认知无线电网络中的频谱感知技术及应用研究
|
摘要
随着无线网络的飞速发展,对无线电频谱资源的需求也与日俱增。认知无线电技术允许认知用户动态共享主授权频谱资源,有效地提高了频谱利用率,是无线通信领域中一项突破性技术。准确高效的频谱感知是实现认知无线电的关键,是目前业界研究热点课题之一。
本文在《宽带抗干扰认知无线电系统设计研究》项目和国家重大专项(2009ZX03002-009-01)以及国家自然科学基金(60672132、60872149)的支持下,对认知无线电网络中的频谱感知技术及其应用进行了深入研究。本文主要工作及创新成果如下：
(1)在协作频谱感知(CSS)中,影响系统性能的各参数之间紧密相关,其中,检测门限是影响系统性能的一个关键因素,目前的研究中,检测门限均通过仿真获得,而没有给出明确的算法。本文首次从带宽的角度综合分析了认知无线电网络中的次用户(SU)数、检测门限、带宽、频谱利用率和次用户吞吐量之间的关系,提出了一种优化的检测门限选取算法,通过直接对数学模型求解即可得到使次用户吞吐量最大化的最优检测门限值,同时确保主用户(PU)受到足够保护。
(2)在CSS中,协作的SU数增加时,发送本地感知结果会占用较大控制信道带宽,而且当报告信道衰落严重时,CSS中的尾感知问题随着协作SU数的增加会变得更加严重,针对此问题,提出了一种基于删余的CSS(C-CSS)方案,并从次用户的角度,给出了一种可使次用户接入机会最大化的优化检测算法,研究结果表明：C-CSS可有效节约控制信道带宽,解决了报告信道存在衰落时的尾感知问题,通过利用优化算法可以在保证目标检测率的前提下获得最低的虚警率,使SU的接入机会最大化；进一步从次用户吞吐量角度研究了C-CSS的性能,分析了C-CSS方案中带宽、频谱利用率、次用户吞吐量与SU数和检测门限之间的关系,提出了C-CSS中使次用户吞吐量最大化的优化算法,研究结果表明：当SU数一定时,为了保证目标检测率,C-CSS方案相比传统CSS方案使次用户吞吐量达到最大时所要求的检测门限是不同的,C-CSS方案可以利用较少的SU数实现较高的次用户吞吐量,利用提出的优化算法可以获得最优的次用户吞吐量。
(3)基于中继协作的传输方式可以有效抵抗信道衰落对系统性能的影响,为了提高报告信道上的传输可靠性,提出了一种基于目标对象的最佳中继协作频谱感知方案(Pe-BRCS),通过最小化报告信道上的错误传输概率为目标选择最佳中继以提高感知性能,研究结果表明：相比SINR-BRCS方案,所提方案可有效降低报告信道上的错误传输概率,降低虚警率下界,可以在牺牲较少计算复杂度的条件下提高CSS性能；针对目前基于中继的CSs研究中均考虑全协作的情况,系统开销大,为了避免不必要的资源浪费,本文提出了一种自适应的最佳中继协作频谱感知方案(ABRCS),目标SU会根据其报告信道条件,自适应的选择是否需要中继协作传输,研究结果表明：所提ABRCS方案无论在SU报告信道条件较好或较差的情况下,均能实现最佳的感知性能。
(4)基于软合并的CSS相比硬合并方式可以有效提高系统检测性能,针对目前基于数据融合的软合并CSS会占用太多控制信道带宽从而导致频谱利用率低下,且没有考虑报告信道存在衰落的问题,本文考虑感知信道和报告信道均存在衰落的情况,提出了一种基于软决策融合的CSS(SC-DF-CSS)方案,推导了其检测性能表达式,并给出了优化的权重向量取值算法,结果表明：相比基于数据融合的软合并CSS,所提SC-DF-CSS仅有较小的性能损失,但可以有效节约控制信道带宽,基于修正偏差系数的优化算法可以很好地近似基于N-P准则的优化算法,大大简化了优化算法复杂度。
(5)对频谱感知性能研究的目的是为了使其更好地在实际中得到应用。目前的小区间干扰协调方法无法突破现有固定的频谱分配架构,小区边缘用户速率仍然很难得到提升,而认知无线电技术作为一种频率规划技术,可以利用频谱感知智能地感知周围环境,通过检测无线频谱的使用情况从而实现资源的优化配置、实现频谱的充分利用,提高频谱利用率。为此,本文提出了一种基于频谱感知技术的小区间干扰协调方法,具有认知功能的终端发起业务请求时,可以自动检测可用频谱上的信号强度,从而选择空闲频谱或已使用频谱信号强度最弱的频谱进行通信,实现真正的最弱干扰通信,降低小区间干扰,提升了小区边缘用户性能。
With the rapid development of wireless network, more and more radio spectrum resource will be needed. Cognitive radio is an exciting emerging technology to improve spectrum efficiency, by which, the licenced spectrum resource can be shared dynamically by cognitive user. The accurate and effective spectrum sensing is the key of realizing the cognitive radio, which are the research hotspots in wireless sphere.
Under the support of Foundation (Design and research on cognitive radio system to anti interference for military use), National Science and Technology Major Project of China (2009ZX03002-009-01) and National Science Foundation of China (60672132,60872149), the spectrum sensing technology and corresponding application in CRN are deeply investigated in this paper. The main contributions of the dissertation are as follows:
(1) The parameters influencing the system performance are closely related in cooperative spectrum sensing and detection threshold is an important factor, which is usally obtained through simulation comparison other than given algorithm. In this paper, the relationship among SU, detection threshold, bandwidth, spectrum utilization and secondary throughput in cognitive radio networks is analyzed in the point of bandwidth for the first time. An optimal algorithm for selecting detection threshold is proposed, by solving the mathematical model, the maximized secondary throughput can always be achieved while assuring sufficient protection to primary user.
(2) In CSS, more secondary user leads to much control bandwidth occupied for sending the local sensing results, and the tail sensing problem will also become serious when the reporting channel suffer from deep fading. To deal with this limitation, a censoring based CSS is proposed and an optimal detection algorithm suggested for further from the point of secondary user. The analytical results show that the control bandwidth can be effectively saved, the dependence of reporting error to sensing user number can also be reduced by C-CSS. The tail sensing problem can be effectively solved and the false alarm probability also decreased. The maximized spectrum access chance of secondary user can be realized while assuring target detection probability by optimal algorithm. In addition, C-CSS performance from the point of secondary user throughput is analyzed for further. The relationship among SU, detection threshold, bandwidth, spectrum utilization and secondary throughput is studyed and the optimal algorithm for maximizing the secondary throughput is proposed. The research results indicate that the required detection threshold for maximizing the secondary throughput is different between traditional CSS and C-CSS under given SU number while assuring object detection probability. The larger secondary throughput can be achieved with less SU in C-CSS. The optimal secondary throughput can be realized by the proposed optimal algorithm.
(3) The system performance will be degraded by channel fading, which can be effectively solved by relay cooperation. In order to improve the transmission reliability, an object based cooperative spectrum sensing scheme with best relay (Pe-BRCS) is proposed, in which the best relay is selected by minimizing the probability of reporting error to improve the sensing performance. Numerical results show that, the proposed Pe-BRCS can make the reduced reporting error probability and the false alarm probability lower bounds. The sensing performance can be improved with little computational complexity compared with SINR-BRCS. At present, the fully cooperation occasion is considered in relay cooperation in which the higher system cost is needed. In order to save the system expense in actual occasion, an adaptive cooperative spectrum sensing scheme with best relay (ABRCS) is proposed, where, the target SU can adaptively decide whether the relay's cooperation is needed according to its reporting channel condition. Results illustrate that the best sensing performance can always be achieved by ABRCS whether SU's reporting channel is good or not.
(4) The detection performance can be effectively improved by soft combination compared with hard combination in CSS. According to the deflection that much control bandwidth will be occupied in CSS based on soft combination with data fusion, which leads to the reduced spectrum utilization, in addition, the imperfect reporting channel has not been considered at present. The actual occasion with both imperfect sensing channel and reporting channel is considered in this paper. A CSS based on soft combine with decision fusion (SC-DF-CSS) is proposed and the detection performance is analyzed for further. At last, the optimal algorithm to obtain the weighting vector is given. The results show that, only a little detection performance loss exists in SC-DF-CSS compared with SC-EF-CSS. The detection performance could be effectively improved by the optimal algorithm, and the optimal algorithm based on modified deflection coefficient can achieve the same performance with the optimal algorithm based on N-P criticism, which make the complexity reduced greatly.
(5) The purpose of doing research on the performance of spectrum sensing is to make it applicable in actual. At present, the inter cell interference coordination method can not break through the regulatory spectrum architecture, and the cell edge user's rate is hard to be promoted. As a frequency programming technology, the cognitive radio technology can make the optimal resource configuration to improve the spectrum utilization by sensing the surroundings intelligently. Accordingly, a inter cell interference coordination method based on spectrum sensing technology is proposed in this paper, where, the terminal with cognitive function can automatically detect the available spectrum when requesting for service, and select the idle spectrum or the occupied spectrum with the weakest signal strength to realize the actual weakest interference communication, make the reduced inter cell interference and improved cell edge user's performance.
本文在《宽带抗干扰认知无线电系统设计研究》项目和国家重大专项(2009ZX03002-009-01)以及国家自然科学基金(60672132、60872149)的支持下,对认知无线电网络中的频谱感知技术及其应用进行了深入研究。本文主要工作及创新成果如下：
(1)在协作频谱感知(CSS)中,影响系统性能的各参数之间紧密相关,其中,检测门限是影响系统性能的一个关键因素,目前的研究中,检测门限均通过仿真获得,而没有给出明确的算法。本文首次从带宽的角度综合分析了认知无线电网络中的次用户(SU)数、检测门限、带宽、频谱利用率和次用户吞吐量之间的关系,提出了一种优化的检测门限选取算法,通过直接对数学模型求解即可得到使次用户吞吐量最大化的最优检测门限值,同时确保主用户(PU)受到足够保护。
(2)在CSS中,协作的SU数增加时,发送本地感知结果会占用较大控制信道带宽,而且当报告信道衰落严重时,CSS中的尾感知问题随着协作SU数的增加会变得更加严重,针对此问题,提出了一种基于删余的CSS(C-CSS)方案,并从次用户的角度,给出了一种可使次用户接入机会最大化的优化检测算法,研究结果表明：C-CSS可有效节约控制信道带宽,解决了报告信道存在衰落时的尾感知问题,通过利用优化算法可以在保证目标检测率的前提下获得最低的虚警率,使SU的接入机会最大化；进一步从次用户吞吐量角度研究了C-CSS的性能,分析了C-CSS方案中带宽、频谱利用率、次用户吞吐量与SU数和检测门限之间的关系,提出了C-CSS中使次用户吞吐量最大化的优化算法,研究结果表明：当SU数一定时,为了保证目标检测率,C-CSS方案相比传统CSS方案使次用户吞吐量达到最大时所要求的检测门限是不同的,C-CSS方案可以利用较少的SU数实现较高的次用户吞吐量,利用提出的优化算法可以获得最优的次用户吞吐量。
(3)基于中继协作的传输方式可以有效抵抗信道衰落对系统性能的影响,为了提高报告信道上的传输可靠性,提出了一种基于目标对象的最佳中继协作频谱感知方案(Pe-BRCS),通过最小化报告信道上的错误传输概率为目标选择最佳中继以提高感知性能,研究结果表明：相比SINR-BRCS方案,所提方案可有效降低报告信道上的错误传输概率,降低虚警率下界,可以在牺牲较少计算复杂度的条件下提高CSS性能；针对目前基于中继的CSs研究中均考虑全协作的情况,系统开销大,为了避免不必要的资源浪费,本文提出了一种自适应的最佳中继协作频谱感知方案(ABRCS),目标SU会根据其报告信道条件,自适应的选择是否需要中继协作传输,研究结果表明：所提ABRCS方案无论在SU报告信道条件较好或较差的情况下,均能实现最佳的感知性能。
(4)基于软合并的CSS相比硬合并方式可以有效提高系统检测性能,针对目前基于数据融合的软合并CSS会占用太多控制信道带宽从而导致频谱利用率低下,且没有考虑报告信道存在衰落的问题,本文考虑感知信道和报告信道均存在衰落的情况,提出了一种基于软决策融合的CSS(SC-DF-CSS)方案,推导了其检测性能表达式,并给出了优化的权重向量取值算法,结果表明：相比基于数据融合的软合并CSS,所提SC-DF-CSS仅有较小的性能损失,但可以有效节约控制信道带宽,基于修正偏差系数的优化算法可以很好地近似基于N-P准则的优化算法,大大简化了优化算法复杂度。
(5)对频谱感知性能研究的目的是为了使其更好地在实际中得到应用。目前的小区间干扰协调方法无法突破现有固定的频谱分配架构,小区边缘用户速率仍然很难得到提升,而认知无线电技术作为一种频率规划技术,可以利用频谱感知智能地感知周围环境,通过检测无线频谱的使用情况从而实现资源的优化配置、实现频谱的充分利用,提高频谱利用率。为此,本文提出了一种基于频谱感知技术的小区间干扰协调方法,具有认知功能的终端发起业务请求时,可以自动检测可用频谱上的信号强度,从而选择空闲频谱或已使用频谱信号强度最弱的频谱进行通信,实现真正的最弱干扰通信,降低小区间干扰,提升了小区边缘用户性能。
With the rapid development of wireless network, more and more radio spectrum resource will be needed. Cognitive radio is an exciting emerging technology to improve spectrum efficiency, by which, the licenced spectrum resource can be shared dynamically by cognitive user. The accurate and effective spectrum sensing is the key of realizing the cognitive radio, which are the research hotspots in wireless sphere.
Under the support of Foundation (Design and research on cognitive radio system to anti interference for military use), National Science and Technology Major Project of China (2009ZX03002-009-01) and National Science Foundation of China (60672132,60872149), the spectrum sensing technology and corresponding application in CRN are deeply investigated in this paper. The main contributions of the dissertation are as follows:
(1) The parameters influencing the system performance are closely related in cooperative spectrum sensing and detection threshold is an important factor, which is usally obtained through simulation comparison other than given algorithm. In this paper, the relationship among SU, detection threshold, bandwidth, spectrum utilization and secondary throughput in cognitive radio networks is analyzed in the point of bandwidth for the first time. An optimal algorithm for selecting detection threshold is proposed, by solving the mathematical model, the maximized secondary throughput can always be achieved while assuring sufficient protection to primary user.
(2) In CSS, more secondary user leads to much control bandwidth occupied for sending the local sensing results, and the tail sensing problem will also become serious when the reporting channel suffer from deep fading. To deal with this limitation, a censoring based CSS is proposed and an optimal detection algorithm suggested for further from the point of secondary user. The analytical results show that the control bandwidth can be effectively saved, the dependence of reporting error to sensing user number can also be reduced by C-CSS. The tail sensing problem can be effectively solved and the false alarm probability also decreased. The maximized spectrum access chance of secondary user can be realized while assuring target detection probability by optimal algorithm. In addition, C-CSS performance from the point of secondary user throughput is analyzed for further. The relationship among SU, detection threshold, bandwidth, spectrum utilization and secondary throughput is studyed and the optimal algorithm for maximizing the secondary throughput is proposed. The research results indicate that the required detection threshold for maximizing the secondary throughput is different between traditional CSS and C-CSS under given SU number while assuring object detection probability. The larger secondary throughput can be achieved with less SU in C-CSS. The optimal secondary throughput can be realized by the proposed optimal algorithm.
(3) The system performance will be degraded by channel fading, which can be effectively solved by relay cooperation. In order to improve the transmission reliability, an object based cooperative spectrum sensing scheme with best relay (Pe-BRCS) is proposed, in which the best relay is selected by minimizing the probability of reporting error to improve the sensing performance. Numerical results show that, the proposed Pe-BRCS can make the reduced reporting error probability and the false alarm probability lower bounds. The sensing performance can be improved with little computational complexity compared with SINR-BRCS. At present, the fully cooperation occasion is considered in relay cooperation in which the higher system cost is needed. In order to save the system expense in actual occasion, an adaptive cooperative spectrum sensing scheme with best relay (ABRCS) is proposed, where, the target SU can adaptively decide whether the relay's cooperation is needed according to its reporting channel condition. Results illustrate that the best sensing performance can always be achieved by ABRCS whether SU's reporting channel is good or not.
(4) The detection performance can be effectively improved by soft combination compared with hard combination in CSS. According to the deflection that much control bandwidth will be occupied in CSS based on soft combination with data fusion, which leads to the reduced spectrum utilization, in addition, the imperfect reporting channel has not been considered at present. The actual occasion with both imperfect sensing channel and reporting channel is considered in this paper. A CSS based on soft combine with decision fusion (SC-DF-CSS) is proposed and the detection performance is analyzed for further. At last, the optimal algorithm to obtain the weighting vector is given. The results show that, only a little detection performance loss exists in SC-DF-CSS compared with SC-EF-CSS. The detection performance could be effectively improved by the optimal algorithm, and the optimal algorithm based on modified deflection coefficient can achieve the same performance with the optimal algorithm based on N-P criticism, which make the complexity reduced greatly.
(5) The purpose of doing research on the performance of spectrum sensing is to make it applicable in actual. At present, the inter cell interference coordination method can not break through the regulatory spectrum architecture, and the cell edge user's rate is hard to be promoted. As a frequency programming technology, the cognitive radio technology can make the optimal resource configuration to improve the spectrum utilization by sensing the surroundings intelligently. Accordingly, a inter cell interference coordination method based on spectrum sensing technology is proposed in this paper, where, the terminal with cognitive function can automatically detect the available spectrum when requesting for service, and select the idle spectrum or the occupied spectrum with the weakest signal strength to realize the actual weakest interference communication, make the reduced inter cell interference and improved cell edge user's performance.
引文
[1]Federal Communications commission. Spectrum Policy Task Force, ET Docket no.02-135 Memorandum opinion and order. Nov 2002.
[2]张国伟.认知无线电网络中频谱感知技术研究.[博士论文],济南,山东大学,2011.
[3]McHenry M, Livsics E, Nguyen T, et al. XG dynamic spectrum access field test results, IEEE Communications Magazine,45(6), Jun.2007, pp.51-57.
[4]McHenry M A. NSF Dpectrum Occupancy Measurements Project Summary, Shared Spectrum Company Rep., Aug.2005.
[5]张雯.认知无线电频谱感知技术性能分析及优化.[博士论文],西安,西安电子科技大学,2011.
[6]Wang P and Akyildiz I F. Can Dynamic Spectrum Access Induce Heavy Tailed Delay? In 2011 IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Aachen,2011, pp.197-207.
[7]Wang P and Akyildiz I F. On the Origins of Heavy-Tailed Delay in Dynamic Spectrum Access Networks, IEEE Transactions on Mobile Computing,11(2), Febrary,2012, pp. 204-217.
[8]Akyildiz I F, Lee W Y, Vuran M C, et al. Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks:a Survey. Computer Networks Journal(Elsevier), 50(13), September 2006, pp.2127-2159.
[9]Shin K G, Hyoil K, Min A W, et.al. Cognitive Radios for Dynamic Spectrum Access:From Concept to Reality, IEEE Wireless Communications,17(6),2010, pp.64-74.
[10]Mitola J and G. Maguit Q. Cognitive Radio:Making Software Radios More Personal, IEEE Personal Communications,6(4), August 1999, pp.13-18.
[11]唐菁敏.基于协同通信的感知无线电系统传输性能研究.[博十论文],北京,北京邮电大学,2011.
[12]闫盛楠.基于协作的认知无线电频谱共享技术研究.[博士论文],北京,北京邮电大学,2011.
[13]http://www.ieee802.org/22/.
[14]IEEE 802.16's license-exempt(LE) task group, [on line], available: http://grouper.ieee802.0rg/16/e.
[15]IEEE PI900 woking group[on line], available:http://grouper.ieee. org/groups/emc/emc/1900/index.html.
[16]Oliver Holland, Markus Muck, Patricia Martigne, et al. Development of a radio enabler for reconfiguration management within the IEEE P1900.4 working group, IEEE DySPAN 2007, April 2007, pp.232-239.
[17]Markus Muck, Soodesh Buljore, Patricia Martigne, et al. IEEE P1900.B:Coexistence support for reconfigurable,heterogeneous air interfaces, IEEE DySPAN 2007, April 2007, pp.381-389.
[18]沈嘉等,3GPP长期演进(LTE)技术原理与系统设计.人民邮电出版社,2004.
[19]司江勃.无线协作传输及其在认知网络中的应用.[博士论文],西安,西安电子科技大学,2010.
[20]郭彩丽,冯春燕,曾志明.认知无线电网络技术及应用.电子工业出版社,2010.7.
[21]Haykin S. Cognitive Radio:Brain-Empowered Wireless Communications, IEEE Journal on Selected Areas in Communications,23(2), February 2005, pp.201-220.
[22]苏曦.基于认知系统中频谱特征的频谱分配与接入机制、资源优化方法研究.[博士论文],北京,北京邮电大学,2010.
[23]Brodersen R W, et al. "Coruvs:a cognitive radio approach for usage of virtual unlicensed spectrum," Berkeley Wireless Research Center (BWRC) White paper,2004.
[24]DAPRA XG WG, The XG Architectural framework V 1.0,2003.
[25]DAPRA XG WG, The XG Vision RFC V1.0,2003.
[26]FP6 End-to-End Reconfigurability (R2R Ⅱ) Integrated Project(IP), http://www.ntia.doc.gov/ osmhome/allochrt.pdf.
[27]Yucek T and Arslan H. A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications, IEEE Communications Surveys & Tutorials,11(1),2009, pp.116-130.
[28]Wang B, Liu K J R. Advances in Cognitive Radio Networks:A Survey, IEEE Journal of Selected Topics in Signal Processing,5(1),2011, pp.5-23.
[29]Rabbachin A, Quek T Q S, Hyundong S, et.al. Cognitive Network Interference, IEEE Journal on Selected Areas in Communications,29(2),2011, pp.480-493.
[30]Akyildiz I F, LeeW Y, Vuran M C, et.al. A Survey on Spectrum Management in Cognitive Radio Networks, IEEE Communications Magazine,46(4),2008, pp.40-48.
[31]Hamid M, Mohammed A and Yang Z. On Spectrum Sharing and Dynamic Spectrum Allocation:MAC Layer Spectrum Sensing in Cognitive Radio Networks, In 2010 WRI International Conference on Communications and Mobile Computing, Shenzhen, China, 2010, pp.183-187.
[32]Subramanian A P, Gupta H, S. R. Das, et.al. Fast Spectrum Allocation in Coordinated Dynamic Spectrum Access Based Cellular Networks, In 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Dublin, Ireland,2007, pp.320-330.
[33]Raman C, Yates R D and Mandayam N B. Scheduling Variable Rate Links via a Spectrum Server, in 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN), Baltimore, MD, United states,2005, pp.110-118.
[34]Zekavat S A and Li X. User-Central Wireless System:Ultimate dynamic channel allocation, In 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN), Baltimore, MD, United states,2005, pp.82-87,2005.
[35]Zheng H and Cao L. Device-Centric Spectrum Management, in 20051st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN), Baltimore, MD, United states,2005, pp.56-65.
[36]Letaief K B and Zhang W. Cooperative Communications for Cognitive Radio Networks, Proceedings of the IEEE,97(5),2009, pp.878-893.
[37]De Domenico A, Strinati E C, Di Benedetto M-G. A Survey on MAC Strategies for Cognitive Radio Networks IEEE Communications Surveys & Tutorials,14(1),2012, pp.21-44.
[38]Salami G, Durowoju O, Attar A, et.al. Comparison Between the Centralized and Distributed Approaches for Spectrum Management, IEEE Communications Surveys & Tutorials,13(2), 2011, pp.274-290.
[39]李明明.认知无线网络中MIMO关键技术研究.[博士论文],北京,北京邮电大学,2011.
[40]Ganesan G and Li Y. Cooperative Spectrum Sensing in Cognitive Radio, part I:Two user networks, IEEE Transactions on Wireless Communications,6(6),2007, pp.2204-2212.
[41]Ganesan G and Li Y. Cooperative Spectrum Sensing in Cognitive Radio, part Ⅱ:Multiuser Networks. IEEE Transactions on Wireless Communications,6(6),2007, pp.2214-2222.
[42]Simeone O, Gambini J, Bar-Ness Y, et.al. Cooperation and Cognitive Radio, In IEEE International Conference on Proc. Communications,2007. ICC'07., Glasgow,2007, pp. 6511-6515.
[43]Scutari G, Palomar D and Barbarossa S. Cognitive MIMO Radio. IEEE Signal Processing Magazine,25(6),2008, pp.46-59.
[44]Amir G and Elvino S S. Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments. In Proc. 1th IEEE Int. Symp. New Front. Dynamic Spect. Netw. DySPAN. Baltimore, MD, United states,2005, pp.131-136.
[45]Amir G and Elvino S S. Spectrum Sensing in Cognitive Radio Networks.The Cooperation-Processing Tradeoff, Wireless Communications and Mobile Computing,7(9), Nov,2007, pp,1049-1060.
[46]Amir G and Elvino S S. Opportunistic Spectrum Access in Fading Channels Through Collaborative Sensing, Journal of Communications,2(2), March,2007, pp.71-82.
[47]Cabric D, Mishra S M and Brodersen R W. Implementation Issues in Spectrum Sensing for Cognitive Radios, In Proc.38th Asilomar Conf. Signals, Syst., Computers. United States, pp.772-776.
[48]Weiss T and Jondral F. Spectrum Pooling:An Innovative Strategy for the Enhancement of Spectrum Efficiency, IEEE Communications Magazine,42(3), March,2004, pp. S8-S14.
[49]Sun C H, Zhang W, Letaief K B. Cooperative Spectrum Sensing for Cognitive Radios Under Bandwidth Constraints, In IEEE Wireless Commun. Networking Conf. WCNC. China, Kowloon,2007, pp.1-5.
[50]Sun C H, Zhang W and Letaief K B. Cluster-Based Cooperative Spectrum Sensing in Cognitive Radio Systems, In IEEE International Conference on Communications,2007. ICC '07. Glasgow,2007, pp.2511-2515.
[51]Liang Y C, Zeng Y H, Peh E, et al. Sensing-Throughput Tradeoff for Cognitive Radio Networks. IEEE Transactions on Wireless Communications,7(4), April,2008, pp. 1326-1337.
[52]Shen J Y, Jiang T, Liu, S Y, et al. Maximum Channel Throughput via Cooperative Spectrum Sensing in Cognitive Radio Networks, IEEE Transactions on Wireless Communications, 8(10), October,2009, pp.5166-5175.
[53]Zhang W and Letaief K B. Cooperative Spectrum Sensing with Transmit and Relay Diversity in Cognitive Radio Networks-[transaction letters], IEEE Transactions on Wireless Communications,7(12), December,2008, pp.4761-4766.
[54]Zhang W, Mallik R K and Letaief K B. Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks, In IEEE International Conference on Communications, China, 2008, pp.3411-3415.
[55]Hillenbrand J, Weiss T, and Jondral F K. Calculation of Detection and False Alarm Probabilities in Spectrum Pooling Systems, IEEE Communications Letters,9(4), April,2005, pp.349-351.
[56]Ganesan G and Li Y. Agility improvement through cooperative diversity in cognitive radio, in Proc. IEEE Global Telecommunications Conference. GLOBECOM'05, St. Louis, MO, United states,2005, pp.2505-2509.
[57]Nosratinia A, Hunter T E and Hedayat A. Cooperative Communication in Wireless Networks, IEEE Communications Magazine,42(10),2004, pp.74-80.
[58]Gandetto M and Regazzoni C. Spectrum sensing:A distributed approach for cognitive terminals, IEEE Journal on Selected Areas in Communications,25(3),2007, pp.546-557.
[59]Digham F F, Alouini M S and Simon M. K. On the Energy Detection of Unknown Signals Over Fading Channels, IEEE Transactions on Wireless Communications,55(1),2007, pp. 21-24.
[60]Urkowitz H. Energy Detection of Unknown Deterministic Signals, In Proc IEEE,55(4), April, 1967, pp.523-531.
[61]Kostylev V I. Energy Detection of a Signal With Random Amplitude, In Proc.2002 International Conference on Communications (ICC 2002), New York, NY, United states, 2002, pp.1606-1610.
[62]Hillenbrand J, Weiss T. and Jondral, F. K. Calculation of Detection and False Alarm Probabilities in Spectrum Pooling Systems, IEEE Communications Letters,9(4),2005, pp. 349-351.
[63]Digham F F, Alouini M S and Simon M K. On the Energy Detection of Unknown Signals over Fading Channels. In Proc.2003 International Conference on Communications (ICC 2003), Anchorage, AK, United states,2003, pp.3575-3579.
[64]Mishra S M, Sahai A and Brodersen R W. Cooperative Sensing Among Cognitive Radios. In Proc.2006 IEEE International Conference on Communications, ICC 2006, Istanbul, Turkey, 2006, pp.1658-1663.
[65]Viswanathan R and Aalo V. On Counting Rules in Distributed Detection, IEEE Transactions on Acoustics, Speech and Signal Processing,37(5), May,1989, pp.772-775.
[66]Quan Z, Cui S and Sayed A H. An Optimal Strategy for Cooperative Spectrum Sensing in Cognitive Radio Networks. In Proc.50th Annual IEEE Global Telecommunications Conference, GLOBECOM 2007, Washington, DC, United states,2007, pp.2947-2951.
[67]Kam M, Zhu Q and Gray W S. Optimal Data Fusion of Correlated Local Decisions in Multiple Sensor Detection Systems. IEEE Transactions on Aerospace and Electronic Systems, 28(3), July,1992, pp.916-920.
[68]Chen B, Jiang R X, Kasetkasem T, et,al. Channel Aware Decision Fusion in Wireless Sensor Networks, IEEE Transactions on Signal Processing,52(12), December,2004, pp.3454-3458.
[69]Chair Z, Varshney P K. Optimal Data Fusion in Multiple Sensor Detection Systems, IEEE Transactions on Aerospace and Electronic Systems, AES-22(1), January 1986, pp.98-101.
[70]Zhi Q, Cui S, Sayed A H. Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks, IEEE Journal of Selected Topics in Signal Processing,2(1),2008, pp. 28-40.
[71]Shen B and Kwak K S. Soft Combination Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks, ETRI,31(3),2009, pp.263-273.
[72]Shen B, Kwak K and Bai Z Q. Optimal Linear Soft Fusion Schemes for Cooperative Sensing in Cognitive Radio Networks. In Proc.2009 IEEE Global Telecommunications Conference, GLOBECOM 2009, Honolulu, HI, United states,2009, pp.1-6.
[73]Ma J, Zhao G D, Li Y. Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks, IEEE Transactions on Wireless Communications,7(11),2008, pp. 4502-4507.
[74]Zheng S K, Liang Y C, Kam P Y, et.al. Cross-Layered Design of Spectrum Sensing and MAC for Opportunistic Spectrum Access, in Proc.2009 IEEE Wireless Communications and Networking Conference, WCNC 2009, Budapest, Hungary,2009, pp.1-6.
[75]Pei Y Y, Hoang A T and, Liang Y C. Sensing-Throughput Tradeoff in Cognitive Radio Networks:How Frequently Should Spectrum Sensing be Carried Out? In 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC'07, Athens, Greece,2007, pp.1804-1805.
[76]Peh E C Y, Liang Y C, Guan Y L, et.al. Optimization of Cooperative Sensing in Cognitive Radio Networks:A Sensing-Throughput Tradeoff View, IEEE Transactions on Vehicular Technology,58(9),2009, pp.5294-5299.
[77]Zeng Y H and Liang Y C. Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances, IEEE Transactions on Vehicular Technology,58(4),2009, pp. 1804-1815.
[78]Laneman J N, Tse D N C and Wornell G W. Cooperative Diversity in Wireless Networks: Efficient Protocols and Outage Behavior, IEEE Transactions on Information Theory,50(12), 2004, pp.3062-3080.
[79]Hunter T E, Sanayei S and Nosratinia A. Outage Analysis of Coded Cooperation, IEEE Transactions on Information Theory,52(2), Febrary,2006, pp.375-391.
[80]Pabst R and Shultz B H. Relay-Based Deployment Concepts for Wireless and Mobile Broadband Radio. IEEE Communications Magazine,42(9), September,2004, pp.80-89.
[81]Simeone O, Bar-Ness Y and Spagnolini U. Stable Throughput of Cognitive Radios With and Without Relaying Capability. IEEE Transactions on Communications,55(12), December, 2007, pp.2351-2360.
[82]Zhang Q, Jia J C and Zhang J. Cooperative Relay to Improve Diversity in Cognitive Radio Networks, IEEE Communications Magazine,47(2), February,2009, pp.111-117.
[83]Zou Y L, Yao Y D and Zheng B Y. Outage Probability Analysis of Cognitive Transmissions: The Impact of Spectrum Sensing Overhead, IEEE transactions on Wireless Communications, 9(8), August,2010, pp.2676-2688.
[84]Zou Y L, Yao Y D and Zheng B Y. Cognitive Transmissions with Multiple Relays in Cognitive Radio Networks, IEEE Transactions on Wireless Communications,10(2), February 2011,pp.648-659.
[85]Zou Y L, Yao Y D and Zheng B Y. A Cooperative Sensing Based Cognitive Relay Transmission Scheme Without a Dedicated Sensing Relay Channel in Cognitive Radio Networks, IEEE Transactions on signal processing,59(2),2011, pp.854-858
[86]Zou Y L, Yao Y D and Zheng B Y. A Selective-Relay Based Cooperative Spectrum Sensing Scheme without Dedicated Reporting Channels in Cognitive Radio Networks. IEEE Transactions on Wireless Communications,10(4),2011, pp.1188-1198.
[87]Zhu J, Zou Y L, and Zheng B Y. Cooperative Detection for Primary User in Cognitive Radio Networks, EURASIP J. Wireless Communications And Networking,2009,doi: 101155/2009/617320 [Online]. Available: http://downloads.hindawi.com/journals/wcn/2009/617320.pdf.
[88]Zou Y L, Zhu J, Zheng B Y, et.al. An Adaptive Cooperation Diversity Scheme with Best-Relay Selection in Cognitive Radio Networks, IEEE Transactions on Signal Processing, 58(10), October,2010, pp.5438-5445.
[89]Stefania S, Issam T, Matthew B著.马霓等译LTE-UMTS长期演进理论与实践.人民邮电出版社,北京,2010.
[90]Yngve S, Tullberg H and Kronander J. Sensor Selection for Cooperative Spectrum Sensing, in 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN), Chicago, IL, United States,2008, pp.1-11.
[91]Bhargava V K. Adaptive Wireless Access System Design for Cognitive Radio Networks, In 2007 IEEE Radio and Wireless Symposium, Long Beach, CA,2007, pp.5-6.
[92]Lee W-Y and Akyildiz I F. Optimal Spectrum Sensing Framework for Cognitive Radio Networks, IEEE Transactions on Wireless Communications,7(10),2008, pp.3845-3857.
[93]Zhang W, Mallik R. and Letaief K B. Optimization of Cooperative Spectrum Sensing with Energy Detection in Cognitive Radio Networks, IEEE Transactions on Wireless Communications,8(12), December 2009, pp.5761-5766.
[94]Shen J, Liu S, Zeng L, et.al. Optimisation of Cooperative Spectrum Sensing in Cognitive Radio Network, IET Communications,3(7),2009, pp.1170-1178.
[95]BOYD S P. VANDENBERGHE L. Convex Optimization. Cambridge Univ. Press,2004.
[96]赵树杰,赵建勋.信号检测与估计理论.清华大学出版社,2005.11.
[97]RIFFITHS D V and SMITH I M. Numerical Methods for Engineers. Lodon:Blackwell Scientific,1991.
[98]Rago C, Willett P and Bar-Shalom Y. Censoring Sensors:a Low Communication-Rate Scheme for Distributed Detection, IEEE Transactions on Aerospace and Electronic Systems, 32(2),1996, pp.554-568.
[99]Jiang R X and Chen B. Fusion of censored decisions in wireless sensor networks, IEEE Transactions on Wireless Communications,4(6), November.2005, pp.2668-2673.
[100]Yiu S. Nonorthogonal Transmission and Noncoherent Fusion of Censored Decisions, IEEE Transactions on Vehicular Technology,58(1),2009, pp.263-273.
[101]Bletsas A, Shin H M, Win Z, et.al. A Simple Cooperative Diversity Method Based on Network Path Selection, IEEE Journal on Selected Areas in Communications,24(3), March, 2006, pp.659-672.
[102]Beres E and Adve R S. Selection Cooperation in Multi-Source Cooperative Networks, IEEE Transactions on Wireless Communications,7(1), January,2008, pp.118-127.
[103]Ikki S and Ahmed M H. Performance Analysis of Adaptive Decode-and-Forward Cooperative Diversity Networks with Best-Relay Selection, IEEE Transactions on Communications,58(1), January,2010, pp.68-72.
[104]Zhao Y, Adve R and Lim T J. Outage Probability at Arbitrary SNR With Cooperative Diversity, IEEE Communications letters,9(8),2005, pp.700-702.
[105]彭木根,王文博等.协同无线通信原理与应用.北京,机械工业出版社,2009.
[106]John G Proakis张力军,张宗橙,郑宝玉等译.数字通信.北京,电子工业出版社,2006.
[107]Cheng H B and Yao Y D. Cognitive-Relay-Based Intercell Interference Cancellation in Cellular Systems, IEEE Transactions on Vehicular Technology,59(4),2010, pp.1901-1909.
[108]Young J C and Shin K G. Opportunistic Access of TV Spectrum Using Cognitive-Radio-Enabled Cellular Networks, IEEE Transactions on Vehicular Technology, 60(8),2011, pp.3853-3864.
[109]3GPPTR 36.913 v.0.1.0. Requirements for further advancements forE-UTRA.
[110]Astely D, Dahlman E, Furuskar A, et.al. LTE:the Evolution of Mobile Broadband, IEEE Communications Magazine,47(4),2009, pp.44-51.
[111]Tachwali Y, Basma F and Refai H H. Cognitive Radio Architecture for Rapidly Deployable Heterogeneous Wireless Networks, IEEE Transactions on Consumer Electronics,56(3),2010, pp.1426-1432.
[112]3GPP TR 36.902 V9.3.0. Evolved Universal Terrestrial Radio Access Network(E-UTRAN); Self-configuring and self-optimizing network (SON) use cases and solutions.
[113]彭涛.基于认知无线电技术的混合网络研究.[博士论文],北京,北京邮电大学,2010.
[114]Ma Y, Kim D I and Wu Z Q. Optimization of OFDMA-Based Cellular Cognitive Radio Networks, IEEE Transactions on Communications,58(8),2010, pp.2265-2276.
[115]Choi K W, Hossain E and KimD I. Downlink Subchannel and Power Allocation in Multi-Cell OFDMA Cognitive Radio Networks, IEEE Transactions on Wireless Communications,10(7),2011, pp.2259-2271.
[116]Sawahashi M, Kishiyama Y, Morimoto A, et.al. Coordinated multipoint transmission/reception techniques for LTE-advanced [Coordinated and Distributed MIMO], IEEE Wireless Communications,17(3),2010, pp.26-34.
[117]Gabor F, Chrysostomos K, Andras R, et.al. Intercell Interference Coordination in OFDMA Networks and in the 3GPP Long Term Evolution System, Journal of Communications,4(7), August,2009, pp.445-453.
[118]3GPP Technical Specification Group Radio Access Network, Physical Layer Aspects for Evolved UTRA,3GPP TR25.814 v0.2.1,2005.
[119]3GPP Technical Specification Group Radio Access Network, Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN),3GPP, Tech. Rep. TR 25.913, Mar. 2006.
[120]Huawei, R1-050841, Further Analysis of Soft Frequency Reuse Scheme,3GPP TSG RAN WG1#42,London, UK,2005.
[121]Huawei, R1-050507, Soft Frequency Reuse Scheme for UTRAN LTE,3GPP TSG RAN WG1 Meeting #41, Athens, Greece,2005.
[122]Huawei and RITT, R1-060229, Some clarifications on soft frequency reuse scheme,3GPP, TSG-RAN WG1 Ad Hoc on LTE,2006.
[123]Jessica Heyman, Intercell Interference Management in an OFDM-based Downlink, Report, June 2006.
[124]Ekstrom H, Furuskar A, Karlsson J, et.al. Technical solutions for the 3G Long-Term Evolution, IEEE Communications Magazine,44(3), March,2006, pp.38-45.
[125]Siemens, R1-050738, Interference mitigation-Considerations and Results on Frequency Reuse, TSG-RAN WG1 Meeting #42, London, UK,2005.
[126]Siemens, R1-050599, Interference Mitigation-Considerations and Results on Frequency Reuse, TSG-RAN WG1 Ad Hoc on LTE, Sophia Antipolis, France,2005.
[127]Siemens, R1-060135, Interference Mitigation by Partial Frequency Reuse, TSG-RAN WG1 Ad Hoc Meeting on LTE, Helsinki, Finland,2006.
[128]Ericsson, R1-050764, Inter-cell Interference Handling for E-UTRA, TSG-RAN WG1 #42, London, UK,2005.
[129]LG Electronics, R1-050833, Interference Mitigation in Evolved UTRA/UTRAN,3GPP TSG RAN WG1#42, London, England,2005.
[130]Alcatel, R1-050695, Interference Coordination for Evolved UTRA Uplink Access,3GPP TSG RAN WG1#42, London, Great Britain,2005.
[131]Nokia, R1-050813, UL interference control considerations,3GPP TSG-RAN WG1 Meeting #42, London, UK,2005.
[132]Texas Instruments, R1-051059, Inter-Cell Interference Mitigation for EUTRA,3GPP TSG RAN WG1, San Diego, USA,2005.
[133]Nokia, R1-060291, OFDMA Downlink Inter-Cell Interference Mitigation,3GPP, TSG-RAN WG1#44,2006.
[134]R1-050896, Description and Simulations of Interference Management Technique for OFDMA based E-UTRA Downlink Evaluation, Qualcomm Europe,2005.
[135]Texas Instruments,R1-060368, Performance of Inter-Cell Interference Mitigation with Semi-Static Frequency Planning for EUTRA Downlink,3GPP TSG RAN WG1#44, Denver, USA,2006.
[136]Ericsson, R1-061374, Downlink Inter-Cell Interference Co-ordination/Avoidance-Evaluation of Frequency Reuse, TSG-RAN WG1#45, Shanghai, China,2006.
[137]Ericsson, R1-061378, E-UTRA Bandwidth Flexibility, TSG-RAN WG1#45, Shanghai, China,2006.
[138]李美玲.软频率复用技术研究及其在LTE系统中的应用.[硕士论文],北京,北京邮电大学,2007.
[139]专利号：200810101751.7 "OFDMA蜂窝系统中基十虚小区的半静态干扰协调方法”
[140]专利号：200910076946.5“一种半静态干扰协调方法”.
[141]专利号：201110093697.8“一种进行干扰协调的方法、系统和设备”
[142]蔡淘等译.无线通信原理与应用.电子工业出版社,1999.11.
[2]张国伟.认知无线电网络中频谱感知技术研究.[博士论文],济南,山东大学,2011.
[3]McHenry M, Livsics E, Nguyen T, et al. XG dynamic spectrum access field test results, IEEE Communications Magazine,45(6), Jun.2007, pp.51-57.
[4]McHenry M A. NSF Dpectrum Occupancy Measurements Project Summary, Shared Spectrum Company Rep., Aug.2005.
[5]张雯.认知无线电频谱感知技术性能分析及优化.[博士论文],西安,西安电子科技大学,2011.
[6]Wang P and Akyildiz I F. Can Dynamic Spectrum Access Induce Heavy Tailed Delay? In 2011 IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks (DySPAN), Aachen,2011, pp.197-207.
[7]Wang P and Akyildiz I F. On the Origins of Heavy-Tailed Delay in Dynamic Spectrum Access Networks, IEEE Transactions on Mobile Computing,11(2), Febrary,2012, pp. 204-217.
[8]Akyildiz I F, Lee W Y, Vuran M C, et al. Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks:a Survey. Computer Networks Journal(Elsevier), 50(13), September 2006, pp.2127-2159.
[9]Shin K G, Hyoil K, Min A W, et.al. Cognitive Radios for Dynamic Spectrum Access:From Concept to Reality, IEEE Wireless Communications,17(6),2010, pp.64-74.
[10]Mitola J and G. Maguit Q. Cognitive Radio:Making Software Radios More Personal, IEEE Personal Communications,6(4), August 1999, pp.13-18.
[11]唐菁敏.基于协同通信的感知无线电系统传输性能研究.[博十论文],北京,北京邮电大学,2011.
[12]闫盛楠.基于协作的认知无线电频谱共享技术研究.[博士论文],北京,北京邮电大学,2011.
[13]http://www.ieee802.org/22/.
[14]IEEE 802.16's license-exempt(LE) task group, [on line], available: http://grouper.ieee802.0rg/16/e.
[15]IEEE PI900 woking group[on line], available:http://grouper.ieee. org/groups/emc/emc/1900/index.html.
[16]Oliver Holland, Markus Muck, Patricia Martigne, et al. Development of a radio enabler for reconfiguration management within the IEEE P1900.4 working group, IEEE DySPAN 2007, April 2007, pp.232-239.
[17]Markus Muck, Soodesh Buljore, Patricia Martigne, et al. IEEE P1900.B:Coexistence support for reconfigurable,heterogeneous air interfaces, IEEE DySPAN 2007, April 2007, pp.381-389.
[18]沈嘉等,3GPP长期演进(LTE)技术原理与系统设计.人民邮电出版社,2004.
[19]司江勃.无线协作传输及其在认知网络中的应用.[博士论文],西安,西安电子科技大学,2010.
[20]郭彩丽,冯春燕,曾志明.认知无线电网络技术及应用.电子工业出版社,2010.7.
[21]Haykin S. Cognitive Radio:Brain-Empowered Wireless Communications, IEEE Journal on Selected Areas in Communications,23(2), February 2005, pp.201-220.
[22]苏曦.基于认知系统中频谱特征的频谱分配与接入机制、资源优化方法研究.[博士论文],北京,北京邮电大学,2010.
[23]Brodersen R W, et al. "Coruvs:a cognitive radio approach for usage of virtual unlicensed spectrum," Berkeley Wireless Research Center (BWRC) White paper,2004.
[24]DAPRA XG WG, The XG Architectural framework V 1.0,2003.
[25]DAPRA XG WG, The XG Vision RFC V1.0,2003.
[26]FP6 End-to-End Reconfigurability (R2R Ⅱ) Integrated Project(IP), http://www.ntia.doc.gov/ osmhome/allochrt.pdf.
[27]Yucek T and Arslan H. A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications, IEEE Communications Surveys & Tutorials,11(1),2009, pp.116-130.
[28]Wang B, Liu K J R. Advances in Cognitive Radio Networks:A Survey, IEEE Journal of Selected Topics in Signal Processing,5(1),2011, pp.5-23.
[29]Rabbachin A, Quek T Q S, Hyundong S, et.al. Cognitive Network Interference, IEEE Journal on Selected Areas in Communications,29(2),2011, pp.480-493.
[30]Akyildiz I F, LeeW Y, Vuran M C, et.al. A Survey on Spectrum Management in Cognitive Radio Networks, IEEE Communications Magazine,46(4),2008, pp.40-48.
[31]Hamid M, Mohammed A and Yang Z. On Spectrum Sharing and Dynamic Spectrum Allocation:MAC Layer Spectrum Sensing in Cognitive Radio Networks, In 2010 WRI International Conference on Communications and Mobile Computing, Shenzhen, China, 2010, pp.183-187.
[32]Subramanian A P, Gupta H, S. R. Das, et.al. Fast Spectrum Allocation in Coordinated Dynamic Spectrum Access Based Cellular Networks, In 2007 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, Dublin, Ireland,2007, pp.320-330.
[33]Raman C, Yates R D and Mandayam N B. Scheduling Variable Rate Links via a Spectrum Server, in 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN), Baltimore, MD, United states,2005, pp.110-118.
[34]Zekavat S A and Li X. User-Central Wireless System:Ultimate dynamic channel allocation, In 2005 1st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN), Baltimore, MD, United states,2005, pp.82-87,2005.
[35]Zheng H and Cao L. Device-Centric Spectrum Management, in 20051st IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN), Baltimore, MD, United states,2005, pp.56-65.
[36]Letaief K B and Zhang W. Cooperative Communications for Cognitive Radio Networks, Proceedings of the IEEE,97(5),2009, pp.878-893.
[37]De Domenico A, Strinati E C, Di Benedetto M-G. A Survey on MAC Strategies for Cognitive Radio Networks IEEE Communications Surveys & Tutorials,14(1),2012, pp.21-44.
[38]Salami G, Durowoju O, Attar A, et.al. Comparison Between the Centralized and Distributed Approaches for Spectrum Management, IEEE Communications Surveys & Tutorials,13(2), 2011, pp.274-290.
[39]李明明.认知无线网络中MIMO关键技术研究.[博士论文],北京,北京邮电大学,2011.
[40]Ganesan G and Li Y. Cooperative Spectrum Sensing in Cognitive Radio, part I:Two user networks, IEEE Transactions on Wireless Communications,6(6),2007, pp.2204-2212.
[41]Ganesan G and Li Y. Cooperative Spectrum Sensing in Cognitive Radio, part Ⅱ:Multiuser Networks. IEEE Transactions on Wireless Communications,6(6),2007, pp.2214-2222.
[42]Simeone O, Gambini J, Bar-Ness Y, et.al. Cooperation and Cognitive Radio, In IEEE International Conference on Proc. Communications,2007. ICC'07., Glasgow,2007, pp. 6511-6515.
[43]Scutari G, Palomar D and Barbarossa S. Cognitive MIMO Radio. IEEE Signal Processing Magazine,25(6),2008, pp.46-59.
[44]Amir G and Elvino S S. Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments. In Proc. 1th IEEE Int. Symp. New Front. Dynamic Spect. Netw. DySPAN. Baltimore, MD, United states,2005, pp.131-136.
[45]Amir G and Elvino S S. Spectrum Sensing in Cognitive Radio Networks.The Cooperation-Processing Tradeoff, Wireless Communications and Mobile Computing,7(9), Nov,2007, pp,1049-1060.
[46]Amir G and Elvino S S. Opportunistic Spectrum Access in Fading Channels Through Collaborative Sensing, Journal of Communications,2(2), March,2007, pp.71-82.
[47]Cabric D, Mishra S M and Brodersen R W. Implementation Issues in Spectrum Sensing for Cognitive Radios, In Proc.38th Asilomar Conf. Signals, Syst., Computers. United States, pp.772-776.
[48]Weiss T and Jondral F. Spectrum Pooling:An Innovative Strategy for the Enhancement of Spectrum Efficiency, IEEE Communications Magazine,42(3), March,2004, pp. S8-S14.
[49]Sun C H, Zhang W, Letaief K B. Cooperative Spectrum Sensing for Cognitive Radios Under Bandwidth Constraints, In IEEE Wireless Commun. Networking Conf. WCNC. China, Kowloon,2007, pp.1-5.
[50]Sun C H, Zhang W and Letaief K B. Cluster-Based Cooperative Spectrum Sensing in Cognitive Radio Systems, In IEEE International Conference on Communications,2007. ICC '07. Glasgow,2007, pp.2511-2515.
[51]Liang Y C, Zeng Y H, Peh E, et al. Sensing-Throughput Tradeoff for Cognitive Radio Networks. IEEE Transactions on Wireless Communications,7(4), April,2008, pp. 1326-1337.
[52]Shen J Y, Jiang T, Liu, S Y, et al. Maximum Channel Throughput via Cooperative Spectrum Sensing in Cognitive Radio Networks, IEEE Transactions on Wireless Communications, 8(10), October,2009, pp.5166-5175.
[53]Zhang W and Letaief K B. Cooperative Spectrum Sensing with Transmit and Relay Diversity in Cognitive Radio Networks-[transaction letters], IEEE Transactions on Wireless Communications,7(12), December,2008, pp.4761-4766.
[54]Zhang W, Mallik R K and Letaief K B. Cooperative Spectrum Sensing Optimization in Cognitive Radio Networks, In IEEE International Conference on Communications, China, 2008, pp.3411-3415.
[55]Hillenbrand J, Weiss T, and Jondral F K. Calculation of Detection and False Alarm Probabilities in Spectrum Pooling Systems, IEEE Communications Letters,9(4), April,2005, pp.349-351.
[56]Ganesan G and Li Y. Agility improvement through cooperative diversity in cognitive radio, in Proc. IEEE Global Telecommunications Conference. GLOBECOM'05, St. Louis, MO, United states,2005, pp.2505-2509.
[57]Nosratinia A, Hunter T E and Hedayat A. Cooperative Communication in Wireless Networks, IEEE Communications Magazine,42(10),2004, pp.74-80.
[58]Gandetto M and Regazzoni C. Spectrum sensing:A distributed approach for cognitive terminals, IEEE Journal on Selected Areas in Communications,25(3),2007, pp.546-557.
[59]Digham F F, Alouini M S and Simon M. K. On the Energy Detection of Unknown Signals Over Fading Channels, IEEE Transactions on Wireless Communications,55(1),2007, pp. 21-24.
[60]Urkowitz H. Energy Detection of Unknown Deterministic Signals, In Proc IEEE,55(4), April, 1967, pp.523-531.
[61]Kostylev V I. Energy Detection of a Signal With Random Amplitude, In Proc.2002 International Conference on Communications (ICC 2002), New York, NY, United states, 2002, pp.1606-1610.
[62]Hillenbrand J, Weiss T. and Jondral, F. K. Calculation of Detection and False Alarm Probabilities in Spectrum Pooling Systems, IEEE Communications Letters,9(4),2005, pp. 349-351.
[63]Digham F F, Alouini M S and Simon M K. On the Energy Detection of Unknown Signals over Fading Channels. In Proc.2003 International Conference on Communications (ICC 2003), Anchorage, AK, United states,2003, pp.3575-3579.
[64]Mishra S M, Sahai A and Brodersen R W. Cooperative Sensing Among Cognitive Radios. In Proc.2006 IEEE International Conference on Communications, ICC 2006, Istanbul, Turkey, 2006, pp.1658-1663.
[65]Viswanathan R and Aalo V. On Counting Rules in Distributed Detection, IEEE Transactions on Acoustics, Speech and Signal Processing,37(5), May,1989, pp.772-775.
[66]Quan Z, Cui S and Sayed A H. An Optimal Strategy for Cooperative Spectrum Sensing in Cognitive Radio Networks. In Proc.50th Annual IEEE Global Telecommunications Conference, GLOBECOM 2007, Washington, DC, United states,2007, pp.2947-2951.
[67]Kam M, Zhu Q and Gray W S. Optimal Data Fusion of Correlated Local Decisions in Multiple Sensor Detection Systems. IEEE Transactions on Aerospace and Electronic Systems, 28(3), July,1992, pp.916-920.
[68]Chen B, Jiang R X, Kasetkasem T, et,al. Channel Aware Decision Fusion in Wireless Sensor Networks, IEEE Transactions on Signal Processing,52(12), December,2004, pp.3454-3458.
[69]Chair Z, Varshney P K. Optimal Data Fusion in Multiple Sensor Detection Systems, IEEE Transactions on Aerospace and Electronic Systems, AES-22(1), January 1986, pp.98-101.
[70]Zhi Q, Cui S, Sayed A H. Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks, IEEE Journal of Selected Topics in Signal Processing,2(1),2008, pp. 28-40.
[71]Shen B and Kwak K S. Soft Combination Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks, ETRI,31(3),2009, pp.263-273.
[72]Shen B, Kwak K and Bai Z Q. Optimal Linear Soft Fusion Schemes for Cooperative Sensing in Cognitive Radio Networks. In Proc.2009 IEEE Global Telecommunications Conference, GLOBECOM 2009, Honolulu, HI, United states,2009, pp.1-6.
[73]Ma J, Zhao G D, Li Y. Soft Combination and Detection for Cooperative Spectrum Sensing in Cognitive Radio Networks, IEEE Transactions on Wireless Communications,7(11),2008, pp. 4502-4507.
[74]Zheng S K, Liang Y C, Kam P Y, et.al. Cross-Layered Design of Spectrum Sensing and MAC for Opportunistic Spectrum Access, in Proc.2009 IEEE Wireless Communications and Networking Conference, WCNC 2009, Budapest, Hungary,2009, pp.1-6.
[75]Pei Y Y, Hoang A T and, Liang Y C. Sensing-Throughput Tradeoff in Cognitive Radio Networks:How Frequently Should Spectrum Sensing be Carried Out? In 18th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC'07, Athens, Greece,2007, pp.1804-1805.
[76]Peh E C Y, Liang Y C, Guan Y L, et.al. Optimization of Cooperative Sensing in Cognitive Radio Networks:A Sensing-Throughput Tradeoff View, IEEE Transactions on Vehicular Technology,58(9),2009, pp.5294-5299.
[77]Zeng Y H and Liang Y C. Spectrum-Sensing Algorithms for Cognitive Radio Based on Statistical Covariances, IEEE Transactions on Vehicular Technology,58(4),2009, pp. 1804-1815.
[78]Laneman J N, Tse D N C and Wornell G W. Cooperative Diversity in Wireless Networks: Efficient Protocols and Outage Behavior, IEEE Transactions on Information Theory,50(12), 2004, pp.3062-3080.
[79]Hunter T E, Sanayei S and Nosratinia A. Outage Analysis of Coded Cooperation, IEEE Transactions on Information Theory,52(2), Febrary,2006, pp.375-391.
[80]Pabst R and Shultz B H. Relay-Based Deployment Concepts for Wireless and Mobile Broadband Radio. IEEE Communications Magazine,42(9), September,2004, pp.80-89.
[81]Simeone O, Bar-Ness Y and Spagnolini U. Stable Throughput of Cognitive Radios With and Without Relaying Capability. IEEE Transactions on Communications,55(12), December, 2007, pp.2351-2360.
[82]Zhang Q, Jia J C and Zhang J. Cooperative Relay to Improve Diversity in Cognitive Radio Networks, IEEE Communications Magazine,47(2), February,2009, pp.111-117.
[83]Zou Y L, Yao Y D and Zheng B Y. Outage Probability Analysis of Cognitive Transmissions: The Impact of Spectrum Sensing Overhead, IEEE transactions on Wireless Communications, 9(8), August,2010, pp.2676-2688.
[84]Zou Y L, Yao Y D and Zheng B Y. Cognitive Transmissions with Multiple Relays in Cognitive Radio Networks, IEEE Transactions on Wireless Communications,10(2), February 2011,pp.648-659.
[85]Zou Y L, Yao Y D and Zheng B Y. A Cooperative Sensing Based Cognitive Relay Transmission Scheme Without a Dedicated Sensing Relay Channel in Cognitive Radio Networks, IEEE Transactions on signal processing,59(2),2011, pp.854-858
[86]Zou Y L, Yao Y D and Zheng B Y. A Selective-Relay Based Cooperative Spectrum Sensing Scheme without Dedicated Reporting Channels in Cognitive Radio Networks. IEEE Transactions on Wireless Communications,10(4),2011, pp.1188-1198.
[87]Zhu J, Zou Y L, and Zheng B Y. Cooperative Detection for Primary User in Cognitive Radio Networks, EURASIP J. Wireless Communications And Networking,2009,doi: 101155/2009/617320 [Online]. Available: http://downloads.hindawi.com/journals/wcn/2009/617320.pdf.
[88]Zou Y L, Zhu J, Zheng B Y, et.al. An Adaptive Cooperation Diversity Scheme with Best-Relay Selection in Cognitive Radio Networks, IEEE Transactions on Signal Processing, 58(10), October,2010, pp.5438-5445.
[89]Stefania S, Issam T, Matthew B著.马霓等译LTE-UMTS长期演进理论与实践.人民邮电出版社,北京,2010.
[90]Yngve S, Tullberg H and Kronander J. Sensor Selection for Cooperative Spectrum Sensing, in 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks(DySPAN), Chicago, IL, United States,2008, pp.1-11.
[91]Bhargava V K. Adaptive Wireless Access System Design for Cognitive Radio Networks, In 2007 IEEE Radio and Wireless Symposium, Long Beach, CA,2007, pp.5-6.
[92]Lee W-Y and Akyildiz I F. Optimal Spectrum Sensing Framework for Cognitive Radio Networks, IEEE Transactions on Wireless Communications,7(10),2008, pp.3845-3857.
[93]Zhang W, Mallik R. and Letaief K B. Optimization of Cooperative Spectrum Sensing with Energy Detection in Cognitive Radio Networks, IEEE Transactions on Wireless Communications,8(12), December 2009, pp.5761-5766.
[94]Shen J, Liu S, Zeng L, et.al. Optimisation of Cooperative Spectrum Sensing in Cognitive Radio Network, IET Communications,3(7),2009, pp.1170-1178.
[95]BOYD S P. VANDENBERGHE L. Convex Optimization. Cambridge Univ. Press,2004.
[96]赵树杰,赵建勋.信号检测与估计理论.清华大学出版社,2005.11.
[97]RIFFITHS D V and SMITH I M. Numerical Methods for Engineers. Lodon:Blackwell Scientific,1991.
[98]Rago C, Willett P and Bar-Shalom Y. Censoring Sensors:a Low Communication-Rate Scheme for Distributed Detection, IEEE Transactions on Aerospace and Electronic Systems, 32(2),1996, pp.554-568.
[99]Jiang R X and Chen B. Fusion of censored decisions in wireless sensor networks, IEEE Transactions on Wireless Communications,4(6), November.2005, pp.2668-2673.
[100]Yiu S. Nonorthogonal Transmission and Noncoherent Fusion of Censored Decisions, IEEE Transactions on Vehicular Technology,58(1),2009, pp.263-273.
[101]Bletsas A, Shin H M, Win Z, et.al. A Simple Cooperative Diversity Method Based on Network Path Selection, IEEE Journal on Selected Areas in Communications,24(3), March, 2006, pp.659-672.
[102]Beres E and Adve R S. Selection Cooperation in Multi-Source Cooperative Networks, IEEE Transactions on Wireless Communications,7(1), January,2008, pp.118-127.
[103]Ikki S and Ahmed M H. Performance Analysis of Adaptive Decode-and-Forward Cooperative Diversity Networks with Best-Relay Selection, IEEE Transactions on Communications,58(1), January,2010, pp.68-72.
[104]Zhao Y, Adve R and Lim T J. Outage Probability at Arbitrary SNR With Cooperative Diversity, IEEE Communications letters,9(8),2005, pp.700-702.
[105]彭木根,王文博等.协同无线通信原理与应用.北京,机械工业出版社,2009.
[106]John G Proakis张力军,张宗橙,郑宝玉等译.数字通信.北京,电子工业出版社,2006.
[107]Cheng H B and Yao Y D. Cognitive-Relay-Based Intercell Interference Cancellation in Cellular Systems, IEEE Transactions on Vehicular Technology,59(4),2010, pp.1901-1909.
[108]Young J C and Shin K G. Opportunistic Access of TV Spectrum Using Cognitive-Radio-Enabled Cellular Networks, IEEE Transactions on Vehicular Technology, 60(8),2011, pp.3853-3864.
[109]3GPPTR 36.913 v.0.1.0. Requirements for further advancements forE-UTRA.
[110]Astely D, Dahlman E, Furuskar A, et.al. LTE:the Evolution of Mobile Broadband, IEEE Communications Magazine,47(4),2009, pp.44-51.
[111]Tachwali Y, Basma F and Refai H H. Cognitive Radio Architecture for Rapidly Deployable Heterogeneous Wireless Networks, IEEE Transactions on Consumer Electronics,56(3),2010, pp.1426-1432.
[112]3GPP TR 36.902 V9.3.0. Evolved Universal Terrestrial Radio Access Network(E-UTRAN); Self-configuring and self-optimizing network (SON) use cases and solutions.
[113]彭涛.基于认知无线电技术的混合网络研究.[博士论文],北京,北京邮电大学,2010.
[114]Ma Y, Kim D I and Wu Z Q. Optimization of OFDMA-Based Cellular Cognitive Radio Networks, IEEE Transactions on Communications,58(8),2010, pp.2265-2276.
[115]Choi K W, Hossain E and KimD I. Downlink Subchannel and Power Allocation in Multi-Cell OFDMA Cognitive Radio Networks, IEEE Transactions on Wireless Communications,10(7),2011, pp.2259-2271.
[116]Sawahashi M, Kishiyama Y, Morimoto A, et.al. Coordinated multipoint transmission/reception techniques for LTE-advanced [Coordinated and Distributed MIMO], IEEE Wireless Communications,17(3),2010, pp.26-34.
[117]Gabor F, Chrysostomos K, Andras R, et.al. Intercell Interference Coordination in OFDMA Networks and in the 3GPP Long Term Evolution System, Journal of Communications,4(7), August,2009, pp.445-453.
[118]3GPP Technical Specification Group Radio Access Network, Physical Layer Aspects for Evolved UTRA,3GPP TR25.814 v0.2.1,2005.
[119]3GPP Technical Specification Group Radio Access Network, Requirements for Evolved UTRA (E-UTRA) and Evolved UTRAN (E-UTRAN),3GPP, Tech. Rep. TR 25.913, Mar. 2006.
[120]Huawei, R1-050841, Further Analysis of Soft Frequency Reuse Scheme,3GPP TSG RAN WG1#42,London, UK,2005.
[121]Huawei, R1-050507, Soft Frequency Reuse Scheme for UTRAN LTE,3GPP TSG RAN WG1 Meeting #41, Athens, Greece,2005.
[122]Huawei and RITT, R1-060229, Some clarifications on soft frequency reuse scheme,3GPP, TSG-RAN WG1 Ad Hoc on LTE,2006.
[123]Jessica Heyman, Intercell Interference Management in an OFDM-based Downlink, Report, June 2006.
[124]Ekstrom H, Furuskar A, Karlsson J, et.al. Technical solutions for the 3G Long-Term Evolution, IEEE Communications Magazine,44(3), March,2006, pp.38-45.
[125]Siemens, R1-050738, Interference mitigation-Considerations and Results on Frequency Reuse, TSG-RAN WG1 Meeting #42, London, UK,2005.
[126]Siemens, R1-050599, Interference Mitigation-Considerations and Results on Frequency Reuse, TSG-RAN WG1 Ad Hoc on LTE, Sophia Antipolis, France,2005.
[127]Siemens, R1-060135, Interference Mitigation by Partial Frequency Reuse, TSG-RAN WG1 Ad Hoc Meeting on LTE, Helsinki, Finland,2006.
[128]Ericsson, R1-050764, Inter-cell Interference Handling for E-UTRA, TSG-RAN WG1 #42, London, UK,2005.
[129]LG Electronics, R1-050833, Interference Mitigation in Evolved UTRA/UTRAN,3GPP TSG RAN WG1#42, London, England,2005.
[130]Alcatel, R1-050695, Interference Coordination for Evolved UTRA Uplink Access,3GPP TSG RAN WG1#42, London, Great Britain,2005.
[131]Nokia, R1-050813, UL interference control considerations,3GPP TSG-RAN WG1 Meeting #42, London, UK,2005.
[132]Texas Instruments, R1-051059, Inter-Cell Interference Mitigation for EUTRA,3GPP TSG RAN WG1, San Diego, USA,2005.
[133]Nokia, R1-060291, OFDMA Downlink Inter-Cell Interference Mitigation,3GPP, TSG-RAN WG1#44,2006.
[134]R1-050896, Description and Simulations of Interference Management Technique for OFDMA based E-UTRA Downlink Evaluation, Qualcomm Europe,2005.
[135]Texas Instruments,R1-060368, Performance of Inter-Cell Interference Mitigation with Semi-Static Frequency Planning for EUTRA Downlink,3GPP TSG RAN WG1#44, Denver, USA,2006.
[136]Ericsson, R1-061374, Downlink Inter-Cell Interference Co-ordination/Avoidance-Evaluation of Frequency Reuse, TSG-RAN WG1#45, Shanghai, China,2006.
[137]Ericsson, R1-061378, E-UTRA Bandwidth Flexibility, TSG-RAN WG1#45, Shanghai, China,2006.
[138]李美玲.软频率复用技术研究及其在LTE系统中的应用.[硕士论文],北京,北京邮电大学,2007.
[139]专利号：200810101751.7 "OFDMA蜂窝系统中基十虚小区的半静态干扰协调方法”
[140]专利号：200910076946.5“一种半静态干扰协调方法”.
[141]专利号：201110093697.8“一种进行干扰协调的方法、系统和设备”
[142]蔡淘等译.无线通信原理与应用.电子工业出版社,1999.11.