容量。根据比例公平的准则,建立了一个在系统总功率受限的条件下,最大化各用户的速率对数和的数学模型。为降低求解的复杂度,将优化问题分为两个步骤,首先在等功率分配的情况下求出最优的子载波分配以及中继节点选择的方案,然后再根据注水定理进行功率分配。仿真结果表明,所提算法能够在保证用户公平性的条件下,有效的提高系统的容量。
(5)研究了认知网络OFDMA系统中的资源分配。在实际的系统中,认知网络往往不能获得授权网络中主用户的信道状态信息的准确值,因此文中研究在非完美信道状态信息下的资源分配算法。首先将该问题建模为一个在主用户的中断概率小于限定值的条件下,最大化认知用户系统容量的数学模型。然后采用基于对偶分解的资源分配算法求出了最优的功率以及子载波的分配方案。最后通过仿真分析了非完美的CSI值对系统性能的影响,并且验证了所提算法的有效性。
With the fast growing demand for high data rate communication, the spectrum resource in wireless communication systems becomes more and more scarce. This stimulates the development of new technology to achieve efficient utilization of radio resources. OFDMA is capable of enhancing the spectrum efficiency of the system. Furthermore, it can combat the inter-symbol interference causing by the multi-path effect. Therefore, OFMDA has become one of the key technologies in broadband wireless systems. This thesis focuses on the problem of resource allocation in the OFDMA systems. By utilizing the tools of convex optimization and intelligent computing, the resource allocation algorithms in three kinds of networks including the cellular network, the relay cellular network and the cognitive network based on OFDMA are proposed. The main work and contributions of this thesis are as follows:
1) The problem of Rate Adaptive (RA) in multiuser OFDMA system is studied. A distributed resource allocation algorithm based on dual decomposition is proposed. The complicated problem is decoupled into several subproblems with regard to the users that can be solved parallelly. Each user can obtain its own subcarriers and power allocation scheme by solving the subproblem according to its local channel state information and the corresponding parameters provided by the base station. The proposed algorithm has a low computational complexity, and is able to reduce the calculation payload of the base station. Simulation results show that the proposed algorithm can converge quickly to an approximated optimal solution in a small number of iterations.
2) The problem of Margin Adaptive (MA) in multiuser OFDMA system is studied. An ant colony algorithm based adaptive resource allocation is proposed. The problem is modelled as finding a minimum cost path in a graph. The ants’solution construction is guided by pheromone trail and heuristic information. Due to the unique heuristic searching mechanism of the ant colony algorithm, the proposed algorithm is guaranteed to converge quickly to an approximately optimal solution. Simulation results show that the proposed algorithm only loses 1 dB in comparison with the optimal solution, and gains 1-2dB in comparison with the fixed allocation scheme.
3) Resource allocation in multihop relay OFDMA systems is studied. The problem is formulated as a sum rate maximization problem subject to each access node’s transmission power and each user’s minimum rate constraints. A dual decomposition based resource allocation algorithm is proposed. The problem is decomposed into several subproblems with regard to the subcarriers. By solving the subproblems, the optimal relay selection scheme, power allocation and subcarrier assignment is obtained. Simulation results show that the proposed algorithm can efficiently improve the capacity of the system, and guarantee each user’s QoS requirement at the same time.
4) Resource allocation in Decode and Forward (DF) relay OFDMA systems is studied. A proportional fairness based resource allocation algorithm is proposed. The proposed algorithm performs a good tradeoff between system capacity and fairness. The system capacity is further improved by subcarrier matching. According to the proportional fairness criterion, the problem is formulated as a sum logarithmic rate maximization problem subject to total transmission power. In order to reduce the computational complexity, the original optimization problem is decomposed into two steps. In the first step, the subcarrier assignment and relay selection scheme are determined based on equal power allocation. In the second step, the power allocation is achieved by waterfilling theorem. Simulation results show that the proposed algorithm can efficiently improve the capacity of the system, and guarantee the fairness among users simultaneously.
5) Resource allocation in cognitive OFDMA system is studied. In practical systems, it may not be possible for the cognitive transmitter to know the perfect channel state information value of the primary user in the authorized network. So the resource allocation with imperfect channel state information is studied in this thesis. The problem is formulated as a weighted sum rate maximization problem subjected to each primary user’s outage possibility constrains. A dual decomposition based resource allocation algorithm is proposed to obtain the optimal power and subcarrier allocation scheme. The influence of imperfect channel state information to the system is analyzed through simulation, and the efficiency of the proposed algorithm is also proved.
引文
[1]彭木根,王文博.下一代宽带无线通信系统-OFDM与WiMAX [M].北京:机械工业出版社,2007
[2]陆彦辉. B3G移动通信系统中无线资源管理技术的研究[D].北京:北京邮电大学,2006
[3]刘辉,李国庆.基于OFDM的无线宽带网络设计与优化[M].任品毅译.西安:西安交通大学出版社,2008
[4] Qiu X, Chawla K. On the performance of adaptive modulation in cellular systems [J]. IEEE Transactions on Wireless Communications, 1999, 47(6): 884-895.
[5] Thomas M. C, Joy A. T. Elements of information theory [M] Wiley-Interscience, 1991
[6] Leke A, Cioffi J.M . A Maximum rate loading algorithm for discrete multitone modulation systems [C]. IEEE Global Telecommunications Conference. Phoenix: IEEE,1997: 1514–1518
[7] Yu w, Cioffi J.M. Constant power waterfilling: performance bound and low-complexity implementation [J] IEEE Transactions on Communications, 2006, 54(1): 23-28
[8] Hughes-Hartogs D. Ensemble modem structure for imperfect transmission media [P]. USA: 4 679 227 (July 1987), 4 731 816 (March 1988) and 4 833 796 (May 1989).
[9] Chow P. S, Cioffi J. M, Bingham J. A. C. Practical discrete multitone transceiver loading algorithm for data transmission over spectrally shaped channels [J]. IEEE Transactions on Communications, 1995, 45(2): 773–775
[10] Fischer R. F. H, Huber J. B. A new loading algorithm for discrete multitone transmission [C]. IEEE Global Telecommunications Conference. London: IEEE,1996: 724-728
[11] Li G, Liu H. On the optimality of the OFDMA network [J]. IEEE Communications Letters, 2005, 9(5): 438-440.
[12]汪裕民. OFDM关键技术与应用[M].北京.机械工业出版社,2007
[13] Jang J, Lee K. B, Transmit power adaptation for multiuser OFDM systems[J] IEEE Journal on Selected Areas in Communications, 2003, 21(2): 171-178.
[14] Rhee M, Cioffi J. M, Increasing in capacity of multiuser OFDM system using dynamic subchannel allocation[C] IEEE Vehicular Technology Conference, Tokyo: IEEE, 2000: 1085–1089
[15] Kim H, Han Y. A proportional fair scheduling for multicarrier transmission systems [J], IEEE communications letters, 2005, 9(3): 210-212
[16] Nguyen T. D, Han Y, A proportional fairness algorithm with QoS provision in downlink OFDMA systems [J], IEEE communications letters, 2006, 10(11): 760-762
[17] Kelly F.P. Charging and rate control for elastic traffic [J]. European Transactions on Telecommunications, 1997, 8(1): 33-37
[18] Kelly F.P, Maulloo A.K, Tan D.K.H. Rate control for communication networks: shadow prices, proportional fairness and stability [J]. Journal of The Operational Research Society, 1998, 49(3): 237-252
[19] Shen Z, Andrews J. G, Evans B. L. Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints [J]. IEEE Transactions on Wireless Communications, 2005, 4(6): 2726-2737
[20] Wong C. Y, Cheng R. S, Letaief K. B, et al. Multiuser OFDM with adaptive subcarrier, bit, and power allocation [J]. IEEE Journal on Selected Areas in Communications, 1999, 17(10): 1747-1758
[21]卫国,唐志华. OFDM系统动态资源分配研究[J].电子学报. 2007,35(6): 83-87
[22] Kivanc D, Li G, Liu H. Computationally efficient bandwidth allocation and power control for OFDMA [J]. IEEE Transactions on Wireless Communications, 2003, 2(6): 1150-1158.
[23] Kim I, Lee H.L, Kim B, et al. On the use of linear programming for dynamic subchannel and bit allocation in multiuser OFDM [C]. IEEE Global Telecommunications Conference, San Antonio: IEEE, 2001: 3648-3652
[24] Zhang X, Ye W, Feng S. L. Adaptive resource allocation for OFDMA system based on ant colony algorithm [C]. The 1st International Conference on Information Science and Engineering, Nanjing: IEEE, 2009: 2526-2529
[25] Ahmadi H, Chew Y H. Adaptive Subcarrier-and bit Allocation in Multiclass Multiuser OFDM Systems using Genetic Algorithms [C]. IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, Tokyo: IEEE, 2009:1883-1887
[26] Wong I.C. A unied framework for optimal resource allocation in multiuser multicarrier wireless systems [D]. Austin: The University of Texas. 2007
[27] Wong I.C, Evans B.L. Optimal OFDMA resource allocation with linear complexity to maximize ergodic weighted sum capacity [C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu: IEEE, 2007:601-604
[28] Yu W, Lui R. Dual methods for nonconvex spectrum optimization of multicarrier systems [J]. IEEE Transactions on Wireless Communications, 2006, 54(7): 1310-1322.
[29] Cendrillon R, Yu W, Moonen M, et al. Optimal multiuser spectrum balancing for digitalsubscriber lines [J]. IEEE Transactions on Communications, 2006, 54(5): 922-933.
[30] Song G. Cross-layer resource allocation and scheduling in wireless multicarrier networks [D]. Atlanta : Georgia Institute of Technology. 2005
[31] Song G, Li Y. Cross-Layer optimization for OFDM wireless networks—Part I: theoretical framework [J]. IEEE Transactions on Wireless Communications, 2005, 4(2): 614-624
[32] Song G, Li Y. Cross-Layer optimization for OFDM wireless networks—Part II: algorithm development [J]. IEEE Transactions on Wireless Communications, 2005, 4(2): 625-634
[33] Song G, Li Y. Utility-based resource allocation and scheduling in ofdm-based wireless broadband networks [J]. IEEE Communications Magazine, 2005, 43(12): 127-134
[34] Guo K, Sun L,Jia S. Utility function based fair data scheduling algorithm for OFDM wireless network [J]. Journal of Systems Engineering and Electronics, 2007,18(4): 731–738,2007
[35] Zhang Y.J, Letaief K.B. Link-adaptive largest-weighted-throughput packet scheduling for real-time traffics in wireless OFDM networks [C]. IEEE Global Telecommunications Conference, St Louis : IEEE, 2005: 2490-2494
[36] Zhang Y.J, Letaief K.B. Cross layer adaptive resource management for wireless packet networks with OFDM signaling [J] IEEE Transactions on Wireless Communications, 2006, 5(11): 3244-3254
[37] Zhang Y.J. A multi-server scheduling framework for resource allocation in wireless multi-carrier networks [J]. IEEE Transactions on Wireless Communications, 2007, 6(11):3864-3891
[38] Gesbert D, Alouini M. How much feedback is multi-user diversity really worth [C]. International Conference on Communications, Paris: IEEE, 2004: 234-238
[39] Svedman P, Wilson S, Cimini L, et al. A simplified opportunistic feedback and scheduling scheme for OFDM [C]. IEEE Vehicular Technology Conference, Milan: IEEE, 2004: 1878-1882
[40] Han Z. H, Lee Y. H. Opportunistic scheduling with partial channel information in OFDMA/FDD system [C]. IEEE Vehicular Technology Conference, Los Angeles: IEEE, 2004: 511-514
[41] Hwang G. U, Ishlzaki F. Design of a fair scheduler exploiting multiuser diversity with feedback information reduction [J]. IEEE Communications Letters, 2008, 12(2): 124-126
[42] Ko J. Y, Lee Y. H, Opportunistic transmission with partial channel information in multi-user OFDM wireless system [C]. IEEE Wireless Communications and Networking Conference, Hong Kong: IEEE, 2007:1319-1323
[43] Wong I.C, Evans B.L. Optimal resource allocation in the OFDMA downlink with imperfect channel knowledge [J]. IEEE Transactions on Communications, 2009, 57(1): 232-241
[44] Brah F, Vandendorpe L, Louveaux J. Constrained Resource Allocation in OFDMA Downlink Systems with partial CSIT [C]. IEEE International Conference on Communications, Beijing: IEEE, 2008: 4144-4148
[45] Awad M. K, Mahinthan V, Mehrjoo M, et al. A dual decomposition-based resource allocation fro OFDMA networks with imperfect CSI [J]. IEEE Transactions on Vehicular Technology, 2010, 59(5): 2394 - 2403
[46] Mokari N, Javan M. R, Navaie K. Cross-layer resource allocation in OFDMA systems for heterogeneous traffic with imperfect CSI [J]. IEEE Transactions on Vehicular Technology, 2010, 59(2): 1011 - 1017
[47] Ng D. W. K, Schober R. Cross-layer scheduling for OFDMA amplify-and-forward relay networks [C]. IEEE Vehicular Technology Conference, Anchorage: IEEE, 2009: 561-565
[48] Foschini G.J, Gans M.J. On limits of wireless communication in a fading environment when using multiple antennas [J]. Wireless Personal Communications, 1998, 6(3): 331-335
[49] Wong K, Cheng R.S.K, Letaief K.B. Adaptive antennas at the mobile and base stations in an OFDM/TDMA system [J]. IEEE Transactions on Communications, 2001, 49(1): 195-206
[50] Xiao X, Zhu G, Hu Z. Guaranteed QoS in dynamic resource allocation with adaptive beamforming for multiuser multi-antenna OFDM systems [C]. IEEE International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan: IEEE, 2005: 274-277
[51] Pan C, Cai Y, Xu Y. Adaptive subcarrier and power allocation for multiuser MIMO-OFDM systems [C]. IEEE International Conference on Communications, Seoul: IEEE, 2005: 2631-2635
[52] Ji T, Zhou C, Zhou S, et al. Low complex user selection strategies for multi-user MIMO downlink scenario [C]. IEEE Wireless Communications and Networking Conference, Hong Kong: IEEE, 2007:1535-1537
[53] Zhang J, Wu Y, Xu M, et al. Linear transmitter precoding design for downlink of multiuser MIMO systems [J]. Electronics Letters, 2005,41(14):811-813
[54] Wu Y, Zhang J, Xu M, et al. Multiuser MIMO downlink precoder design based on themaximal SJNR criterion [C]. IEEE Global Telecommunications Conference, St Louis : IEEE, 2005: 2694-2698
[55] Salem, M, Adinoyi A, Rahman M, et al. An overview of radio resource management in relay-enhanced OFDMA-based networks [J]. IEEE Communications Surveys & Tutorials, 2010, 12(3): 422-438
[56] Salem M, Adinoyi A, Yanikomeroglu H, et al. Opportunities and challenges in OFDMA-based cellular relay networks: A radio resource management perspective [J]. IEEE Transactions on Vehicular Technology, 2010, 59(5): 2496-2510
[57] Nam W, Chang W, Chung S.Y, et al. Transmit optimization for relay-based cellular OFDMA systems [C] IEEE International Conference on Communications. Glasgow: IEEE, 2007: 5714-5719
[58] Li G, Liu H. Resource allocation for OFDMA relay networks with fairness constraints [J]. IEEE Journal on Selected Areas in Communications, 2006, 24(11): 2061-2068.
[59] Ng D.W.K, Schober R. Cross-layer scheduling for OFDMA amplify-and forward relay networks [J]. IEEE Transactions on Vehicular Technology, 2010, 59(3): 1443-1458.
[60] Cui Y, Lau V.K.N, Wang R. Distributive subband allocation, power and rate control for relay-assisted OFDMA cellular system with imperfect system state knowledge [J]. IEEE Transactions on Wireless Communications, 2009, 8(10): 5096-5102
[61] Ng T.C.Y, Yu W. Joint optimization of strategies and resource allocations in cooperative cellular networks [J]. IEEE Journal on Selected Areas in Communications, 2007, 25(2): 328-339.
[62] Han Z, Himsoon T, Siriwongpairat W. P, et al. Resource Allocation for Multiuser Cooperative OFDM Networks Who Helps Whom and How to Cooperate [J]. IEEE Transactions on Vehicular Technology, 2009, 58(5): 2378-2391
[63] Weng L, Murch R. D. Cooperation strategies and resource allocations in multiuser OFDMA systems [J]. IEEE Transactions on Vehicular Technology, 2009, 58(5): 2331-2342
[64] Kim S. J, Wang X, Madihian M. Optimal resource allocation in multi-hop OFDMA wireless networks with cooperative relay [J]. IEEE Transactions on Wireless Communications, 2008, 7(5): 1833-1838
[65] Mitola J, Maguire G Q. Cognitive radio: Making software radios more personal [J]. IEEE Wireless Communications, 1999, 6(4): 13-18.
[66] Weiss T, Hillenbrand J, Krohn A, et al. Mutual interference in OFDM-based spectrum pooling systems [C]. IEEE Vehicular Technology Conference, Milan: IEEE, 2004:1873-1877
[67] Federal Communications Commission. Spectrum policy task force [J]. Rep. ET Docket, 2002,(02-135)
[68] Bansal G, Hossain M J, Bhargava V K. Optimal and suboptimal power allocation schemes for OFDM-based cognitive radio systems [J]. IEEE Transactions on Wireless Communications, 2008, 7(11): 4710-4718.
[69] Qin T Leung C. Fair adaptive resource allocation for multiuser OFDM cognitive radio system [C]. 2007 2nd International Conference on Communications and Networking in China, Shanghai: IEEE, 2007: 417-421
[70] Zhang Y, Leung C. Cross-layer resource allocation for mixed services in multiuser OFDM-based cognitive radio systems [J]. IEEE Transactions on Vehicular Technology, 2009, 58(8): 4605-4619
[71] Zhang Y, Leung C. Resource allocation for non-real-time services in OFDM-based cognitive radio systems [J]. IEEE Communications Letters, 2009, 13(1): 16-18
[72] Cheng P, Zhang Z, Chen H. H, et al. Optimal distributed joint frequency, rate and power allocation in cognitive OFDMA systems [J] IET Communications, 2008, 2(6): 815-826
[73]余官定,罗海燕,赵志峰等.认知正交频分复用系统的功率分配研究[J].浙江大学学报, 2009,43(4): 673-676
[74]卢前溪,王文博,傅龙等.认知无线电子载波和功率分配[J].北京邮电大学学报, 2008,31(4): 102-106
[75] Hasan Z, Hossain E, Despins C, et al. Power allocation for cognitive radios based on primary user activity in an OFDM system [C]. IEEE Global Telecommunications Conference, New Orleans: IEEE, 2008: 1-6
[76] Ngo D T, Tellambura C, Nguyen H. H. Resource allocation for OFDMA-based cognitive radio multicast networks with primary user activity consideration [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1668-1679.
[77] Wang Chen C. H, Wang C. L. Power allocation for OFDM-based cognitive radio systems under primary user activity [C]. IEEE Vehicular Technology Conference, Taiwan: IEEE, 2010: 1-5
[78] Rappaport T. S.无线通信原理与应用[M].第二版.周文安,付秀花,王志辉等译.北京:电子工业出版社,2007
[79] Clarke R. H. A statistical theory of mobile-radio reception [J]. Bell System Technical Journal, 1968, 47: 957-1000
[80]王文博,郑侃.宽带无线通信OFDM技术[M].第二版.北京:人民邮电出版社,2007
[81] Letaief K.B, Zhang Y.J. Dynamic multiuser resource allocation and adaptation for wireless systems [J] IEEE Wireless Communications, 2006, 13(4) 38-47
[82] Seong K, Mohseni M, Cioffi J M. Optimal resource allocation for OFDMA downlink systems [C]. IEEE International Symposium on Information Theory. Seattle: IEEE, 2006: 1394-1398
[83] Wong I C, Evans B L. Optimal downlink OFDMA resource allocation with linear complexity to maximize ergodic rates [J]. IEEE Transactions on Wireless Communications, 2008, 7(3): 962-971.
[84] Chung Y J, Paik C H, Kim H G. Subgradient approach for resource management in multiuser OFDM systems [C]. First International Conference on Communications and Electronics, Hanoi, Vietnam: IEEE, 2006: 203-207
[85] Krongold B S. Optimal efficient discrete resource allocation for OFDMA systems [C]. EEE Wireless Communications and Networking Conference, Las Vegas: IEEE, 2008: 1722-1727.
[86] Wunder G, Michel T. Multiuser OFDMA optimization: algorithms and duality gap analysis [C]. International ITG Workshop on Smart Antennas, Darmstadt: IEEE,2008: 233-240
[87] Qiu X, Chawla K. On the performance of adaptive modulation in cellular systems [J]. IEEE Transactions on Wireless Communications, 1999, 47(6): 884-895.
[88] Boyd S, Vandenberghe L. Convex optimization [M]. New York: Cambridge University Press, 2004: 89-90
[89] Luo Z. Q, Yu W. An introduction to convex optimization for communications and signal processing [J]. IEEE Journal on Selected Areas in Communications, 2006, 24(8): 1426-1438
[90] Palomar D. P, Chiang M. A tutorial on decomposition methods for network utility maximization [J] IEEE Journal on Selected Areas in Communications, 2006, 24(8): 1439-1451
[91] Boyd S, Mutapcic A. Subgradient method [EB/OL]. (2004). http://www.stanford.edu/class/ee392o
[92] Kim I, Lee H.L, Kim B, et al. On the use of linear programming for dynamic subchannel and bit allocation in multiuser OFDM [J]. IEEE Transactions on Vehicular Technology, 2006, 55(4): 1195-1207.
[93] Ergen M, Coleri S, Varaiya P. QoS aware adaptive resource allocation techniques for fair scheduling in OFDMA based broadband wireless access systems [J] IEEE Transactions on Broadcasting, 2003 49(4): 362-370
[94] J. G. Proakis, Digital Communications, 3rd ed[M]. Nes York: Mcgraw Hill, 1995
[95] Dorigo M, Maniezzo V, Colorni A. The ant system: Optimization by a colony of cooperating agents[J], IEEE Transactions on Systems, Man and Cybernetis- Part B, 1996, 26(1): 29-41.
[96] Dorigo M, Stutzle T,蚁群优化[M].张军,胡晓敏,罗旭耀等译.北京:清华大学出版社,2007
[97] Dorigo M, Gambardella L.M. Ant Colony System: A Cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66
[98] Liu W. M, Li S. J, Zhao F. G, et al. An Ant Colony Optimization Algorithm for the Multiple Traveling Salesmen Problem [C]. IEEE Conference on Industrial Electronics and Applications, xian: IEEE, 2009: 1524-1528
[99] Dorigo M. Ant colonies for the travelling salesman problem[J]. Bio Systems, 1996. 43: 81-73
[100] Maniezzo V, Colorni A. The ant system applied to the quadratic assignment problem[J]. IEEE Transactions on Data and Knowledge Engineering, 1999, 11(5): 769-778.
[101] Demirel N, Toksan M. D. Optimization of the quadratic assignment problem using an ant colony algorithm[J]. Applied Mathematics and Computation, 2006, 183(1): 427-435.
[102] Tseng L. Y, Chen S. C. A hybrid metaheuristic for the resource-constrained project scheduling problem[J]. European Journal of Operational Research, 2006, 175(2): 707-721.
[103] Liao C. J, Juan H. C. An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups[J]. Computers and Operations Research, 2007, 34(7): 1899-1909.
[104] Abbaspour K. C, Schulin R, Genuchten M. T. V. Estimating unsaturated soil hydraulic parameters using ant colony optimization[J]. Advances in Water Resources, 2001, 24(8): 827-841.
[105] Duan H .B, Wang D. B, Zhu J. Q, et al. Parameter identification of LuGre friction model for flight simulation servo system based on ant colony algorithm[J]. Transactions ofNanjing University of Aeronautics and Astronautics, 2004, 21(3): 179-183
[106] Han F, Shi P. F. An improved ant colony algorithm for fuzzy clustering in image segmentation[J]. Neurocomputing, 2007, 70(4-6): 665-671.
[107] Le S. H. M, Kallel A, Descombes X. Ant colony optimization for image regularization based on a nonstationary Markov modeling [J]. IEEE Transactions on Image Processing, 2007, 16(3): 865-878.
[108] K.M. Sim, W.H. Sun. Ant colony optimization for routing and load balancing: survey and new directions [J]. IEEE Transactions on systems, Man, and Cybernetics- Part A, 2003. 33(5): p. 560-572.
[109] Asadinia S, Rafsanjani M. K, Saeid A. B. A novel routing algorithm based-on ant colony in mobile ad hoc networks [C]. IEEE International Conference on Ubi-Media Computing, jinhua: IEEE, 2010:77-82
[110] Hussein S. H, Saadawi T. N, Lee M. J. Probability routing algorithm for mobile ad hoc networks resources management [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(12): 2248-2259
[111] Murty K. G. Linear Programmuing. New York: Jhon Wiley, 1983
[112] Genc V, Murphy S, Yu Y, et al. IEEE 802.16j relay-based wireless access networks: an overview [J]. IEEE Wireless Communications, 2008, 15(5):58-63
[113] Papapanagiotou I,Toumpakaris D,Lee J, et al. A survey on next generation mobile WiMAX networks: objectives, features and technical challenges [J]. IEEE Communications Surveys and Tutorials, 2009, 11(4): 3-18
[114] Wang J, Zhao Y, Korhonen T. Cross layer optimization with complete fairness constrains in OFDMA relay networks [C]. IEEE Global Telecommunications Conference, New Orleans: IEEE, 2008:1-5.
[115] Bae C, Cho D H. Fairness-aware adaptive resource allocation scheme in multihop OFDMA systems [J]. IEEE communications letters, 2007, 11(2): 134-136
[116] Meulen E C v d. Three terminal communications channels [J] Advanced appl. Probability, 1971, 3: 120-154.
[117] Cover T, Gamal A E. Capacity theorems for the relay channel [J]. IEEE Transactions on Information Theory, 1979, 25(5): 572-584.
[118] Laneman J. N, Tse D. N, Wornell G. W. Cooperative diversity in wireless networks: efficient protocols and outage behavior [J]. IEEE Transactions on Information Theory, 2004, 50(12): 3062-3082
[119] Lin Y, Wang W, Fan B, et al. Resource allocation optimization for OFDM-basedamplify-and-forward multi-relay system [C]. IEEE Wireless Communications and Networking Conference, Budapest: IEEE, 2009: 1-5.
[120] Li H, Luo H, Wang X, et al. Fairness-aware resource allocation in OFDMA cooperative relaying network [C]. IEEE International Conference on Communications, Dresden: IEEE, 2009: 1-5.
[121] You L, Song M, Song J. D. Cross-layer optimization for fairness in OFDMA cellular networks with fixed relays [C]. IEEE Global Telecommunications Conference, New Orleans: IEEE, 2008: 1-6
[122] Mo J. H, Walrand J. Fair end-to-end window-based congestion control [J]. IEEE-ACM Transactions on Networking, 2000, 8(5): 556-567
[123]金慈航. OFDM系统中基于对偶分解理论的资源分配算法[D].合肥:中国科技大学,2008
[124]孙群龙.多载波系统中的自适应资源分配研究[D].合肥:中国科技大学,2009
[125] Jain R. The art of computer systems performance analysis: techniques for experimental design, measurement, simulation and modeling [M], New York: Wiley, 1991.
[126]刘洪杰.基于认知无线电的动态频谱管理理论及相关关键技术研究[D].北京:北京邮电大学,2009
[127] FCC. Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies, NPRM & order [R]. ET Docket, FCC, 2003
[128] IEEE 802 LAN/MAN standards committee 802.22 WG [EB/OL]. http://www.ieee802.org/22/, 2010.
[129] Boroujeny F B, Kempter R. Multicarrier communication technology for spectrum sensing and communication in cognitive radios [J] IEEE Communication Magazine, 2008, 46(4):80-85
[130]谢军辉,冯平.认知无线网络中基于凸优化的功率分配研究[J].计算机应用研究,2010,27(3):1161-1166
[131]王鹏,钟晓峰,肖立民等.基于OFDM的认知无线电系统中最优功率分配,2009,49(8):1144-1147
[132] Zhang Y. H, Leung C. An efficient power-loading scheme for OFDM-based cognitive radio systems [J]. IEEE Transactions on Vehicular Technology, 59(4): 1858-1864
[133]刘鑫,谭学治.认知无线电中OFDM多用户频谱分配[J].哈尔滨工程大学学报,2009,30(10):1165-1169
[134]李维英,陈东,刑成文等.认知无线电系统中OFDM多用户资源分配算法[J].西安电子科技大学学报. 2007,34(3):368-372
[135]程鹏.基于凸优化理论的无线网络跨层资源分配研究[D].杭州:浙江大学,2008
[136]张维迎.博弈论与信息经济学[M].上海:上海人民出版社,2004
[2]陆彦辉. B3G移动通信系统中无线资源管理技术的研究[D].北京:北京邮电大学,2006
[3]刘辉,李国庆.基于OFDM的无线宽带网络设计与优化[M].任品毅译.西安:西安交通大学出版社,2008
[4] Qiu X, Chawla K. On the performance of adaptive modulation in cellular systems [J]. IEEE Transactions on Wireless Communications, 1999, 47(6): 884-895.
[5] Thomas M. C, Joy A. T. Elements of information theory [M] Wiley-Interscience, 1991
[6] Leke A, Cioffi J.M . A Maximum rate loading algorithm for discrete multitone modulation systems [C]. IEEE Global Telecommunications Conference. Phoenix: IEEE,1997: 1514–1518
[7] Yu w, Cioffi J.M. Constant power waterfilling: performance bound and low-complexity implementation [J] IEEE Transactions on Communications, 2006, 54(1): 23-28
[8] Hughes-Hartogs D. Ensemble modem structure for imperfect transmission media [P]. USA: 4 679 227 (July 1987), 4 731 816 (March 1988) and 4 833 796 (May 1989).
[9] Chow P. S, Cioffi J. M, Bingham J. A. C. Practical discrete multitone transceiver loading algorithm for data transmission over spectrally shaped channels [J]. IEEE Transactions on Communications, 1995, 45(2): 773–775
[10] Fischer R. F. H, Huber J. B. A new loading algorithm for discrete multitone transmission [C]. IEEE Global Telecommunications Conference. London: IEEE,1996: 724-728
[11] Li G, Liu H. On the optimality of the OFDMA network [J]. IEEE Communications Letters, 2005, 9(5): 438-440.
[12]汪裕民. OFDM关键技术与应用[M].北京.机械工业出版社,2007
[13] Jang J, Lee K. B, Transmit power adaptation for multiuser OFDM systems[J] IEEE Journal on Selected Areas in Communications, 2003, 21(2): 171-178.
[14] Rhee M, Cioffi J. M, Increasing in capacity of multiuser OFDM system using dynamic subchannel allocation[C] IEEE Vehicular Technology Conference, Tokyo: IEEE, 2000: 1085–1089
[15] Kim H, Han Y. A proportional fair scheduling for multicarrier transmission systems [J], IEEE communications letters, 2005, 9(3): 210-212
[16] Nguyen T. D, Han Y, A proportional fairness algorithm with QoS provision in downlink OFDMA systems [J], IEEE communications letters, 2006, 10(11): 760-762
[17] Kelly F.P. Charging and rate control for elastic traffic [J]. European Transactions on Telecommunications, 1997, 8(1): 33-37
[18] Kelly F.P, Maulloo A.K, Tan D.K.H. Rate control for communication networks: shadow prices, proportional fairness and stability [J]. Journal of The Operational Research Society, 1998, 49(3): 237-252
[19] Shen Z, Andrews J. G, Evans B. L. Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints [J]. IEEE Transactions on Wireless Communications, 2005, 4(6): 2726-2737
[20] Wong C. Y, Cheng R. S, Letaief K. B, et al. Multiuser OFDM with adaptive subcarrier, bit, and power allocation [J]. IEEE Journal on Selected Areas in Communications, 1999, 17(10): 1747-1758
[21]卫国,唐志华. OFDM系统动态资源分配研究[J].电子学报. 2007,35(6): 83-87
[22] Kivanc D, Li G, Liu H. Computationally efficient bandwidth allocation and power control for OFDMA [J]. IEEE Transactions on Wireless Communications, 2003, 2(6): 1150-1158.
[23] Kim I, Lee H.L, Kim B, et al. On the use of linear programming for dynamic subchannel and bit allocation in multiuser OFDM [C]. IEEE Global Telecommunications Conference, San Antonio: IEEE, 2001: 3648-3652
[24] Zhang X, Ye W, Feng S. L. Adaptive resource allocation for OFDMA system based on ant colony algorithm [C]. The 1st International Conference on Information Science and Engineering, Nanjing: IEEE, 2009: 2526-2529
[25] Ahmadi H, Chew Y H. Adaptive Subcarrier-and bit Allocation in Multiclass Multiuser OFDM Systems using Genetic Algorithms [C]. IEEE 20th International Symposium on Personal, Indoor and Mobile Radio Communications, Tokyo: IEEE, 2009:1883-1887
[26] Wong I.C. A unied framework for optimal resource allocation in multiuser multicarrier wireless systems [D]. Austin: The University of Texas. 2007
[27] Wong I.C, Evans B.L. Optimal OFDMA resource allocation with linear complexity to maximize ergodic weighted sum capacity [C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu: IEEE, 2007:601-604
[28] Yu W, Lui R. Dual methods for nonconvex spectrum optimization of multicarrier systems [J]. IEEE Transactions on Wireless Communications, 2006, 54(7): 1310-1322.
[29] Cendrillon R, Yu W, Moonen M, et al. Optimal multiuser spectrum balancing for digitalsubscriber lines [J]. IEEE Transactions on Communications, 2006, 54(5): 922-933.
[30] Song G. Cross-layer resource allocation and scheduling in wireless multicarrier networks [D]. Atlanta : Georgia Institute of Technology. 2005
[31] Song G, Li Y. Cross-Layer optimization for OFDM wireless networks—Part I: theoretical framework [J]. IEEE Transactions on Wireless Communications, 2005, 4(2): 614-624
[32] Song G, Li Y. Cross-Layer optimization for OFDM wireless networks—Part II: algorithm development [J]. IEEE Transactions on Wireless Communications, 2005, 4(2): 625-634
[33] Song G, Li Y. Utility-based resource allocation and scheduling in ofdm-based wireless broadband networks [J]. IEEE Communications Magazine, 2005, 43(12): 127-134
[34] Guo K, Sun L,Jia S. Utility function based fair data scheduling algorithm for OFDM wireless network [J]. Journal of Systems Engineering and Electronics, 2007,18(4): 731–738,2007
[35] Zhang Y.J, Letaief K.B. Link-adaptive largest-weighted-throughput packet scheduling for real-time traffics in wireless OFDM networks [C]. IEEE Global Telecommunications Conference, St Louis : IEEE, 2005: 2490-2494
[36] Zhang Y.J, Letaief K.B. Cross layer adaptive resource management for wireless packet networks with OFDM signaling [J] IEEE Transactions on Wireless Communications, 2006, 5(11): 3244-3254
[37] Zhang Y.J. A multi-server scheduling framework for resource allocation in wireless multi-carrier networks [J]. IEEE Transactions on Wireless Communications, 2007, 6(11):3864-3891
[38] Gesbert D, Alouini M. How much feedback is multi-user diversity really worth [C]. International Conference on Communications, Paris: IEEE, 2004: 234-238
[39] Svedman P, Wilson S, Cimini L, et al. A simplified opportunistic feedback and scheduling scheme for OFDM [C]. IEEE Vehicular Technology Conference, Milan: IEEE, 2004: 1878-1882
[40] Han Z. H, Lee Y. H. Opportunistic scheduling with partial channel information in OFDMA/FDD system [C]. IEEE Vehicular Technology Conference, Los Angeles: IEEE, 2004: 511-514
[41] Hwang G. U, Ishlzaki F. Design of a fair scheduler exploiting multiuser diversity with feedback information reduction [J]. IEEE Communications Letters, 2008, 12(2): 124-126
[42] Ko J. Y, Lee Y. H, Opportunistic transmission with partial channel information in multi-user OFDM wireless system [C]. IEEE Wireless Communications and Networking Conference, Hong Kong: IEEE, 2007:1319-1323
[43] Wong I.C, Evans B.L. Optimal resource allocation in the OFDMA downlink with imperfect channel knowledge [J]. IEEE Transactions on Communications, 2009, 57(1): 232-241
[44] Brah F, Vandendorpe L, Louveaux J. Constrained Resource Allocation in OFDMA Downlink Systems with partial CSIT [C]. IEEE International Conference on Communications, Beijing: IEEE, 2008: 4144-4148
[45] Awad M. K, Mahinthan V, Mehrjoo M, et al. A dual decomposition-based resource allocation fro OFDMA networks with imperfect CSI [J]. IEEE Transactions on Vehicular Technology, 2010, 59(5): 2394 - 2403
[46] Mokari N, Javan M. R, Navaie K. Cross-layer resource allocation in OFDMA systems for heterogeneous traffic with imperfect CSI [J]. IEEE Transactions on Vehicular Technology, 2010, 59(2): 1011 - 1017
[47] Ng D. W. K, Schober R. Cross-layer scheduling for OFDMA amplify-and-forward relay networks [C]. IEEE Vehicular Technology Conference, Anchorage: IEEE, 2009: 561-565
[48] Foschini G.J, Gans M.J. On limits of wireless communication in a fading environment when using multiple antennas [J]. Wireless Personal Communications, 1998, 6(3): 331-335
[49] Wong K, Cheng R.S.K, Letaief K.B. Adaptive antennas at the mobile and base stations in an OFDM/TDMA system [J]. IEEE Transactions on Communications, 2001, 49(1): 195-206
[50] Xiao X, Zhu G, Hu Z. Guaranteed QoS in dynamic resource allocation with adaptive beamforming for multiuser multi-antenna OFDM systems [C]. IEEE International Conference on Wireless Communications, Networking and Mobile Computing, Wuhan: IEEE, 2005: 274-277
[51] Pan C, Cai Y, Xu Y. Adaptive subcarrier and power allocation for multiuser MIMO-OFDM systems [C]. IEEE International Conference on Communications, Seoul: IEEE, 2005: 2631-2635
[52] Ji T, Zhou C, Zhou S, et al. Low complex user selection strategies for multi-user MIMO downlink scenario [C]. IEEE Wireless Communications and Networking Conference, Hong Kong: IEEE, 2007:1535-1537
[53] Zhang J, Wu Y, Xu M, et al. Linear transmitter precoding design for downlink of multiuser MIMO systems [J]. Electronics Letters, 2005,41(14):811-813
[54] Wu Y, Zhang J, Xu M, et al. Multiuser MIMO downlink precoder design based on themaximal SJNR criterion [C]. IEEE Global Telecommunications Conference, St Louis : IEEE, 2005: 2694-2698
[55] Salem, M, Adinoyi A, Rahman M, et al. An overview of radio resource management in relay-enhanced OFDMA-based networks [J]. IEEE Communications Surveys & Tutorials, 2010, 12(3): 422-438
[56] Salem M, Adinoyi A, Yanikomeroglu H, et al. Opportunities and challenges in OFDMA-based cellular relay networks: A radio resource management perspective [J]. IEEE Transactions on Vehicular Technology, 2010, 59(5): 2496-2510
[57] Nam W, Chang W, Chung S.Y, et al. Transmit optimization for relay-based cellular OFDMA systems [C] IEEE International Conference on Communications. Glasgow: IEEE, 2007: 5714-5719
[58] Li G, Liu H. Resource allocation for OFDMA relay networks with fairness constraints [J]. IEEE Journal on Selected Areas in Communications, 2006, 24(11): 2061-2068.
[59] Ng D.W.K, Schober R. Cross-layer scheduling for OFDMA amplify-and forward relay networks [J]. IEEE Transactions on Vehicular Technology, 2010, 59(3): 1443-1458.
[60] Cui Y, Lau V.K.N, Wang R. Distributive subband allocation, power and rate control for relay-assisted OFDMA cellular system with imperfect system state knowledge [J]. IEEE Transactions on Wireless Communications, 2009, 8(10): 5096-5102
[61] Ng T.C.Y, Yu W. Joint optimization of strategies and resource allocations in cooperative cellular networks [J]. IEEE Journal on Selected Areas in Communications, 2007, 25(2): 328-339.
[62] Han Z, Himsoon T, Siriwongpairat W. P, et al. Resource Allocation for Multiuser Cooperative OFDM Networks Who Helps Whom and How to Cooperate [J]. IEEE Transactions on Vehicular Technology, 2009, 58(5): 2378-2391
[63] Weng L, Murch R. D. Cooperation strategies and resource allocations in multiuser OFDMA systems [J]. IEEE Transactions on Vehicular Technology, 2009, 58(5): 2331-2342
[64] Kim S. J, Wang X, Madihian M. Optimal resource allocation in multi-hop OFDMA wireless networks with cooperative relay [J]. IEEE Transactions on Wireless Communications, 2008, 7(5): 1833-1838
[65] Mitola J, Maguire G Q. Cognitive radio: Making software radios more personal [J]. IEEE Wireless Communications, 1999, 6(4): 13-18.
[66] Weiss T, Hillenbrand J, Krohn A, et al. Mutual interference in OFDM-based spectrum pooling systems [C]. IEEE Vehicular Technology Conference, Milan: IEEE, 2004:1873-1877
[67] Federal Communications Commission. Spectrum policy task force [J]. Rep. ET Docket, 2002,(02-135)
[68] Bansal G, Hossain M J, Bhargava V K. Optimal and suboptimal power allocation schemes for OFDM-based cognitive radio systems [J]. IEEE Transactions on Wireless Communications, 2008, 7(11): 4710-4718.
[69] Qin T Leung C. Fair adaptive resource allocation for multiuser OFDM cognitive radio system [C]. 2007 2nd International Conference on Communications and Networking in China, Shanghai: IEEE, 2007: 417-421
[70] Zhang Y, Leung C. Cross-layer resource allocation for mixed services in multiuser OFDM-based cognitive radio systems [J]. IEEE Transactions on Vehicular Technology, 2009, 58(8): 4605-4619
[71] Zhang Y, Leung C. Resource allocation for non-real-time services in OFDM-based cognitive radio systems [J]. IEEE Communications Letters, 2009, 13(1): 16-18
[72] Cheng P, Zhang Z, Chen H. H, et al. Optimal distributed joint frequency, rate and power allocation in cognitive OFDMA systems [J] IET Communications, 2008, 2(6): 815-826
[73]余官定,罗海燕,赵志峰等.认知正交频分复用系统的功率分配研究[J].浙江大学学报, 2009,43(4): 673-676
[74]卢前溪,王文博,傅龙等.认知无线电子载波和功率分配[J].北京邮电大学学报, 2008,31(4): 102-106
[75] Hasan Z, Hossain E, Despins C, et al. Power allocation for cognitive radios based on primary user activity in an OFDM system [C]. IEEE Global Telecommunications Conference, New Orleans: IEEE, 2008: 1-6
[76] Ngo D T, Tellambura C, Nguyen H. H. Resource allocation for OFDMA-based cognitive radio multicast networks with primary user activity consideration [J]. IEEE Transactions on Vehicular Technology, 2010, 59(4): 1668-1679.
[77] Wang Chen C. H, Wang C. L. Power allocation for OFDM-based cognitive radio systems under primary user activity [C]. IEEE Vehicular Technology Conference, Taiwan: IEEE, 2010: 1-5
[78] Rappaport T. S.无线通信原理与应用[M].第二版.周文安,付秀花,王志辉等译.北京:电子工业出版社,2007
[79] Clarke R. H. A statistical theory of mobile-radio reception [J]. Bell System Technical Journal, 1968, 47: 957-1000
[80]王文博,郑侃.宽带无线通信OFDM技术[M].第二版.北京:人民邮电出版社,2007
[81] Letaief K.B, Zhang Y.J. Dynamic multiuser resource allocation and adaptation for wireless systems [J] IEEE Wireless Communications, 2006, 13(4) 38-47
[82] Seong K, Mohseni M, Cioffi J M. Optimal resource allocation for OFDMA downlink systems [C]. IEEE International Symposium on Information Theory. Seattle: IEEE, 2006: 1394-1398
[83] Wong I C, Evans B L. Optimal downlink OFDMA resource allocation with linear complexity to maximize ergodic rates [J]. IEEE Transactions on Wireless Communications, 2008, 7(3): 962-971.
[84] Chung Y J, Paik C H, Kim H G. Subgradient approach for resource management in multiuser OFDM systems [C]. First International Conference on Communications and Electronics, Hanoi, Vietnam: IEEE, 2006: 203-207
[85] Krongold B S. Optimal efficient discrete resource allocation for OFDMA systems [C]. EEE Wireless Communications and Networking Conference, Las Vegas: IEEE, 2008: 1722-1727.
[86] Wunder G, Michel T. Multiuser OFDMA optimization: algorithms and duality gap analysis [C]. International ITG Workshop on Smart Antennas, Darmstadt: IEEE,2008: 233-240
[87] Qiu X, Chawla K. On the performance of adaptive modulation in cellular systems [J]. IEEE Transactions on Wireless Communications, 1999, 47(6): 884-895.
[88] Boyd S, Vandenberghe L. Convex optimization [M]. New York: Cambridge University Press, 2004: 89-90
[89] Luo Z. Q, Yu W. An introduction to convex optimization for communications and signal processing [J]. IEEE Journal on Selected Areas in Communications, 2006, 24(8): 1426-1438
[90] Palomar D. P, Chiang M. A tutorial on decomposition methods for network utility maximization [J] IEEE Journal on Selected Areas in Communications, 2006, 24(8): 1439-1451
[91] Boyd S, Mutapcic A. Subgradient method [EB/OL]. (2004). http://www.stanford.edu/class/ee392o
[92] Kim I, Lee H.L, Kim B, et al. On the use of linear programming for dynamic subchannel and bit allocation in multiuser OFDM [J]. IEEE Transactions on Vehicular Technology, 2006, 55(4): 1195-1207.
[93] Ergen M, Coleri S, Varaiya P. QoS aware adaptive resource allocation techniques for fair scheduling in OFDMA based broadband wireless access systems [J] IEEE Transactions on Broadcasting, 2003 49(4): 362-370
[94] J. G. Proakis, Digital Communications, 3rd ed[M]. Nes York: Mcgraw Hill, 1995
[95] Dorigo M, Maniezzo V, Colorni A. The ant system: Optimization by a colony of cooperating agents[J], IEEE Transactions on Systems, Man and Cybernetis- Part B, 1996, 26(1): 29-41.
[96] Dorigo M, Stutzle T,蚁群优化[M].张军,胡晓敏,罗旭耀等译.北京:清华大学出版社,2007
[97] Dorigo M, Gambardella L.M. Ant Colony System: A Cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 53-66
[98] Liu W. M, Li S. J, Zhao F. G, et al. An Ant Colony Optimization Algorithm for the Multiple Traveling Salesmen Problem [C]. IEEE Conference on Industrial Electronics and Applications, xian: IEEE, 2009: 1524-1528
[99] Dorigo M. Ant colonies for the travelling salesman problem[J]. Bio Systems, 1996. 43: 81-73
[100] Maniezzo V, Colorni A. The ant system applied to the quadratic assignment problem[J]. IEEE Transactions on Data and Knowledge Engineering, 1999, 11(5): 769-778.
[101] Demirel N, Toksan M. D. Optimization of the quadratic assignment problem using an ant colony algorithm[J]. Applied Mathematics and Computation, 2006, 183(1): 427-435.
[102] Tseng L. Y, Chen S. C. A hybrid metaheuristic for the resource-constrained project scheduling problem[J]. European Journal of Operational Research, 2006, 175(2): 707-721.
[103] Liao C. J, Juan H. C. An ant colony optimization for single-machine tardiness scheduling with sequence-dependent setups[J]. Computers and Operations Research, 2007, 34(7): 1899-1909.
[104] Abbaspour K. C, Schulin R, Genuchten M. T. V. Estimating unsaturated soil hydraulic parameters using ant colony optimization[J]. Advances in Water Resources, 2001, 24(8): 827-841.
[105] Duan H .B, Wang D. B, Zhu J. Q, et al. Parameter identification of LuGre friction model for flight simulation servo system based on ant colony algorithm[J]. Transactions ofNanjing University of Aeronautics and Astronautics, 2004, 21(3): 179-183
[106] Han F, Shi P. F. An improved ant colony algorithm for fuzzy clustering in image segmentation[J]. Neurocomputing, 2007, 70(4-6): 665-671.
[107] Le S. H. M, Kallel A, Descombes X. Ant colony optimization for image regularization based on a nonstationary Markov modeling [J]. IEEE Transactions on Image Processing, 2007, 16(3): 865-878.
[108] K.M. Sim, W.H. Sun. Ant colony optimization for routing and load balancing: survey and new directions [J]. IEEE Transactions on systems, Man, and Cybernetics- Part A, 2003. 33(5): p. 560-572.
[109] Asadinia S, Rafsanjani M. K, Saeid A. B. A novel routing algorithm based-on ant colony in mobile ad hoc networks [C]. IEEE International Conference on Ubi-Media Computing, jinhua: IEEE, 2010:77-82
[110] Hussein S. H, Saadawi T. N, Lee M. J. Probability routing algorithm for mobile ad hoc networks resources management [J]. IEEE Journal on Selected Areas in Communications, 2005, 23(12): 2248-2259
[111] Murty K. G. Linear Programmuing. New York: Jhon Wiley, 1983
[112] Genc V, Murphy S, Yu Y, et al. IEEE 802.16j relay-based wireless access networks: an overview [J]. IEEE Wireless Communications, 2008, 15(5):58-63
[113] Papapanagiotou I,Toumpakaris D,Lee J, et al. A survey on next generation mobile WiMAX networks: objectives, features and technical challenges [J]. IEEE Communications Surveys and Tutorials, 2009, 11(4): 3-18
[114] Wang J, Zhao Y, Korhonen T. Cross layer optimization with complete fairness constrains in OFDMA relay networks [C]. IEEE Global Telecommunications Conference, New Orleans: IEEE, 2008:1-5.
[115] Bae C, Cho D H. Fairness-aware adaptive resource allocation scheme in multihop OFDMA systems [J]. IEEE communications letters, 2007, 11(2): 134-136
[116] Meulen E C v d. Three terminal communications channels [J] Advanced appl. Probability, 1971, 3: 120-154.
[117] Cover T, Gamal A E. Capacity theorems for the relay channel [J]. IEEE Transactions on Information Theory, 1979, 25(5): 572-584.
[118] Laneman J. N, Tse D. N, Wornell G. W. Cooperative diversity in wireless networks: efficient protocols and outage behavior [J]. IEEE Transactions on Information Theory, 2004, 50(12): 3062-3082
[119] Lin Y, Wang W, Fan B, et al. Resource allocation optimization for OFDM-basedamplify-and-forward multi-relay system [C]. IEEE Wireless Communications and Networking Conference, Budapest: IEEE, 2009: 1-5.
[120] Li H, Luo H, Wang X, et al. Fairness-aware resource allocation in OFDMA cooperative relaying network [C]. IEEE International Conference on Communications, Dresden: IEEE, 2009: 1-5.
[121] You L, Song M, Song J. D. Cross-layer optimization for fairness in OFDMA cellular networks with fixed relays [C]. IEEE Global Telecommunications Conference, New Orleans: IEEE, 2008: 1-6
[122] Mo J. H, Walrand J. Fair end-to-end window-based congestion control [J]. IEEE-ACM Transactions on Networking, 2000, 8(5): 556-567
[123]金慈航. OFDM系统中基于对偶分解理论的资源分配算法[D].合肥:中国科技大学,2008
[124]孙群龙.多载波系统中的自适应资源分配研究[D].合肥:中国科技大学,2009
[125] Jain R. The art of computer systems performance analysis: techniques for experimental design, measurement, simulation and modeling [M], New York: Wiley, 1991.
[126]刘洪杰.基于认知无线电的动态频谱管理理论及相关关键技术研究[D].北京:北京邮电大学,2009
[127] FCC. Facilitating opportunities for flexible, efficient, and reliable spectrum use employing cognitive radio technologies, NPRM & order [R]. ET Docket, FCC, 2003
[128] IEEE 802 LAN/MAN standards committee 802.22 WG [EB/OL]. http://www.ieee802.org/22/, 2010.
[129] Boroujeny F B, Kempter R. Multicarrier communication technology for spectrum sensing and communication in cognitive radios [J] IEEE Communication Magazine, 2008, 46(4):80-85
[130]谢军辉,冯平.认知无线网络中基于凸优化的功率分配研究[J].计算机应用研究,2010,27(3):1161-1166
[131]王鹏,钟晓峰,肖立民等.基于OFDM的认知无线电系统中最优功率分配,2009,49(8):1144-1147
[132] Zhang Y. H, Leung C. An efficient power-loading scheme for OFDM-based cognitive radio systems [J]. IEEE Transactions on Vehicular Technology, 59(4): 1858-1864
[133]刘鑫,谭学治.认知无线电中OFDM多用户频谱分配[J].哈尔滨工程大学学报,2009,30(10):1165-1169
[134]李维英,陈东,刑成文等.认知无线电系统中OFDM多用户资源分配算法[J].西安电子科技大学学报. 2007,34(3):368-372
[135]程鹏.基于凸优化理论的无线网络跨层资源分配研究[D].杭州:浙江大学,2008
[136]张维迎.博弈论与信息经济学[M].上海:上海人民出版社,2004