异构中继协作网络的资源分配研究
|
摘要
现有移动通信网（2G/3G）、无线局域网（WLAN）、Ad-hoc网络、卫星网络等能够为用户提供多种多样的服务，但要同时满足多样化的服务质量要求，还需要充分利用不同网络间的互补特性。因此，异构网络融合是未来无线通信网络发展的必然趋势。而中继协作技术能够有效改善网络覆盖和可靠性，是未来异构无线通信网络的关键技术之一。本论文选择了异构中继协作网络的传输机制设计及资源优化管理问题开展研究，探索了未来移动通信网络的可能工作模式。
针对长期难以解决的中继协作传输速率可能会低于直传链路容量的问题，提出了一种多载波中继协作译码转发传输机制，将宽带中继协作可达速率提升到可确保不低于直传链路容量的水平。该机制通过节点间传输时间复用和多载波联合编码，充分发挥了中继协作的转发距离优势，证明了中继协作除了可以改善覆盖和可靠性，还可以提高频谱效率，为中继协作技术的速率优势提供了理论依据。
针对干扰信号动态变化的认知中继协作网络的实际场景，建立了干扰预测模型，提出了复杂度低、实时性强的MAC层频谱接入和物理层资源分配的联合优化算法，可实现大部分优化运算的离线操作，并大幅降低源节点和中继节点的计算负荷，保证了认知协作传输控制的实时性和最优性。
针对频谱检测存在误差的认知中继协作网络的实际场景，首次发现并证明了检测误差概率函数（在通信、雷达领域广泛使用的特殊函数广义马库姆Q函数）为对数凹函数的数学性质，填补了对检测概率函数的解析性质认识的不足。该性质不仅从根本上解决了检测参数与传输参数的联合优化问题，提高了认知协作传输的鲁棒性，还可以在涉及该函数的优化问题求解的收敛性、最优性及实现算法等方面发挥重要作用，在常见指标如误码率、中断概率和检测概率的分析计算中也可以提供更精准和便利的上下界分析。
针对大规模异构中继协作蜂窝网络所需要迫切解决的分布式控制问题，提出一种分布式联合优化理论框架，设计了功率分配、信道分配和中继链路选择的低复杂度、分布式联合优化算法，解决了集中式控制信息交互数据量大、实时性差和灵活性差问题，首次在不需要对优化问题进行近似的情况下，严格证明了中继协作网络中分布式资源分配的收敛性和最优性，为实际中继协作网络的分布式无线资源管理奠定了理论基础。
Nowadays, the subscribers have access to all kinds of wireless services via the2Gand3G mobile communication networks, wireless local area networks (WLAN), Ad-hocnetworks, satellite networks, and etc. However, it is still quite difcult to satisfy variouskinds of wireless services simultaneously. The combination of heterogeneous wirelessnetworks have become an important technology to conquer these difculties. On the oth-er hand, cooperative relaying can enhance network coverage and reliability efectively,which has been widely recognized as one key method of the next generation heteroge-neous wireless networks. This thesis investigates wireless transmission technologies andresource managements of heterogeneous cooperative relay networks, and explores efec-tive work mode of future wireless networks.
An important problem, which has not been resolved for a long time, is that theachievable data rate of cooperative relaying may be smaller than the capacity of source-destination direct transmissions in some scenarios. For this, I propose a multi-carrierdecode-and-forward (DF) relay strategy, which makes sure that the achievable rate ofwide-band cooperative relaying is no-smaller than the capacity of source-destination di-rect transmissions. By making use of transmission time multiplexing between the sourceand relay nodes and channel coding across the sub-carriers, the proposed relay strategycan fully exploit the benefits of shorter forwarding transmission distance. This strategydemonstrates that cooperative relaying can not only enhance the coverage and reliabilityof wireless networks, but also improve the spectrum efciency of wireless networks.
Since the interference signal varies dynamically in realistic heterogeneous coopera-tive relay networks, we establish a model for interference prediction. The spectrum accessand resource allocation strategy of cooperative relay network is optimized to mitigate themutual interference among the wireless networks. Low complexity, real-time spectrumaccess and resource allocation algorithm with negligible sensing-transmission time de-lay is developed. Most computations of this algorithm are accomplished of-line, leavingonly simple tasks for real-time computations. The computational load of the source andrelay nodes is reduced dramatically. Real-time and optimal control of cognitive relaytransmissions can be achieved by this strategy.
Then, the robustness of cognitive relaying with respect to spectrum sensing errorsis investigated. I discover and rigorous prove that the spectrum sensing error probabili-ty (the generalized Marcum Q function, which has been widely used in wireless com-munication and radar systems) is a log-concave function, which extends the analyticalproperties of spectrum sensing error probability. This novel log-concave property notonly resolves the joint optimization of sensing parameters and transmission parametersto improve the robustness of cognitive relaying, but also play an important role in oth-er optimization problems involving the spectrum sensing error probability. Some otherapplications of this log-concave property includes tight bounds and other analytical s-tudy of transmission error probabilities, outage probabilities and detection probabilitiesin wireless communications and radar communications.
Finally, distributed resource allocation algorithm is proposed for heterogeneous co-operative relay cellular networks with multiple sources, multiple relays and multiple des-tinations. The time delay and large amount of data exchange of centralized resourceallocations can be avoided by this algorithm. The convergence and optimality of this dis-tributed resource allocation algorithm is proved rigorously without any approximation ofthe original resource allocation problem.
针对长期难以解决的中继协作传输速率可能会低于直传链路容量的问题，提出了一种多载波中继协作译码转发传输机制，将宽带中继协作可达速率提升到可确保不低于直传链路容量的水平。该机制通过节点间传输时间复用和多载波联合编码，充分发挥了中继协作的转发距离优势，证明了中继协作除了可以改善覆盖和可靠性，还可以提高频谱效率，为中继协作技术的速率优势提供了理论依据。
针对干扰信号动态变化的认知中继协作网络的实际场景，建立了干扰预测模型，提出了复杂度低、实时性强的MAC层频谱接入和物理层资源分配的联合优化算法，可实现大部分优化运算的离线操作，并大幅降低源节点和中继节点的计算负荷，保证了认知协作传输控制的实时性和最优性。
针对频谱检测存在误差的认知中继协作网络的实际场景，首次发现并证明了检测误差概率函数（在通信、雷达领域广泛使用的特殊函数广义马库姆Q函数）为对数凹函数的数学性质，填补了对检测概率函数的解析性质认识的不足。该性质不仅从根本上解决了检测参数与传输参数的联合优化问题，提高了认知协作传输的鲁棒性，还可以在涉及该函数的优化问题求解的收敛性、最优性及实现算法等方面发挥重要作用，在常见指标如误码率、中断概率和检测概率的分析计算中也可以提供更精准和便利的上下界分析。
针对大规模异构中继协作蜂窝网络所需要迫切解决的分布式控制问题，提出一种分布式联合优化理论框架，设计了功率分配、信道分配和中继链路选择的低复杂度、分布式联合优化算法，解决了集中式控制信息交互数据量大、实时性差和灵活性差问题，首次在不需要对优化问题进行近似的情况下，严格证明了中继协作网络中分布式资源分配的收敛性和最优性，为实际中继协作网络的分布式无线资源管理奠定了理论基础。
Nowadays, the subscribers have access to all kinds of wireless services via the2Gand3G mobile communication networks, wireless local area networks (WLAN), Ad-hocnetworks, satellite networks, and etc. However, it is still quite difcult to satisfy variouskinds of wireless services simultaneously. The combination of heterogeneous wirelessnetworks have become an important technology to conquer these difculties. On the oth-er hand, cooperative relaying can enhance network coverage and reliability efectively,which has been widely recognized as one key method of the next generation heteroge-neous wireless networks. This thesis investigates wireless transmission technologies andresource managements of heterogeneous cooperative relay networks, and explores efec-tive work mode of future wireless networks.
An important problem, which has not been resolved for a long time, is that theachievable data rate of cooperative relaying may be smaller than the capacity of source-destination direct transmissions in some scenarios. For this, I propose a multi-carrierdecode-and-forward (DF) relay strategy, which makes sure that the achievable rate ofwide-band cooperative relaying is no-smaller than the capacity of source-destination di-rect transmissions. By making use of transmission time multiplexing between the sourceand relay nodes and channel coding across the sub-carriers, the proposed relay strategycan fully exploit the benefits of shorter forwarding transmission distance. This strategydemonstrates that cooperative relaying can not only enhance the coverage and reliabilityof wireless networks, but also improve the spectrum efciency of wireless networks.
Since the interference signal varies dynamically in realistic heterogeneous coopera-tive relay networks, we establish a model for interference prediction. The spectrum accessand resource allocation strategy of cooperative relay network is optimized to mitigate themutual interference among the wireless networks. Low complexity, real-time spectrumaccess and resource allocation algorithm with negligible sensing-transmission time de-lay is developed. Most computations of this algorithm are accomplished of-line, leavingonly simple tasks for real-time computations. The computational load of the source andrelay nodes is reduced dramatically. Real-time and optimal control of cognitive relaytransmissions can be achieved by this strategy.
Then, the robustness of cognitive relaying with respect to spectrum sensing errorsis investigated. I discover and rigorous prove that the spectrum sensing error probabili-ty (the generalized Marcum Q function, which has been widely used in wireless com-munication and radar systems) is a log-concave function, which extends the analyticalproperties of spectrum sensing error probability. This novel log-concave property notonly resolves the joint optimization of sensing parameters and transmission parametersto improve the robustness of cognitive relaying, but also play an important role in oth-er optimization problems involving the spectrum sensing error probability. Some otherapplications of this log-concave property includes tight bounds and other analytical s-tudy of transmission error probabilities, outage probabilities and detection probabilitiesin wireless communications and radar communications.
Finally, distributed resource allocation algorithm is proposed for heterogeneous co-operative relay cellular networks with multiple sources, multiple relays and multiple des-tinations. The time delay and large amount of data exchange of centralized resourceallocations can be avoided by this algorithm. The convergence and optimality of this dis-tributed resource allocation algorithm is proved rigorously without any approximation ofthe original resource allocation problem.
引文
[1]曹志刚,钱亚生.现代通信原理.北京:清华大学出版社,1992.
[2]高锟.新通信网络的实现.电信科学,1990,(4):1–2.
[3] http://baike.baidu.com/view/21461.htm.
[4]代琳.分布式无线通信系统的容量和关键技术研究[博士学位论文].北京:清华大学,2002.
[5] Frenkiel R. Creating cellular: A history of the AMPS project (1971-1983). IEEE Commun.Mag.,2010,48(9):14–24.
[6]李军.异构无线网络融合理论与技术实现.北京:电子工业出版社,2008.
[7]3GPP. http://www.3gpp.org/HSPA.
[8] http://baike.baidu.com/view/1359910.html.
[9] ITU-R M2134. Requirements related to technical performance for IMT-Advanced radio inter-face(s).
[10] Hashimoto A, Yoshino H, Atarashi H. Roadmap of IMT-Advanced development. IEEE Mi-crow. Mag.,2008,9(4):80–88.
[11]3GPP. http://www.3gpp.org/LTE-Advanced.
[12] IEEE. http://wirelessman.org/tgm/.
[13] ITU. http://www.itu.int/net/pressofce/press releases/2010/48.aspx.
[14]冯伟.非理想分布式无线通信系统中的资源分配与优化[博士学位论文].北京:清华大学,2010.
[15] Berrou C, Glavieux A, Thitimajshima P. Near shannon limit error-correcting coding and de-coding: Turbo-codes.1. Proceedings of IEEE International Conference on Communications,(IEEE ICC1993), volume2,1993.1064–1070vol.2.
[16] Gallager R G. Low-density parity-check codes. IRE Trans. Inf. Theory,1962,8(1):21–28.
[17] Weinstein S, Ebert P. Data transmission by frequency-division multiplexing using the discretefourier transform. IEEE Transactions on Communications,1971,19(5):628–634.
[18] Foschini G J. Layered space-time architecture for wireless communications in a fading envi-ronment when using multi-element antennas. Bell Labs Technical Journal,1996,1(2):41–59.
[19] Telatar E. Capacity of multi-antenna gaussian channels. European Trans Telecommunication,1999,10(6):585–596.
[20] Alamouti S M. A simple transmit diversity technique for wireless communications. IEEEJournal on Selected Areas in Coomunications,1998,16(8):1451–1458.
[21] Pabst R, Walke B H, Schultz D C, et al. Relay-based deployment concepts for wireless andmobile broadband radio. IEEE Commun. Mag.,2004,42(9):80–89.
[22] Peters S, Heath R. The future of WiMAX: Multihop relaying with ieee802.16j. IEEE Com-mun. Mag.,2009,47(1):104–111.
[23] Soldani D, Dixit S. Wireless relays for broadband access. IEEE Commun. Mag.,2008,46(3):58–66.
[24] Femto Forum. http://www.femtoforum.com.
[25] Chandrasekhar V, Andrews J G, Gatherer A. Femtocell networks: A survey. IEEE Commun.Mag.,2008,46(9):59–67.
[26] Claussen H, Ho L T W, Samuel L G. An overview of the femtocell concept. Bell Lab. Tech.J.,2008,13:221–245.
[27] Cover T, Gamal A. Capacity theorems for the relay channel. IEEE Trans. Inf. Theory,1979,IT-25(5):572–584.
[28] Laneman J, Tse D, Wornell G. Cooperative diversity in wireless networks: Efcient protocolsand outage behavior. IEEE Trans. Inf. Theory,2004,50(12):3062–3080.
[29] Kramer G, Gastpar M, Gupta P. Cooperative strategies and capacity theorems for relay net-works. IEEE Trans. Inf. Theory,2005,51(9):3037–3063.
[30] Chakrabarti A, Baynast A, Sabharwal A, et al. Low density parity check codes for the relaychannel. IEEE J. Sel. Areas Commun.,2007,25(2):280–291.
[31] Salem M, Adinoyi A, Rahman M, et al. An overview of radio resource management in relay-enhanced OFDMA-based networks. IEEE Commun. Surveys Tuts.,2010,12(3):422–438.
[32] Rankov B, Wittneben A. Spectral efcient protocols for half-duplex fading relay channels.IEEE J. Sel. Areas Commun.,2007,25(2):379–389.
[33] Avestimehr A, Tse D. Outage capacity of the fading relay channel in the low-SNR regime.IEEE Trans. Inf. Theory,2007,53(4):1401–1415.
[34] Landstro¨m S, Furuska′r A, Johansson K, et al. Heterogeneous networks-increasing cellularcapacity. Ericsson Review,2011,(1):4–9.
[35] Bertsekas D P, Tsitsiklis J N. Parallel and Distributed Computation: Numerical Methods. En-glewood Clifs, NJ: Prentice-Hall,1989.
[36] Johansson B, Soldati P, Johansson M. Mathematical decomposition techniques for distributedcross-layer optimization of data networks. IEEE J. Sel. Areas Commun.,2006,24(8):1535–1547.
[37] Palomar D, Chiang M. A tutorial on decomposition methods for network utility maximization.IEEE J. Sel. Areas Commun.,2006,24(8):1439–1451.
[38] Lin X, Shrof N, Srikant R. A tutorial on cross-layer optimization in wireless networks. IEEEJ. Sel. Areas Commun.,2006,24(8):1452–1463.
[39] Chiang M, Low S, Calderbank A, et al. Layering as optimization decomposition: A mathemat-ical theory of network architectures. Proc. IEEE,2007,95(1):255–312.
[40] Palomar D, Chiang M. Alternative distributed algorithms for network utility maximization:Framework and applications. IEEE Trans. Auto. Control,2007,52(12):2254–2269.
[41] Lin X, Shrof N, Srikant R. On the connection-level stability of congestion-controlled commu-nication networks. IEEE Trans. Inf. Theory,2008,54(5):2317–2338.
[42] Lin X, Shrof N. Utility maximization for communication networks with multipath routing.IEEE Trans. Auto. Control,2006,51(5):766–781.
[43] Zhao Q, Sadler B M. A survey of dynamic spectrum access: Signal processing, networking,and regulatory policy. IEEE Signal Process. Mag.,2007,24(5):79–89.
[44] Peha J. Sharing spectrum through spectrum policy reform and cognitive radio. Proc. IEEE,2009,97(4):708–719.
[45] IEEE. http://www.wirelessman.org/le/.
[46] IEEE. www.ieee802.org/22/.
[47] IEEE. http://grouper.ieee.org/groups/802/11/Reports/tgy update.htm.
[48] Liu B, Chen B, Blum R S. Minimum error probability cooperative relay design. IEEE Trans.Signal Process.,2007,55(2):656–664.
[49] Ng T C Y, Yu W. Joint optimization of relay strategies and resource allocations in cooperativecellular networks. IEEE J. Sel. Areas Commun.,2007,25(2):328–339.
[50] Liu K J R, Sadek A K, Su W, et al. Cooperative Communications and Networking. Cambridge:Cambridge University Press,2009.
[51] Gu¨ndu¨z D, Erkip E, Goldsmith A, et al. Cooperative relaying in sensor networks. Proceedingsof Proceedings of the Fifth International Conference on Cognitive Radio Oriented WirelessNetworks Communications (CROWNCOM2010),2010.1–5.
[52] Scaglione A, Goeckel D, Laneman J. Cooperative communications in mobile ad hoc networks:Rethinking the link abstraction. IEEE Signal Process. Mag.,2006,23(5):18–29.
[53] Lo A, Niemegeers I. Multi-hop relay architectures for3GPP LTE-advanced. Proceedingsof Proceedings of IEEE9th Malaysia International Conference on Communications (MICC2009),2009.123–127.
[54] Warty C. Cooperative communication for multiple satellite network. Proceedings of Proceed-ings of IEEE Aerospace Conference,2010.1–7.
[55] H st-Madsen A, Zhang J. Capacity bounds and power allocation for wireless relay channels.IEEE Trans. Inf. Theory,2005,51(6):2020–2040.
[56] Loa K, Wu C C, Sheu S T, et al. Imt-advanced relay standards. IEEE Commun. Mag.,2010,48(8):40–48.
[57] Li G, Liu H. Resource allocation for OFDMA relay networks with fairness constraints. IEEEJ. Sel. Areas Commun.,2006,24(11):2061–2069.
[58] Jitvanichphaibool K, Zhang R, Liang Y C. Optimal resource allocation for two-way relay-assisted OFDMA. IEEE Trans. Veh. Technol.,2009,58(7):3311–3321.
[59] Cui Y, Lau V, Wang R. Distributive subband allocation, power and rate control for relay-assisted OFDMA cellular system with imperfect system state knowledge. IEEE Trans. WirelessCommun.,2009,8(10):5096–5102.
[60] Nosratinia A, Hunter T, Hedayat A. Cooperative communication in wireless networks. IEEECommun. Mag.,2004,42(10):74–80.
[61] Liang Y, Veeravalli V V. Gaussian orthogonal relay channels: optimal resource allocation andcapacity. IEEE Trans. Inf. Theory,2005,51(9):3284–3289.
[62] Liang Y, Veeravalli V V, Poor H V. Resource allocation for wireless fading relay channel:Max-min solution. IEEE Trans. Inf. Theory,2007,53(10):3432–3453.
[63] Serbetli S, Yener A. Relay assisted F/TDMA ad hoc networks: node classification, powerallocation and relaying strategies. IEEE Trans. Commun.,2008,56(6):937–947.
[64] IEEE. IEEE802.16m System Requirements: IEEE802.16m-07/002r10.
[65] Boyd S, Vandenberghe L. Convex Optimization. Cambridge, UK: Cambridge University Press,2004.
[66] Gamal A E, Kim Y H. Lecture Notes on Network Information Theory.2010.
[67] Gamal A, Aref M. The capacity of the semideterministic relay channel. IEEE Trans. Inf.Theory,1982, IT-28(3):536–536.
[68] Chakrabarti A, Erkip E, Sabharwal A, et al. Code designs for cooperative communication.IEEE Signal Process. Mag.,2007,24(5):16–26.
[69] Li Y. Distributed coding for cooperative wireless networks: An overview and recent advances.IEEE Commun. Mag.,2009,47(8):71–77.
[70] Li C, Yue G, Wang X, et al. LDPC code design for half-duplex cooperative relay. IEEE Trans.Wireless Commun.,2008,7(11):4558–4567.
[71] Wang B, Zhang J, H st-Madsen A. On the capacity of MIMO relay channels. IEEE Trans. Inf.Theory,2005,51(1):29–43.
[72] Tang J, Zhang X. Cross-layer resource allocation over wireless relay networks for quality ofservice provisioning. IEEE J. Sel. Areas Commun.,2007,25(5):645–656.
[73] Farhadi G, Beaulieu N. On the ergodic capacity of wireless relaying systems over Rayleighfading channels. IEEE Trans. Wireless Commun.,2008,7(11):4462–4467.
[74] Simon M K, Alouini M S. Digital Communication over Fading Channels: A Unified Approachto Performance Analysis. New York: Wiley,2000.
[75] Nakagami M. The m distribution: A general formula of intensity distribution of rapid fad-ing. In: Hofman W G,(eds.). Proceedings of Statistical Methods in Radio Wave Propagation.Oxford: Pergamon Press,1960:3–36.
[76] Gradshteyn I S, Ryzhik I. Table of Integrals, Series and Products. New York: Academic Press,2000.
[77] Moschopoulos P G. The distribution of the sum of independent gamma random variables. Ann.Inst. Statist. Math.(Part A),1985,37:541–543.
[78] Zhong C, Wong K K, Jin S. Capacity bounds for MIMO nakagami-m fading channels. IEEETrans. Signal Process.,2009,57(9):3613–3623.
[79] Zhao Q, Tong L, Swami A, et al. Decentralized cognitive MAC for opportunistic spectrumaccess in ad hoc networks: A POMDP framework. IEEE J. Sel. Areas Commun.,2007,25(3):589–600.
[80] Su H, Zhang X. Cross-layer based opportunistic MAC protocols for QoS provisionings overcognitive radio wireless networks. IEEE J. Sel. Areas Commun.,2008,26(1):118–129.
[81] Geirhofer S, Tong L, Sadler B M. Dynamic spectrum access in the time domain: Modeling andexploiting whitespace. IEEE Commun. Mag.,2007,45(5):66–72.
[82] Geirhofer S, Tong L, Sadler B M. Cognitive medium access: Constraining interference basedon experimental models. IEEE J. Sel. Areas Commun.,2008,26(1):95–105.
[83] Zhao Q, Geirhofer S, Tong L, et al. Opportunistic spectrum access via periodic channel sensing.IEEE Trans. Signal Process.,2008,56(2):785–796.
[84] Geirhofer S, Sun J Z, Tong L, et al. Cognitive frequency hopping based on interference predic-tion: Theory and experimental results. ACM SIGMOBILE Mob. Comput.and Commun. Rev.,2009,13(2):49–61.
[85] Li X, Zhao Q C, Guan X, et al. Optimal cognitive access of Markovian channels under tightcollision constraints. IEEE J. Sel. Areas Commun.,2011. To appear.
[86] Lai L, El Gamal H, Jiang H, et al. Cognitive medium access: Exploration, exploitation, andcompetition. IEEE Trans. Mob. Comput.,2011,10(2):239–253.
[87] Wang P, Zhao M, Xiao L, et al. Power allocation in OFDM-based cognitive radio systems.Proceedings of Proceedings of Global Telecommunications Conference (IEEE GLOBECOM2007),2007.4061–4065.
[88] Zhang R, Liang Y C. Exploiting multi-antennas for opportunistic spectrum sharing in cognitiveradio networks. IEEE J. Sel. Topics Signal Process.,2008,2(1):88–102.
[89] Marques A, Wang X, Giannakis G. Dynamic resource management for cognitive radios usinglimited-rate feedback. IEEE Trans. Signal Process.,2009,57(9):3651–3666.
[90] Zhang R, Liang Y, Cui S. Dynamic resource allocation in cognitive radio networks. IEEESignal Process. Mag.,2010,27(3):102–114.
[91] Geirhofer S, Tong L, Sadler B M. A sensing-based cognitive coexistence method for interferinginfrastructure and ad-hoc systems. Wirel. Commun. Mob. Comput.,2010,10(1):16–30.
[92] Walke B H, Mangold S, Berlemann L. IEEE802Wireless Systems: Protocols, Multi-HopMesh/Relaying, Performance and Spectrum Coexistence. Hoboken, NJ: John Wiley&SonsInc.,2006.
[93] Zhao Y, Mao S, Neel J, et al. Performance evaluation of cognitive radios: Metrics, utilityfunctions, and methodology. Proc. IEEE,2009,97(4):642–659.
[94] Fitzek F H P, Katz M. Cellular controlled peer to peer communications: Overview and po-tentials. Proceedings of Cognitive Wireless Networks. Springer, Netherlands: Springer,2007:31–59.
[95] Mo¨lsa¨ J, Karsikas J, Ka¨rkka¨inen A, et al. Field test results and use scenarios for a WiMAXbased Finnish broadband tactical backbone network. Proceedings of Proceedings of IEEEMilitary Communications Conference (IEEE MILCOM2010),2010.2357–2362.
[96] Resnick S I. Adventures in Stochastic Processes. Boston, MA: Birkha¨user,1992.
[97] Jafar S A,(Shitz) S S. Degrees of freedom region of the MIMO X channel. IEEE Trans. Inf.Theory,2008,54(1):151–170.
[98] Bertsekas D P. Nonlinear Programming.2ed., Belmont, MA: Athena Scientific,1999.
[99] Boyd S, Xiao L, Mutapcic A. Lecture notes of EE392o: Subgradient methods. Stanford Uni-versity,2003..
[100] Yu W, Lui R. Dual methods for nonconvex spectrum optimization of multicarrier systems.IEEE Trans. Commun.,2006,54(7):1310–1322.
[101] Bazaraa M, Sherali H, Shetty C. Nonlinear Programming: Theory and Algorithms.3ed.,Hoboken, New Jersey: Wiley-interscience,2006.
[102] Shor N Z. Minimization Methods for Non-Diferentiable Functions. New York, NY: Springer-Verlag New York, Inc.,1985.
[103] Chandrasekhar V, Andrews J. Uplink capacity and interference avoidance for two-tier femto-cell networks. IEEE Trans. Wireless Commun.,2009,8(7):3498–3509.
[104] Tse D, Viswanath P. Fundamentals of Wireless Communications. Cambridge, UK: CambridgeUniversity Press,2005.
[105] Zhang Y, Liu S, Rui Y, et al. Channel prediction assisted by radio propagation environmentsinformation. Proceedings of Proc. IEEE Int. Conf. Circuits Syst. Commun.(IEEE ICCSC),Shanghai, China,2008.733–736.
[106] Liang Y C, Zeng Y, Peh E, et al. Sensing-throughput tradeof for cognitive radio networks.IEEE Trans. Wireless Commun.,2008,7(4):1326–1337.
[107] Lee W Y, Akyildiz I. Optimal spectrum sensing framework for cognitive radio networks. IEEETrans. Wireless Commun.,2008,7(10):3845–3857.
[108] Xu Y, Sun Y, Li Y, et al. Joint sensing period and transmission time optimization for energy-constrained cognitive radios. EURASIP Journal on Wireless Communications and Networking,Article ID818964,16pages,2010..
[109] Hoang A, Liang Y C, Zeng Y. Adaptive joint scheduling of spectrum sensing and data trans-mission in cognitive radio networks. IEEE Trans. Commun.,2010,58(1):235–246.
[110] Simeone O, Bar-Ness Y, Spagnolini U. Stable throughput of cognitive radios with and withoutrelaying capability. IEEE Trans. Commun.,2007,55(12):2351–2360.
[111] Simeone O, Stanojev I, Savazzi S, et al. Spectrum leasing to cooperating secondary ad hocnetworks. IEEE J. Sel. Areas Commun.,2008,26(1):203–213.
[112] Goldsmith A, Jafar S, Maric I, et al. Breaking spectrum gridlock with cognitive radios: Aninformation theoretic perspective. Proc. IEEE,2009,97(5):894–914.
[113] Ben Letaief K, Zhang W. Cooperative communications for cognitive radio networks. Proc.IEEE,2009,97(5):878–893.
[114] Zhang Q, Jia J, Zhang J. Cooperative relay to improve diversity in cognitive radio networks.IEEE Commun. Mag.,2009,47(2):111–117.
[115] Urkowitz H. Energy detection of unknown deterministic signals. Proc. IEEE,1967,55(4):523–531.
[116] Ghasemi A, Sousa E. Collaborative spectrum sensing for opportunistic access in fading en-vironments. Proceedings of IEEE International Symposium on New Frontiers in DynamicSpectrum Access Networks (IEEE DySPAN2005),2005.131–136.
[117] Yucek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications.IEEE Communications Surveys Tutorials,2009,11(1):116–130.
[118] Geirhofer S, Tong L, Sadler B M. Measurement-based models for cognitive medium access inWLAN bands. Technical Report ACSP-TR-02-07-02..
[119] Pandharipande A, Linnartz J P. Performance analysis of primary user detection in a multipleantenna cognitive radio. Proceedings of IEEE International Conference on Communications(IEEE ICC2007),2007.6482–6486.
[120] Digham F F, Alouini M S, Simon M K. On the energy detection of unknown signals overfading channels. IEEE Trans. Commun.,2007,55(1):21–24.
[121] Herath S, Rajatheva N. Analysis of equal gain combining in energy detection for cognitiveradio over nakagami channels. Proceedings of IEEE Global Telecommunications Conference(IEEE GLOBECOM2008),2008.
[122] Herath S, Rajatheva N, Tellambura C. Unified approach for energy detection of unknowndeterministic signal in cognitive radio over fading channels. Proceedings of IEEE InternationalConference on Communications Workshops (IEEE ICC Workshops2009),2009.1–5.
[123] Herath S, Rajatheva N, Tellambura C. On the energy detection of unknown deterministicsignal over nakagami channelswith selection combining. Proceedings of Canadian Conferenceon Electrical and Computer Engineering (CCECE2009),2009.745–749.
[124] Sun Y, Baricz A, Zhou S. On the monotonicity, log-concavity and tight bounds of the general-ized marcum and nuttall q functions. IEEE Trans. Inf. Theory,2010,56(3):1166–1186.
[125] Marcum J I. Table of functions. U.S. Air Force Project RAND Res. Memo. M-339,1950.ASTIA Document AD1165451, RAND Corporation, Santa Monica, CA, Jan.1.
[126] Abramowitz M, Stegun I. Handbook of Mathematical Functions with Formulas, Graphs, andMathematical Tables.9ed., New York, NY: Dover Press,1972.
[127] Karlin S. Total Positivity. Stanford: Stanford Univ. Press,1968.
[128] Papoulis A, Pillai S U. Probability, Random Variables and Stochastic Processes.4ed.,McGraw-Hill Companies, Inc.,2002.
[129] Hardy G H, Littlehood J E, Po′lya G. Inequalities.2ed., Cambridge, England: CambridgeUniversity Press,1952.
[130] Heath M T. Scientific computing: An introductory survey. New York: McGraw-Hill Compa-nies,2001.
[131] Cover T M, Thomas J A. Elements of Information Theory.2ed., Hoboken, New Jersey:Wiley-interscience,2006.
[132] Zhao Y, Adve R, Lim T. Improving amplify-and-forward relay networks: optimal power allo-cation versus selection. IEEE Trans. Wireless Commun.,2007,6(8):3114–3123.
[133] Hong Y W, Huang W J, Chiu F H, et al. Cooperative communications in resource-constrainedwireless networks. IEEE Signal Process. Mag.,2007,24(3):47–57.
[134] Li Y, Vucetic B, Zhou Z, et al. Distributed adaptive power allocation for wireless relay net-works. IEEE Trans. Wireless Commun.,2007,6(3):948–958.
[135] Ding Z, Chin W H, Leung K. Distributed beamforming and power allocation for cooperativenetworks. IEEE Trans. Wireless Commun.,2008,7(5):1817–1822.
[136] Chen M, Serbetli S, Yener A. Distributed power allocation strategies for parallel relay net-works. IEEE Trans. Wireless Commun.,2008,7(2):552–561.
[137] Dang W, Tao M, Mu H, et al. Subcarrier-pair based resource allocation for cooperative multi-relay OFDM systems. IEEE Trans. Wireless Commun.,2010,9(5):1640–1649.
[138] Maric I, Yates R. Bandwidth and power allocation for cooperative strategies in gaussian relaynetworks. IEEE Trans. Inf. Theory,2010,56(4):1880–1889.
[139] Li H, Luo H, Wang X, et al. Fairness-aware resource allocation in ofdma cooperative relay-ing network. Proceedings of IEEE International Conference on Communications (ICC2009),2009.1–5.
[140] Gong X, Vorobyov S, Tellambura C. Joint bandwidth and power allocation with admissioncontrol in wireless multi-user networks with and without relaying. IEEE Trans. Signal Process.,2011. To appear.
[141] Le L, Hossain E. Cross-layer optimization frameworks for multihop wireless networks usingcooperative diversity. IEEE Trans. Wireless Commun.,2008,7(7):2592–2602.
[142] Kadloor S, Adve R. Relay selection and power allocation in cooperative cellular networks.IEEE Trans. Wireless Commun.,2010,9(5):1676–1685.
[143] Hosseini K, Adve R. Relay selection and max-min resource allocation for multi-source OFDM-based mesh networks. Proceedings of IEEE International Conference on Communications(ICC2010),,2010.1–6.
[144] Pan Y, Nix A, Beach M. Distributed resource allocation for OFDMA-based relay networks.IEEE Trans. Veh. Technol.,2011,60(3):919–931.
[145] Mesbah W, Davidson T N. Power and resource allocation for orthogonal multiple access relaysystems. EURASIP J. Signal Process.,2008,2008(5):1640–1649.
[146] Chen M, Yener A. Power allocation for F/TDMA multiuser two-way relay networks. IEEETrans. Wireless Commun.,2010,9(2):546–551.
[147] Weng L, Murch R. Cooperation strategies and resource allocations in multiuser OFDMAsystems. IEEE Trans. Veh. Technol.,2009,58(5):2331–2342.
[148] Xiao L, Johansson M, Boyd S P. Simultaneous routing and resource allocation via dual de-composition. IEEE Trans. Commun.,2004,52(7):1136–1144.
[149] Johnson N L, Kotz S, Balakrishnan N. Continuous Univariate Distributions.2ed., New York:John Wiley&Sons Inc.,1995.
[150] Bagnoli M, Bergstrom T. Log-concave probability and its applications. Economic Theory,2005,26:445–469.
[151] Baricz A. Tura′n type inequalities for some probability density functions. Studia Sci. Math.Hungar. To appear.
[152] Sun Y, Mao Z, Zhong X, et al. Distributed resource allocation for decode-and-forward cooper-ative relay networks: A fast proximal point method. IEEE/ACM Transactions on Networking,2011. Submitted for publication.
[2]高锟.新通信网络的实现.电信科学,1990,(4):1–2.
[3] http://baike.baidu.com/view/21461.htm.
[4]代琳.分布式无线通信系统的容量和关键技术研究[博士学位论文].北京:清华大学,2002.
[5] Frenkiel R. Creating cellular: A history of the AMPS project (1971-1983). IEEE Commun.Mag.,2010,48(9):14–24.
[6]李军.异构无线网络融合理论与技术实现.北京:电子工业出版社,2008.
[7]3GPP. http://www.3gpp.org/HSPA.
[8] http://baike.baidu.com/view/1359910.html.
[9] ITU-R M2134. Requirements related to technical performance for IMT-Advanced radio inter-face(s).
[10] Hashimoto A, Yoshino H, Atarashi H. Roadmap of IMT-Advanced development. IEEE Mi-crow. Mag.,2008,9(4):80–88.
[11]3GPP. http://www.3gpp.org/LTE-Advanced.
[12] IEEE. http://wirelessman.org/tgm/.
[13] ITU. http://www.itu.int/net/pressofce/press releases/2010/48.aspx.
[14]冯伟.非理想分布式无线通信系统中的资源分配与优化[博士学位论文].北京:清华大学,2010.
[15] Berrou C, Glavieux A, Thitimajshima P. Near shannon limit error-correcting coding and de-coding: Turbo-codes.1. Proceedings of IEEE International Conference on Communications,(IEEE ICC1993), volume2,1993.1064–1070vol.2.
[16] Gallager R G. Low-density parity-check codes. IRE Trans. Inf. Theory,1962,8(1):21–28.
[17] Weinstein S, Ebert P. Data transmission by frequency-division multiplexing using the discretefourier transform. IEEE Transactions on Communications,1971,19(5):628–634.
[18] Foschini G J. Layered space-time architecture for wireless communications in a fading envi-ronment when using multi-element antennas. Bell Labs Technical Journal,1996,1(2):41–59.
[19] Telatar E. Capacity of multi-antenna gaussian channels. European Trans Telecommunication,1999,10(6):585–596.
[20] Alamouti S M. A simple transmit diversity technique for wireless communications. IEEEJournal on Selected Areas in Coomunications,1998,16(8):1451–1458.
[21] Pabst R, Walke B H, Schultz D C, et al. Relay-based deployment concepts for wireless andmobile broadband radio. IEEE Commun. Mag.,2004,42(9):80–89.
[22] Peters S, Heath R. The future of WiMAX: Multihop relaying with ieee802.16j. IEEE Com-mun. Mag.,2009,47(1):104–111.
[23] Soldani D, Dixit S. Wireless relays for broadband access. IEEE Commun. Mag.,2008,46(3):58–66.
[24] Femto Forum. http://www.femtoforum.com.
[25] Chandrasekhar V, Andrews J G, Gatherer A. Femtocell networks: A survey. IEEE Commun.Mag.,2008,46(9):59–67.
[26] Claussen H, Ho L T W, Samuel L G. An overview of the femtocell concept. Bell Lab. Tech.J.,2008,13:221–245.
[27] Cover T, Gamal A. Capacity theorems for the relay channel. IEEE Trans. Inf. Theory,1979,IT-25(5):572–584.
[28] Laneman J, Tse D, Wornell G. Cooperative diversity in wireless networks: Efcient protocolsand outage behavior. IEEE Trans. Inf. Theory,2004,50(12):3062–3080.
[29] Kramer G, Gastpar M, Gupta P. Cooperative strategies and capacity theorems for relay net-works. IEEE Trans. Inf. Theory,2005,51(9):3037–3063.
[30] Chakrabarti A, Baynast A, Sabharwal A, et al. Low density parity check codes for the relaychannel. IEEE J. Sel. Areas Commun.,2007,25(2):280–291.
[31] Salem M, Adinoyi A, Rahman M, et al. An overview of radio resource management in relay-enhanced OFDMA-based networks. IEEE Commun. Surveys Tuts.,2010,12(3):422–438.
[32] Rankov B, Wittneben A. Spectral efcient protocols for half-duplex fading relay channels.IEEE J. Sel. Areas Commun.,2007,25(2):379–389.
[33] Avestimehr A, Tse D. Outage capacity of the fading relay channel in the low-SNR regime.IEEE Trans. Inf. Theory,2007,53(4):1401–1415.
[34] Landstro¨m S, Furuska′r A, Johansson K, et al. Heterogeneous networks-increasing cellularcapacity. Ericsson Review,2011,(1):4–9.
[35] Bertsekas D P, Tsitsiklis J N. Parallel and Distributed Computation: Numerical Methods. En-glewood Clifs, NJ: Prentice-Hall,1989.
[36] Johansson B, Soldati P, Johansson M. Mathematical decomposition techniques for distributedcross-layer optimization of data networks. IEEE J. Sel. Areas Commun.,2006,24(8):1535–1547.
[37] Palomar D, Chiang M. A tutorial on decomposition methods for network utility maximization.IEEE J. Sel. Areas Commun.,2006,24(8):1439–1451.
[38] Lin X, Shrof N, Srikant R. A tutorial on cross-layer optimization in wireless networks. IEEEJ. Sel. Areas Commun.,2006,24(8):1452–1463.
[39] Chiang M, Low S, Calderbank A, et al. Layering as optimization decomposition: A mathemat-ical theory of network architectures. Proc. IEEE,2007,95(1):255–312.
[40] Palomar D, Chiang M. Alternative distributed algorithms for network utility maximization:Framework and applications. IEEE Trans. Auto. Control,2007,52(12):2254–2269.
[41] Lin X, Shrof N, Srikant R. On the connection-level stability of congestion-controlled commu-nication networks. IEEE Trans. Inf. Theory,2008,54(5):2317–2338.
[42] Lin X, Shrof N. Utility maximization for communication networks with multipath routing.IEEE Trans. Auto. Control,2006,51(5):766–781.
[43] Zhao Q, Sadler B M. A survey of dynamic spectrum access: Signal processing, networking,and regulatory policy. IEEE Signal Process. Mag.,2007,24(5):79–89.
[44] Peha J. Sharing spectrum through spectrum policy reform and cognitive radio. Proc. IEEE,2009,97(4):708–719.
[45] IEEE. http://www.wirelessman.org/le/.
[46] IEEE. www.ieee802.org/22/.
[47] IEEE. http://grouper.ieee.org/groups/802/11/Reports/tgy update.htm.
[48] Liu B, Chen B, Blum R S. Minimum error probability cooperative relay design. IEEE Trans.Signal Process.,2007,55(2):656–664.
[49] Ng T C Y, Yu W. Joint optimization of relay strategies and resource allocations in cooperativecellular networks. IEEE J. Sel. Areas Commun.,2007,25(2):328–339.
[50] Liu K J R, Sadek A K, Su W, et al. Cooperative Communications and Networking. Cambridge:Cambridge University Press,2009.
[51] Gu¨ndu¨z D, Erkip E, Goldsmith A, et al. Cooperative relaying in sensor networks. Proceedingsof Proceedings of the Fifth International Conference on Cognitive Radio Oriented WirelessNetworks Communications (CROWNCOM2010),2010.1–5.
[52] Scaglione A, Goeckel D, Laneman J. Cooperative communications in mobile ad hoc networks:Rethinking the link abstraction. IEEE Signal Process. Mag.,2006,23(5):18–29.
[53] Lo A, Niemegeers I. Multi-hop relay architectures for3GPP LTE-advanced. Proceedingsof Proceedings of IEEE9th Malaysia International Conference on Communications (MICC2009),2009.123–127.
[54] Warty C. Cooperative communication for multiple satellite network. Proceedings of Proceed-ings of IEEE Aerospace Conference,2010.1–7.
[55] H st-Madsen A, Zhang J. Capacity bounds and power allocation for wireless relay channels.IEEE Trans. Inf. Theory,2005,51(6):2020–2040.
[56] Loa K, Wu C C, Sheu S T, et al. Imt-advanced relay standards. IEEE Commun. Mag.,2010,48(8):40–48.
[57] Li G, Liu H. Resource allocation for OFDMA relay networks with fairness constraints. IEEEJ. Sel. Areas Commun.,2006,24(11):2061–2069.
[58] Jitvanichphaibool K, Zhang R, Liang Y C. Optimal resource allocation for two-way relay-assisted OFDMA. IEEE Trans. Veh. Technol.,2009,58(7):3311–3321.
[59] Cui Y, Lau V, Wang R. Distributive subband allocation, power and rate control for relay-assisted OFDMA cellular system with imperfect system state knowledge. IEEE Trans. WirelessCommun.,2009,8(10):5096–5102.
[60] Nosratinia A, Hunter T, Hedayat A. Cooperative communication in wireless networks. IEEECommun. Mag.,2004,42(10):74–80.
[61] Liang Y, Veeravalli V V. Gaussian orthogonal relay channels: optimal resource allocation andcapacity. IEEE Trans. Inf. Theory,2005,51(9):3284–3289.
[62] Liang Y, Veeravalli V V, Poor H V. Resource allocation for wireless fading relay channel:Max-min solution. IEEE Trans. Inf. Theory,2007,53(10):3432–3453.
[63] Serbetli S, Yener A. Relay assisted F/TDMA ad hoc networks: node classification, powerallocation and relaying strategies. IEEE Trans. Commun.,2008,56(6):937–947.
[64] IEEE. IEEE802.16m System Requirements: IEEE802.16m-07/002r10.
[65] Boyd S, Vandenberghe L. Convex Optimization. Cambridge, UK: Cambridge University Press,2004.
[66] Gamal A E, Kim Y H. Lecture Notes on Network Information Theory.2010.
[67] Gamal A, Aref M. The capacity of the semideterministic relay channel. IEEE Trans. Inf.Theory,1982, IT-28(3):536–536.
[68] Chakrabarti A, Erkip E, Sabharwal A, et al. Code designs for cooperative communication.IEEE Signal Process. Mag.,2007,24(5):16–26.
[69] Li Y. Distributed coding for cooperative wireless networks: An overview and recent advances.IEEE Commun. Mag.,2009,47(8):71–77.
[70] Li C, Yue G, Wang X, et al. LDPC code design for half-duplex cooperative relay. IEEE Trans.Wireless Commun.,2008,7(11):4558–4567.
[71] Wang B, Zhang J, H st-Madsen A. On the capacity of MIMO relay channels. IEEE Trans. Inf.Theory,2005,51(1):29–43.
[72] Tang J, Zhang X. Cross-layer resource allocation over wireless relay networks for quality ofservice provisioning. IEEE J. Sel. Areas Commun.,2007,25(5):645–656.
[73] Farhadi G, Beaulieu N. On the ergodic capacity of wireless relaying systems over Rayleighfading channels. IEEE Trans. Wireless Commun.,2008,7(11):4462–4467.
[74] Simon M K, Alouini M S. Digital Communication over Fading Channels: A Unified Approachto Performance Analysis. New York: Wiley,2000.
[75] Nakagami M. The m distribution: A general formula of intensity distribution of rapid fad-ing. In: Hofman W G,(eds.). Proceedings of Statistical Methods in Radio Wave Propagation.Oxford: Pergamon Press,1960:3–36.
[76] Gradshteyn I S, Ryzhik I. Table of Integrals, Series and Products. New York: Academic Press,2000.
[77] Moschopoulos P G. The distribution of the sum of independent gamma random variables. Ann.Inst. Statist. Math.(Part A),1985,37:541–543.
[78] Zhong C, Wong K K, Jin S. Capacity bounds for MIMO nakagami-m fading channels. IEEETrans. Signal Process.,2009,57(9):3613–3623.
[79] Zhao Q, Tong L, Swami A, et al. Decentralized cognitive MAC for opportunistic spectrumaccess in ad hoc networks: A POMDP framework. IEEE J. Sel. Areas Commun.,2007,25(3):589–600.
[80] Su H, Zhang X. Cross-layer based opportunistic MAC protocols for QoS provisionings overcognitive radio wireless networks. IEEE J. Sel. Areas Commun.,2008,26(1):118–129.
[81] Geirhofer S, Tong L, Sadler B M. Dynamic spectrum access in the time domain: Modeling andexploiting whitespace. IEEE Commun. Mag.,2007,45(5):66–72.
[82] Geirhofer S, Tong L, Sadler B M. Cognitive medium access: Constraining interference basedon experimental models. IEEE J. Sel. Areas Commun.,2008,26(1):95–105.
[83] Zhao Q, Geirhofer S, Tong L, et al. Opportunistic spectrum access via periodic channel sensing.IEEE Trans. Signal Process.,2008,56(2):785–796.
[84] Geirhofer S, Sun J Z, Tong L, et al. Cognitive frequency hopping based on interference predic-tion: Theory and experimental results. ACM SIGMOBILE Mob. Comput.and Commun. Rev.,2009,13(2):49–61.
[85] Li X, Zhao Q C, Guan X, et al. Optimal cognitive access of Markovian channels under tightcollision constraints. IEEE J. Sel. Areas Commun.,2011. To appear.
[86] Lai L, El Gamal H, Jiang H, et al. Cognitive medium access: Exploration, exploitation, andcompetition. IEEE Trans. Mob. Comput.,2011,10(2):239–253.
[87] Wang P, Zhao M, Xiao L, et al. Power allocation in OFDM-based cognitive radio systems.Proceedings of Proceedings of Global Telecommunications Conference (IEEE GLOBECOM2007),2007.4061–4065.
[88] Zhang R, Liang Y C. Exploiting multi-antennas for opportunistic spectrum sharing in cognitiveradio networks. IEEE J. Sel. Topics Signal Process.,2008,2(1):88–102.
[89] Marques A, Wang X, Giannakis G. Dynamic resource management for cognitive radios usinglimited-rate feedback. IEEE Trans. Signal Process.,2009,57(9):3651–3666.
[90] Zhang R, Liang Y, Cui S. Dynamic resource allocation in cognitive radio networks. IEEESignal Process. Mag.,2010,27(3):102–114.
[91] Geirhofer S, Tong L, Sadler B M. A sensing-based cognitive coexistence method for interferinginfrastructure and ad-hoc systems. Wirel. Commun. Mob. Comput.,2010,10(1):16–30.
[92] Walke B H, Mangold S, Berlemann L. IEEE802Wireless Systems: Protocols, Multi-HopMesh/Relaying, Performance and Spectrum Coexistence. Hoboken, NJ: John Wiley&SonsInc.,2006.
[93] Zhao Y, Mao S, Neel J, et al. Performance evaluation of cognitive radios: Metrics, utilityfunctions, and methodology. Proc. IEEE,2009,97(4):642–659.
[94] Fitzek F H P, Katz M. Cellular controlled peer to peer communications: Overview and po-tentials. Proceedings of Cognitive Wireless Networks. Springer, Netherlands: Springer,2007:31–59.
[95] Mo¨lsa¨ J, Karsikas J, Ka¨rkka¨inen A, et al. Field test results and use scenarios for a WiMAXbased Finnish broadband tactical backbone network. Proceedings of Proceedings of IEEEMilitary Communications Conference (IEEE MILCOM2010),2010.2357–2362.
[96] Resnick S I. Adventures in Stochastic Processes. Boston, MA: Birkha¨user,1992.
[97] Jafar S A,(Shitz) S S. Degrees of freedom region of the MIMO X channel. IEEE Trans. Inf.Theory,2008,54(1):151–170.
[98] Bertsekas D P. Nonlinear Programming.2ed., Belmont, MA: Athena Scientific,1999.
[99] Boyd S, Xiao L, Mutapcic A. Lecture notes of EE392o: Subgradient methods. Stanford Uni-versity,2003..
[100] Yu W, Lui R. Dual methods for nonconvex spectrum optimization of multicarrier systems.IEEE Trans. Commun.,2006,54(7):1310–1322.
[101] Bazaraa M, Sherali H, Shetty C. Nonlinear Programming: Theory and Algorithms.3ed.,Hoboken, New Jersey: Wiley-interscience,2006.
[102] Shor N Z. Minimization Methods for Non-Diferentiable Functions. New York, NY: Springer-Verlag New York, Inc.,1985.
[103] Chandrasekhar V, Andrews J. Uplink capacity and interference avoidance for two-tier femto-cell networks. IEEE Trans. Wireless Commun.,2009,8(7):3498–3509.
[104] Tse D, Viswanath P. Fundamentals of Wireless Communications. Cambridge, UK: CambridgeUniversity Press,2005.
[105] Zhang Y, Liu S, Rui Y, et al. Channel prediction assisted by radio propagation environmentsinformation. Proceedings of Proc. IEEE Int. Conf. Circuits Syst. Commun.(IEEE ICCSC),Shanghai, China,2008.733–736.
[106] Liang Y C, Zeng Y, Peh E, et al. Sensing-throughput tradeof for cognitive radio networks.IEEE Trans. Wireless Commun.,2008,7(4):1326–1337.
[107] Lee W Y, Akyildiz I. Optimal spectrum sensing framework for cognitive radio networks. IEEETrans. Wireless Commun.,2008,7(10):3845–3857.
[108] Xu Y, Sun Y, Li Y, et al. Joint sensing period and transmission time optimization for energy-constrained cognitive radios. EURASIP Journal on Wireless Communications and Networking,Article ID818964,16pages,2010..
[109] Hoang A, Liang Y C, Zeng Y. Adaptive joint scheduling of spectrum sensing and data trans-mission in cognitive radio networks. IEEE Trans. Commun.,2010,58(1):235–246.
[110] Simeone O, Bar-Ness Y, Spagnolini U. Stable throughput of cognitive radios with and withoutrelaying capability. IEEE Trans. Commun.,2007,55(12):2351–2360.
[111] Simeone O, Stanojev I, Savazzi S, et al. Spectrum leasing to cooperating secondary ad hocnetworks. IEEE J. Sel. Areas Commun.,2008,26(1):203–213.
[112] Goldsmith A, Jafar S, Maric I, et al. Breaking spectrum gridlock with cognitive radios: Aninformation theoretic perspective. Proc. IEEE,2009,97(5):894–914.
[113] Ben Letaief K, Zhang W. Cooperative communications for cognitive radio networks. Proc.IEEE,2009,97(5):878–893.
[114] Zhang Q, Jia J, Zhang J. Cooperative relay to improve diversity in cognitive radio networks.IEEE Commun. Mag.,2009,47(2):111–117.
[115] Urkowitz H. Energy detection of unknown deterministic signals. Proc. IEEE,1967,55(4):523–531.
[116] Ghasemi A, Sousa E. Collaborative spectrum sensing for opportunistic access in fading en-vironments. Proceedings of IEEE International Symposium on New Frontiers in DynamicSpectrum Access Networks (IEEE DySPAN2005),2005.131–136.
[117] Yucek T, Arslan H. A survey of spectrum sensing algorithms for cognitive radio applications.IEEE Communications Surveys Tutorials,2009,11(1):116–130.
[118] Geirhofer S, Tong L, Sadler B M. Measurement-based models for cognitive medium access inWLAN bands. Technical Report ACSP-TR-02-07-02..
[119] Pandharipande A, Linnartz J P. Performance analysis of primary user detection in a multipleantenna cognitive radio. Proceedings of IEEE International Conference on Communications(IEEE ICC2007),2007.6482–6486.
[120] Digham F F, Alouini M S, Simon M K. On the energy detection of unknown signals overfading channels. IEEE Trans. Commun.,2007,55(1):21–24.
[121] Herath S, Rajatheva N. Analysis of equal gain combining in energy detection for cognitiveradio over nakagami channels. Proceedings of IEEE Global Telecommunications Conference(IEEE GLOBECOM2008),2008.
[122] Herath S, Rajatheva N, Tellambura C. Unified approach for energy detection of unknowndeterministic signal in cognitive radio over fading channels. Proceedings of IEEE InternationalConference on Communications Workshops (IEEE ICC Workshops2009),2009.1–5.
[123] Herath S, Rajatheva N, Tellambura C. On the energy detection of unknown deterministicsignal over nakagami channelswith selection combining. Proceedings of Canadian Conferenceon Electrical and Computer Engineering (CCECE2009),2009.745–749.
[124] Sun Y, Baricz A, Zhou S. On the monotonicity, log-concavity and tight bounds of the general-ized marcum and nuttall q functions. IEEE Trans. Inf. Theory,2010,56(3):1166–1186.
[125] Marcum J I. Table of functions. U.S. Air Force Project RAND Res. Memo. M-339,1950.ASTIA Document AD1165451, RAND Corporation, Santa Monica, CA, Jan.1.
[126] Abramowitz M, Stegun I. Handbook of Mathematical Functions with Formulas, Graphs, andMathematical Tables.9ed., New York, NY: Dover Press,1972.
[127] Karlin S. Total Positivity. Stanford: Stanford Univ. Press,1968.
[128] Papoulis A, Pillai S U. Probability, Random Variables and Stochastic Processes.4ed.,McGraw-Hill Companies, Inc.,2002.
[129] Hardy G H, Littlehood J E, Po′lya G. Inequalities.2ed., Cambridge, England: CambridgeUniversity Press,1952.
[130] Heath M T. Scientific computing: An introductory survey. New York: McGraw-Hill Compa-nies,2001.
[131] Cover T M, Thomas J A. Elements of Information Theory.2ed., Hoboken, New Jersey:Wiley-interscience,2006.
[132] Zhao Y, Adve R, Lim T. Improving amplify-and-forward relay networks: optimal power allo-cation versus selection. IEEE Trans. Wireless Commun.,2007,6(8):3114–3123.
[133] Hong Y W, Huang W J, Chiu F H, et al. Cooperative communications in resource-constrainedwireless networks. IEEE Signal Process. Mag.,2007,24(3):47–57.
[134] Li Y, Vucetic B, Zhou Z, et al. Distributed adaptive power allocation for wireless relay net-works. IEEE Trans. Wireless Commun.,2007,6(3):948–958.
[135] Ding Z, Chin W H, Leung K. Distributed beamforming and power allocation for cooperativenetworks. IEEE Trans. Wireless Commun.,2008,7(5):1817–1822.
[136] Chen M, Serbetli S, Yener A. Distributed power allocation strategies for parallel relay net-works. IEEE Trans. Wireless Commun.,2008,7(2):552–561.
[137] Dang W, Tao M, Mu H, et al. Subcarrier-pair based resource allocation for cooperative multi-relay OFDM systems. IEEE Trans. Wireless Commun.,2010,9(5):1640–1649.
[138] Maric I, Yates R. Bandwidth and power allocation for cooperative strategies in gaussian relaynetworks. IEEE Trans. Inf. Theory,2010,56(4):1880–1889.
[139] Li H, Luo H, Wang X, et al. Fairness-aware resource allocation in ofdma cooperative relay-ing network. Proceedings of IEEE International Conference on Communications (ICC2009),2009.1–5.
[140] Gong X, Vorobyov S, Tellambura C. Joint bandwidth and power allocation with admissioncontrol in wireless multi-user networks with and without relaying. IEEE Trans. Signal Process.,2011. To appear.
[141] Le L, Hossain E. Cross-layer optimization frameworks for multihop wireless networks usingcooperative diversity. IEEE Trans. Wireless Commun.,2008,7(7):2592–2602.
[142] Kadloor S, Adve R. Relay selection and power allocation in cooperative cellular networks.IEEE Trans. Wireless Commun.,2010,9(5):1676–1685.
[143] Hosseini K, Adve R. Relay selection and max-min resource allocation for multi-source OFDM-based mesh networks. Proceedings of IEEE International Conference on Communications(ICC2010),,2010.1–6.
[144] Pan Y, Nix A, Beach M. Distributed resource allocation for OFDMA-based relay networks.IEEE Trans. Veh. Technol.,2011,60(3):919–931.
[145] Mesbah W, Davidson T N. Power and resource allocation for orthogonal multiple access relaysystems. EURASIP J. Signal Process.,2008,2008(5):1640–1649.
[146] Chen M, Yener A. Power allocation for F/TDMA multiuser two-way relay networks. IEEETrans. Wireless Commun.,2010,9(2):546–551.
[147] Weng L, Murch R. Cooperation strategies and resource allocations in multiuser OFDMAsystems. IEEE Trans. Veh. Technol.,2009,58(5):2331–2342.
[148] Xiao L, Johansson M, Boyd S P. Simultaneous routing and resource allocation via dual de-composition. IEEE Trans. Commun.,2004,52(7):1136–1144.
[149] Johnson N L, Kotz S, Balakrishnan N. Continuous Univariate Distributions.2ed., New York:John Wiley&Sons Inc.,1995.
[150] Bagnoli M, Bergstrom T. Log-concave probability and its applications. Economic Theory,2005,26:445–469.
[151] Baricz A. Tura′n type inequalities for some probability density functions. Studia Sci. Math.Hungar. To appear.
[152] Sun Y, Mao Z, Zhong X, et al. Distributed resource allocation for decode-and-forward cooper-ative relay networks: A fast proximal point method. IEEE/ACM Transactions on Networking,2011. Submitted for publication.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |