多天线频谱感知与接入模型协议研究
摘要
无线技术由于可以灵活方便地接入通信网络而备受青睐。据Erikson全球移动公司报道,2013年手机用户数达到了75亿,这一数字已超过当前70亿的世界人口数。无线通信已成为服务技术的主流且规模正在不断扩大,人们不得不在公用网内开发云端技术来处理大数据量的传输。随着网络用户向移动互联网的转移和物联网技术的发展,无线传输的容量和可靠性被赋予了更高的要求。然而如何提高频谱利用率、采用有限的频谱资源承载这些业务是无线通信技术进一步发展必须要解决的首要问题。
固定的频谱分配政策是造成频谱利用率低下的主要原因。2003年美国联邦通信委员会(FCC)和国际电信联盟(ITU)等频谱机构发现各地区的频谱利用率非常低下。为了提高认知系统的频谱使用效率和对频谱环境的适应能力,无线网络需要具有传感能力。1999年美国提出了通过谱传感利用频谱的认知无线电标准,采取动态频谱管理技术允许非授权用户伺机接入授权用户的空闲频谱而实现对频谱的有效利用。据称该技术可以完全解决频谱匮乏的问题,已被认为是未来无线网络的核心技术之一。
动态利用频谱需要解决频谱感知和频谱接入两个方面的问题。前者要求系统对空闲频谱实施快速而准确的检测；后者要求用户能自适应地接入频谱空洞,并在发现主用户的情况下快速地切换到其他信道。隐终端和无线衰落等不利的环境因素使得用户在相互覆盖范围内无法发现潜在的通信冲突,使得感知性能大打折扣；同时当网络负载较大时,频谱共享系统的服务质量会下降。本文对这两个方面涉及的理论与技术问题进行深入探索,主要工作如下：
首先,介绍认知无线电的研究背景,说明当前频谱危机和授权频谱利用率低下的现实。然后对认知无线电技术的概念、特点、各种标准、项目和应用前景进行了阐述。
第二,研究了多天线分集合并技术和频谱感知技术,并提出一种多天线的智能谱感知方法,该方法融合了匹配滤波器检测、能量检测和循环平稳检测算法。通过假设信道存在高斯噪声和平坦Nakagami衰落而建立了相关的二元假设检验模型,并推导出瞬时检测概率的封闭表达式；根据SC、EGC、MRC三种多天线合并方式下的幅度增益的随机统计特性,分别解析了瞬时信噪比对应的概率分布。通过将瞬时检测概率在其概率密度函数上进行平均而导出精确的或近似的平均检测概率表达式,同时根据各检测算法的事件概率计算出平均检测时间的表达式。其后利用MATLAB的数值计算工具分析了检测概率和检测时间的ROC特性。
第三,研究动态频谱接入的协议和模型。提出了一种基于服务期损失制轮询调度的有中心混合频谱接入方案,次用户根据感知结果和干扰温度判断主用户的活动状态,并概率式地接入信道。通过构建Nakagami信道下的MAC层排队模型,解析了次用户的平均服务率的封闭表达式。为了保证主用户的服务质量,规定主用户具有最大容忍延时,以此为约束条件分析了平均服务率的理论最大值。由于次用户的业务类型往往多样化,该部分又进一步研究了次用户系统具有多队列排队结构的情形,考虑了两种调度方案：第一种为概率式调度方案,采用拉格朗日乘子法得到了各个队列的接入概率；第二种是损失制服务轮询调度机制,在传统的门限服务、限定1服务和完全／门限两级优先级服务三种的轮询模型中假设有顾客离开,然后采用嵌入式Markov链和概率母函数的分析方法建立了与此调度方案相吻合的数学模型及其函数关系式,并精确解析出系统特性参数(平均排队队长、平均循环周期、平均等待时延)的表达式。其后利用MATLAB的数值计算工具分析了以上参数的ROC特性。
第四,基于OPNET软件,根据前节的多天线感知和轮询频谱接入方案设计了认知无线电感知接入仿真平台,包含三个方面：在物理层,用Simulink搭建信号衰落、加性高斯信道、产生PU信号类型等过程；在MAC层,用OPNET搭建节点模块、数据源产生模块、队列模块；物理层和MAC层通过OPNET和MATLAB的联合仿真引擎接口进行信息交互。然后利用该实验平台对设定的仿真场景下的频谱利用率、平均排队队长和平均等待时延等相关参数进行了测试研究。
Wireless technology is favored since it could access communication networks flexibly and easily. According to the report of global mobile company Erikson, the number of mobile phone users has reached7.5billion in2013, which has exceeded the current world population of7billion. Wireless communication has become the mainstream of service technology and its scale is expanding continuously, and thus people have to develop cloud technology in the public network to handle the large amount of data transmission. With the shift of network users to mobile Internet and the development of the technology of Net of Things, the higher demands about capacity and reliability of wireless transmission were put forward. Therefore, it is the primary issue of how to improve the spectrum efficiency and use limited spectrum resource to carry these businesses as the further development of wireless communication technology.
Fixed spectrum allocation policy is the chief cause of low spectrum efficiency. In2003, Federal Communications Commission (FCC), International Telecommunication Union (ITU) and other spectrum institutions found that the spectral efficiency for all regions was very low. In order to improve spectral efficiency and the adaptability in spectrum environment of cognitive system, wireless networks need to have the capability of sensing. In1999, the United States proposed the Cognitive Radio standard of using spectrum through spectrum sensing, which allowed the unauthorized user to access to idle spectrum of authorized user opportunistically using dynamic management techniques, and to use the spectrum effectively. It is reported that the problem of spectrum shortage could be completely solved, and Cognitive Radio is considered to be one of the core technologies in future wireless network.
To use spectrum dynamically, the problem of spectrum sensing and spectrum access should be solved. The former requires the system implementing rapid and accurate detection to idle spectrum; the latter requires users accessing spectrum holes adaptively, and switching to another channel quickly when the primary user was found. Some adverse environmental factors, such as hidden terminal and wireless fading, will make users can not find potential communicate conflicts in the mutual coverage, and make wireless sensing performance reduce greatly; Meanwhile, the quality of service of spectrum sharing system will decline when the network load becomes large. This dissertation deeply explored the theory and technical issues involved in the above two aspects. The main work is listed as follows:
First of all, the research background of Cognitive Radio was introduced, which indicated that the reality of the current spectrum crisis and low utilization rate of authorized spectrum. Then the concept, features, standards, projects and prospects of Cognitive Radio technology were described.
Second of all, the multi-antenna diversity combining technology and spectrum sensing technology were studied, and a multi-antenna intelligent spectrum sensing method was proposed, which combined the Matched Filter Detection, Energy Detection and Cyclostationary Detection algorithms. The relevant Binary Hypothesis Testing model was established through assuming the presence of Gaussian noise and flat Nakagami fading channel, and the closed expressions of instantaneous detection probabilities were deduced; According to the random statistical properties of amplitude gain under the three multi-antenna combine modes of SC, EGC, MRC, the probability distribution characteristic of instantaneous signal to noise ratio was analyzed separately. By averaging the instantaneous detect probability on the probability density function of instantaneous signal to noise ratio, the exact or approximate average detection probability expression was derived, and the expression of average detection time was also obtained according to the event Probability of every detection algorithm. Subsequently, the ROC characteristics of detection probability and detection time were analyzed by MATLAB numerical calculation tool.
Third of all, the protocol and model of Dynamic Spectrum Access were studied. A centred and mixed spectrum access scheme based on losing service polling scheduling was proposed, in which Second Users judge the active state of primary user according to the results of sensing and interfere temperature and then access channel by a probability. By constructing the MAC layer queuing model under Nakagami channel, the closed expression of average service rate of Second User was analyzed. In order to ensure the quality of service for Primary Users, we specified the Primary User who has the maximum tolerable delay, in which the theoretical maximum Average Service Rate was analyzed. Because the business type of Second User was diverse, the case of multi-queue structure in second user system was further investigated, in which two scheduling schemes was considered:the first one was probabilistic scheduling scheme, and the access probability of each queue was obtained according to Lagrange Multiplier Method; the second one was the losing service polling scheduling mechanism, in which we supposed that the customer will leave in the three traditional polling models of Gated service, Limit1service and Exhaustive-Gated two levels priority service. The mathematical model and function relationship was established using the analytical method of embedded Markov chain and Probability Generating Function, and the expression of system parameters, such as Average Queue Length, Average Cycle Time, Average Waiting Time, were precisely parse out. Subsequently, the ROC characteristics above were analyzed by MATLAB numerical calculation tool.
Finally, based on OPNET software, Cognitive Radio sensing and access simulation platform was designed according to multi-antenna spectrum sensing and polling spectrum access scheme above, which included three aspects:in physical layer, the process of signal fading, additive Gaussian channel, generate primary user signal types were build with Simulink; in MAC layer, node module, data sources generating module and queue module were build with OPNET. Physical layer and MAC layer exchange information through OPNET and MATLAB co-simulation engine interface. Then we used the experimental platform to test and research the relevant parameters by setting a simulation scenario, including spectrum utilization, Average Queue Length and Average Waiting Time.
固定的频谱分配政策是造成频谱利用率低下的主要原因。2003年美国联邦通信委员会(FCC)和国际电信联盟(ITU)等频谱机构发现各地区的频谱利用率非常低下。为了提高认知系统的频谱使用效率和对频谱环境的适应能力,无线网络需要具有传感能力。1999年美国提出了通过谱传感利用频谱的认知无线电标准,采取动态频谱管理技术允许非授权用户伺机接入授权用户的空闲频谱而实现对频谱的有效利用。据称该技术可以完全解决频谱匮乏的问题,已被认为是未来无线网络的核心技术之一。
动态利用频谱需要解决频谱感知和频谱接入两个方面的问题。前者要求系统对空闲频谱实施快速而准确的检测；后者要求用户能自适应地接入频谱空洞,并在发现主用户的情况下快速地切换到其他信道。隐终端和无线衰落等不利的环境因素使得用户在相互覆盖范围内无法发现潜在的通信冲突,使得感知性能大打折扣；同时当网络负载较大时,频谱共享系统的服务质量会下降。本文对这两个方面涉及的理论与技术问题进行深入探索,主要工作如下：
首先,介绍认知无线电的研究背景,说明当前频谱危机和授权频谱利用率低下的现实。然后对认知无线电技术的概念、特点、各种标准、项目和应用前景进行了阐述。
第二,研究了多天线分集合并技术和频谱感知技术,并提出一种多天线的智能谱感知方法,该方法融合了匹配滤波器检测、能量检测和循环平稳检测算法。通过假设信道存在高斯噪声和平坦Nakagami衰落而建立了相关的二元假设检验模型,并推导出瞬时检测概率的封闭表达式；根据SC、EGC、MRC三种多天线合并方式下的幅度增益的随机统计特性,分别解析了瞬时信噪比对应的概率分布。通过将瞬时检测概率在其概率密度函数上进行平均而导出精确的或近似的平均检测概率表达式,同时根据各检测算法的事件概率计算出平均检测时间的表达式。其后利用MATLAB的数值计算工具分析了检测概率和检测时间的ROC特性。
第三,研究动态频谱接入的协议和模型。提出了一种基于服务期损失制轮询调度的有中心混合频谱接入方案,次用户根据感知结果和干扰温度判断主用户的活动状态,并概率式地接入信道。通过构建Nakagami信道下的MAC层排队模型,解析了次用户的平均服务率的封闭表达式。为了保证主用户的服务质量,规定主用户具有最大容忍延时,以此为约束条件分析了平均服务率的理论最大值。由于次用户的业务类型往往多样化,该部分又进一步研究了次用户系统具有多队列排队结构的情形,考虑了两种调度方案：第一种为概率式调度方案,采用拉格朗日乘子法得到了各个队列的接入概率；第二种是损失制服务轮询调度机制,在传统的门限服务、限定1服务和完全／门限两级优先级服务三种的轮询模型中假设有顾客离开,然后采用嵌入式Markov链和概率母函数的分析方法建立了与此调度方案相吻合的数学模型及其函数关系式,并精确解析出系统特性参数(平均排队队长、平均循环周期、平均等待时延)的表达式。其后利用MATLAB的数值计算工具分析了以上参数的ROC特性。
第四,基于OPNET软件,根据前节的多天线感知和轮询频谱接入方案设计了认知无线电感知接入仿真平台,包含三个方面：在物理层,用Simulink搭建信号衰落、加性高斯信道、产生PU信号类型等过程；在MAC层,用OPNET搭建节点模块、数据源产生模块、队列模块；物理层和MAC层通过OPNET和MATLAB的联合仿真引擎接口进行信息交互。然后利用该实验平台对设定的仿真场景下的频谱利用率、平均排队队长和平均等待时延等相关参数进行了测试研究。
Wireless technology is favored since it could access communication networks flexibly and easily. According to the report of global mobile company Erikson, the number of mobile phone users has reached7.5billion in2013, which has exceeded the current world population of7billion. Wireless communication has become the mainstream of service technology and its scale is expanding continuously, and thus people have to develop cloud technology in the public network to handle the large amount of data transmission. With the shift of network users to mobile Internet and the development of the technology of Net of Things, the higher demands about capacity and reliability of wireless transmission were put forward. Therefore, it is the primary issue of how to improve the spectrum efficiency and use limited spectrum resource to carry these businesses as the further development of wireless communication technology.
Fixed spectrum allocation policy is the chief cause of low spectrum efficiency. In2003, Federal Communications Commission (FCC), International Telecommunication Union (ITU) and other spectrum institutions found that the spectral efficiency for all regions was very low. In order to improve spectral efficiency and the adaptability in spectrum environment of cognitive system, wireless networks need to have the capability of sensing. In1999, the United States proposed the Cognitive Radio standard of using spectrum through spectrum sensing, which allowed the unauthorized user to access to idle spectrum of authorized user opportunistically using dynamic management techniques, and to use the spectrum effectively. It is reported that the problem of spectrum shortage could be completely solved, and Cognitive Radio is considered to be one of the core technologies in future wireless network.
To use spectrum dynamically, the problem of spectrum sensing and spectrum access should be solved. The former requires the system implementing rapid and accurate detection to idle spectrum; the latter requires users accessing spectrum holes adaptively, and switching to another channel quickly when the primary user was found. Some adverse environmental factors, such as hidden terminal and wireless fading, will make users can not find potential communicate conflicts in the mutual coverage, and make wireless sensing performance reduce greatly; Meanwhile, the quality of service of spectrum sharing system will decline when the network load becomes large. This dissertation deeply explored the theory and technical issues involved in the above two aspects. The main work is listed as follows:
First of all, the research background of Cognitive Radio was introduced, which indicated that the reality of the current spectrum crisis and low utilization rate of authorized spectrum. Then the concept, features, standards, projects and prospects of Cognitive Radio technology were described.
Second of all, the multi-antenna diversity combining technology and spectrum sensing technology were studied, and a multi-antenna intelligent spectrum sensing method was proposed, which combined the Matched Filter Detection, Energy Detection and Cyclostationary Detection algorithms. The relevant Binary Hypothesis Testing model was established through assuming the presence of Gaussian noise and flat Nakagami fading channel, and the closed expressions of instantaneous detection probabilities were deduced; According to the random statistical properties of amplitude gain under the three multi-antenna combine modes of SC, EGC, MRC, the probability distribution characteristic of instantaneous signal to noise ratio was analyzed separately. By averaging the instantaneous detect probability on the probability density function of instantaneous signal to noise ratio, the exact or approximate average detection probability expression was derived, and the expression of average detection time was also obtained according to the event Probability of every detection algorithm. Subsequently, the ROC characteristics of detection probability and detection time were analyzed by MATLAB numerical calculation tool.
Third of all, the protocol and model of Dynamic Spectrum Access were studied. A centred and mixed spectrum access scheme based on losing service polling scheduling was proposed, in which Second Users judge the active state of primary user according to the results of sensing and interfere temperature and then access channel by a probability. By constructing the MAC layer queuing model under Nakagami channel, the closed expression of average service rate of Second User was analyzed. In order to ensure the quality of service for Primary Users, we specified the Primary User who has the maximum tolerable delay, in which the theoretical maximum Average Service Rate was analyzed. Because the business type of Second User was diverse, the case of multi-queue structure in second user system was further investigated, in which two scheduling schemes was considered:the first one was probabilistic scheduling scheme, and the access probability of each queue was obtained according to Lagrange Multiplier Method; the second one was the losing service polling scheduling mechanism, in which we supposed that the customer will leave in the three traditional polling models of Gated service, Limit1service and Exhaustive-Gated two levels priority service. The mathematical model and function relationship was established using the analytical method of embedded Markov chain and Probability Generating Function, and the expression of system parameters, such as Average Queue Length, Average Cycle Time, Average Waiting Time, were precisely parse out. Subsequently, the ROC characteristics above were analyzed by MATLAB numerical calculation tool.
Finally, based on OPNET software, Cognitive Radio sensing and access simulation platform was designed according to multi-antenna spectrum sensing and polling spectrum access scheme above, which included three aspects:in physical layer, the process of signal fading, additive Gaussian channel, generate primary user signal types were build with Simulink; in MAC layer, node module, data sources generating module and queue module were build with OPNET. Physical layer and MAC layer exchange information through OPNET and MATLAB co-simulation engine interface. Then we used the experimental platform to test and research the relevant parameters by setting a simulation scenario, including spectrum utilization, Average Queue Length and Average Waiting Time.
引文
[1]Regulations R. International Telecommunication Union[J]. Radiocommunication Sector. ITU-R. Geneva.2008.
[2]Yang H., Wilson D. Term Paper Spectrum Crunch Worldwide Issue[J]. Spectrum.2012,12:13.
[3]M.2078 I.-R. Spectrum requirements for the future development of IMT-2000 and IMT-Advanced[R].2006.
[4]郭永明,李宁,李军芳.未来智能无线电将对频谱管理带来深刻影响[J].电信科学.2012,28(7)：94-100.
[5]McHenry M. A. NSF spectrum occupancy measurements project summary[J]. Shared spectrum company report.2005.
[6]Patil K., Skouby K., Chandra A., et al. Spectrum occupancy statistics in the context of cognitive radio[C]. in Wireless Personal Multimedia Communications (WPMC),2011 14th International Symposium on.2011. IEEE.
[7]Dzulkifli M. R., Kamarudin M. R., Abdul Rahman T. Spectrum occupancy at UHF TV band for cognitive radio applications[C]. in RF and Microwave Conference (RFM),2011 IEEE International.2011. IEEE.
[8]Mehdawi M., Riley N., Ammar M., et al. Comparing historical and current spectrum occupancy measurements in the context of cognitive radio[C]. in Telecommunications Forum (TELFOR), 2012 20th.2012. IEEE.
[9]Xue J., Feng Z., Zhang P. Spectrum Occupancy Measurements and Analysis in Beijing[C]. in Electronic Engineering and Computer Science,2013 International Conference on 2013.
[10]Force F. S. P. T. Report of the spectrum efficiency working group[R].2002. Nov.
[11]Yin L., Yin S.-x., Wang S., et al. Quantitative spectrum occupancy evaluation in China:based on a large scale concurrent spectrum measurement[J]. The Journal of China Universities of Posts and Telecommunications.2012,19(3):122-128.
[12]Chen D., Yang J., Wu J., et al. Spectrum Occupancy Analysis Based on Radio Monitoring Network[C]. in ICCC2012, Beijing.2012.
[13]李红岩.认知无线电的若干关键技术研究[D].2009.北京邮电大学博士论文
[14]Mitola Ⅲ J., Maguire Jr G. Q. Cognitive radio:making software radios more personal[J]. Personal Communications, IEEE.1999,6(4):13-18.
[15]Staple G., Werbach K. IEEE spectrum:the end of spectrum scarcity[R].2004.
[16]Group I. W. W. IEEE 802.22-2011 Standard for Cognitive Wireless Regional Area Networks (RAN) for Operation in TV Bands[R].2011. Piscataway, NJ:IEEE Standards Association.
[17]Zhu J., Yin H. Enabling collocated coexistence in IEEE 802.16 networks via perceived concurrency[J]. Communications Magazine, IEEE.2009,47(6):108-114.
[18]Korakis T., Ercetin O., Krishnamurthy S., et al. Link quality based association mechanism in IEEE 802.11 h compliant wireless LANs[J]. Power.2005,87:5.
[19]Smith S. Essentials of an 802.11 y network-The recently approved standard will allow for high-powered Wi-Fi-enabled communications at distances of 3 miles or more[J]. Cabling Installation & Maintenance.2009,17(1):27.
[20]Prasad R. V, Pawelczak P., Hoffineyer J. A., et al. Cognitive functionality in next generation wireless networks:standardization efforts[J]. Communications Magazine, IEEE.2008,46(4): 72-78.
[21]Weiss T. A., Jondral F. K. Spectrum pooling:an innovative strategy for the enhancement of spectrum efficiency[J]. Communications Magazine, IEEE.2004,42(3):S8-14.
[22]Brodersen R. W., Wolisz A., Cabric D., et al. Corvus:a cognitive radio approach for usage of virtual unlicensed spectrum[J]. Berkeley Wireless Research Center (BWRC) White paper.2004.
[23]Huschke J., Leaves P. Dynamic radio for IP-services in vehicular environments (DRiVE)[R].
[24]OVERDRIVE I. Project,"Spectrum efficient uni-and multicast services over dynamic multi-radio networks in vehicular environments."[R].
[25]Bourse D., Muck M., Simon O., et al. End-to-end reconfigurability (E2R Ⅱ):Management and control of adaptive communication systems[C]. in 1ST Mobile Summit.2006.
[26]Buddhikot M. M., Kolodzy P., Miller S., et al. DIMSUMnet:new directions in wireless networking using coordinated dynamic spectrum[C]. in World of Wireless Mobile and Multimedia Networks,2005. WoWMoM 2005. Sixth IEEE International Symposium on a.2005. Ieee.
[27]Darpa. "The XG Vision Request for Comments" [J]. XG Working Group.2003, Version 2.
[28]Lockerd A., Breazeal C. Tutelage and socially guided robot learning[C]. in Intelligent Robots and Systems,2004.(IROS 2004). Proceedings.2004 IEEE/RSJ International Conference on.2004. IEEE.
[29]Zhang Q., Kokkeler A. B., Smit G. J. A reconfigurable radio architecture for cognitive radio in emergency networks[C]. in Wireless Technology,2006. The 9th European Conference on.2006. IEEE.
[30]Wang J., Ghosh M., Challapali K. Emerging cognitive radio applications:A survey[J]. Communications Magazine, IEEE.2011,49(3):74-81.
[31]宋志群,刘玉涛,王荆宁.认知无线电技术及其应用[M].2012：国防工业出版社.
[32]Robey F. C., Fuhrmann D. R., Kelly E. J., et al. A CFAR adaptive matched filter detector[J]. Aerospace and Electronic Systems, IEEE Transactions on.1992,28(1):208-216.
[33]Urkowitz H. Energy detection of unknown deterministic signals[J]. Proceedings of the IEEE.1967, 55(4):523-531.
[34]Ye Z., Grosspietsch J., Memik G. Spectrum sensing using cyclostationary spectrum density for cognitive radios[C]. in Signal Processing Systems,2007 IEEE Workshop on.2007. IEEE.
[35]Luo L., Neihart N. M., Roy S., et al. A two-stage sensing technique for dynamic spectrum access[J]. Wireless Communications, IEEE Transactions on.2009,8(6):3028-3037.
[36]Ejaz W., ul Hasan N., Azam M. A., et al. Improved local spectrum sensing for cognitive radio networks[J]. EURASIP Journal on Advances in Signal Processing.2012,2012(1):1-12.
[37]Ejaz W, Hasan N., Kim H. S. SNR-based adaptive spectrum sensing for cognitive radio networks[J]. Int. J. Innov. Comput. Inf. Control.2012,8(9):6095-6106.
[38]Nair P. R., Vinod A. P., Smitha K. G., et al. Fast two-stage spectrum detector for cognitive radios in uncertain noise channels[J]. IET communications.2012,6(11):1341-1348.
[39]FCC E. Docket No 03-222 Notice of proposed rule making and order[R].2003. December.
[40]Elahi I., Muhammad K., Balsara P. T. IIP2 and DC offsets in the presence of leakage at LO frequency[J]. Circuits and Systems Ⅱ:Express Briefs, IEEE Transactions on.2006,53(8): 647-651.
[41]Quan Z., Cui S., Sayed A. H. Optimal linear cooperation for spectrum sensing in cognitive radio networks[J]. Selected Topics in Signal Processing, IEEE Journal of.2008,2(1):28-40.
[42]Do T., Mark B. L. Joint spatial-temporal spectrum sensing for cognitive radio networks[J]. Vehicular Technology, IEEE Transactions on.2010,59(7):3480-3490.
[43]Kortun A., Ratnarajah T., Sellathurai M., et al. On the performance of eigenvalue-based cooperative spectrum sensing for cognitive radio[J]. Selected Topics in Signal Processing, IEEE Journal of.2011,5(1):49-55.
[44]石磊,周正,唐亮,等.认知无线电网络中压缩协作频谱感知[J].北京邮电大学学报.2011,34(5)：76-79.
[45]邹玉龙.认知无线电网络中协作中继技术研究[D].2012.南京邮电大学博士论文
[46]岳文静,陈志,郑宝玉,等.基于可靠次用户信息的协作频谱感知算法研究[J].电子与信息学报.2012,34(5)：1208-1213.
[47]Saberinia E. A Practical Multi-Bit Data Combining Strategy for Centralized Cooperative Spectrum Sensing[J].2013.
[48]杨黎,王晓湘,赵堃.多天线感知无线电中的协作频谱感知算法[J].电子与信息学报.2009,31(10)：2338-2342.
[49]Wang H., Wang H., Zhang L., et al. Cyclostationarity analysis on EBPSK and its optimizations[J]. Chinese Journal of Radio Science.2010,25(1):42-46.
[50]Alghamdi O. A., Ahmed M. Z. Optimal and suboptimal multi antenna spectrum sensing techniques with master node cooperation for cognitive radio systems[J]. Journal of Communications.2011, 6(7):512-523.
[51]Wenshan Y., Pinyi R., Zhou S., et al. A multiple antenna spectrum sensing scheme based on space and time diversity in cognitive radios[J]. IEICE Transactions on Communications.2011,94(5): 1254-1264.
[52]Tugnait J. K. On multiple antenna spectrum sensing under noise variance uncertainty and flat fading[J]. Signal Processing, IEEE Transactions on.2012.60(4):1823-1832.
[53]Shen L., Wang H., Zhang W., et al. Multiple Antennas Assisted Blind Spectrum Sensing in Cognitive Radio Channels[J]. Communications Letters, IEEE.2012,16(1):92-94.
[54]Singh A., Bhatnagar M. R., Mallik R. K. Cooperative spectrum sensing in multiple antenna based cognitive radio network using an improved energy detector[J]. Communications Letters, IEEE. 2012,16(1):64-67.
[55]赵友轩,周游,胡捍英,等.多天线认知系统中最优协作用户数的选择研究[J].信号处理.2013,29(5)：625-631.
[56]Tabesh A., Taherpour A., Pourgharehkhan Z. Multiple antenna spectrum sensing using statistical a-priori information in cognitive radios[J]. IET Signal Processing.2013,7(1):59-70.
[57]Ejaz W., ul Hasan N., Lee S., et al, I3S:intelligent spectrum sensing scheme for cognitive radio networks[J]. EURASIP Journal on Wireless Communications and Networking.2013,2013(1):26.
[58]Simon M. K., Alouini M.-S. A unified approach to the performance analysis of digital communication over generalized fading channels[J]. Proceedings of the IEEE.1998,86(9): 1860-1877.
[59]Yue W., Zheng B. Spectrum sensing algorithms for primary detection based on reliability in cognitive radio systems[J]. Computers & Electrical Engineering.2010,36(3):469-479.
[60]陈星,贺志强,吴伟陵.基于循环平稳的多天线感知无线电频谱检测[J].北京邮电大学学报.2008,31(2)：85-89.
[61]Zhang Q. A generic correlated Nakagami fading model for wireless communications[J]. Communications, IEEE Transactions on.2003,51(11):1745-1748.
[62]Karagiannidis G. K. Moments-based approach to the performance analysis of equal gain diversity in Nakagami-m fading[J]. Communications, IEEE Transactions on.2004,52(5):685-690.
[63]Zlatanov N., Hadzi-Velkov Z., Karagiannidis G. An efficient approximation to the correlated Nakagami-m sums and its application in equal gain diversity receivers[J]. Wireless Communications, IEEE Transactions on.2010,9(1):302-310.
[64]Digham F. F., Alouini M.-S., Simon M. K. On the energy detection of unknown signals over fading channels[J]. Communications, IEEE Transactions on.2007,55(1):21-24.
[65]张雯,杨家玮,闫琦,等.认知无线电中Nakagami信道下等增益合作检测性能分析[J].北京邮电大学学报.2010,33(006)：93-97.
[66]Chennakeshu S., Hassan A., Ozarow L. Average SNR interpretation for diversity reception[J]. Electronics Letters.1994,30(20):1641-1642.
[67]Yacoub M. D., da Silva C. M., Vargas Bautista J. E. Second-order statistics for diversity-combining techniques in Nakagami-fading channels[J]. Vehicular Technology, IEEE Transactions on.2001,50(6):1464-1470.
[68]Rahman M. A., Harada H. New exact closed-form PDF of the sum of Nakagami-m random variables with applications[J]. Communications, IEEE Transactions on.2011,59(2):395-401.
[69]da Costa D. B., Yacoub M. D., Santos Filho J. An Improved Closed-Form Approximation to the Sum of Arbitrary Nakagami-< formula formulatype=[J]. Vehicular Technology, IEEE Transactions on.2008,57(6):3854-3858.
[70]Yacoub M. D. The α-μ distribution:A physical fading model for the Stacy distribution[J]. Vehicular Technology, IEEE Transactions on.2007,56(1):27-34.
[71]Dharmawansa P., Rajatheva N., Ahmed K. On the distribution of the sum of Nakagami-m random variables[J]. IEEE transactions on communications.2007,55(7):1407-1416.
[72]胡罡.无线认知网络频谱感知与接入关键技术研究[D].2010.国防科学技术大学博士论文
[73]Song M., Xin C., Zhao Y., et al. Dynamic spectrum access:from cognitive radio to network radio[J]. Wireless Communications, IEEE.2012,19(1):23-29.
[74]Ren P., Wang Y., Du Q., et al. A survey on dynamic spectrum access protocols for distributed cognitive wireless networks[J]. EURASIP J. Wireless Comm. and Networking.2012,2012:60.
[75]Xing Y., Chandramouli R., Mangold S. Dynamic spectrum access in open spectrum wireless networks[J]. Selected Areas in Communications, IEEE Journal on.2006,24(3):626-637.
[76]郭彩丽,曾志民,冯春燕,等.机会频谱接入系统的切换请求排队机制及性能分析[J].电子与信息学报.2009,31(6)：1505-1508.
[77]Lee H., Cho D.-H. Capacity improvement and analysis of VoIP service in a cognitive radio system[J]. Vehicular Technology, IEEE Transactions on.2010,59(4):1646-1651.
[78]Li H., Han Z. Socially optimal queuing control in cognitive radio networks subject to service interruptions:To queue or not to queue?[J]. Wireless Communications, IEEE Transactions on. 2011,10(5):1656-1666.
[79]Wang L.-C., Wang C.-W., Feng K.-T. A queueing-theoretical framework for QoS-enhanced spectrum management in cognitive radio networks[J]. Wireless Communications, IEEE.2011, 18(6):18-26.
[80]Wang L.-C., Wang C.-W., Adachi F. Load-balancing spectrum decision for cognitive radio networks[J]. Selected Areas in Communications, IEEE Journal on.2011,29(4):757-769.
[81]Wang L.-C., Wang C.-W., Chang C.-J. Modeling and analysis for spectrum handoffs in cognitive radio networks[J]. Mobile Computing, IEEE Transactions on.2012,11(9):1499-1513.
[82]王玲,彭启琮,彭启航.基于收益的认知无线电多信道阶梯排队接入机制分析[J].电子与信息学报.2012,34：8.
[83]Tumuluru V. K., Wang P., Niyato D., et al. Performance analysis of cognitive radio spectrum access with prioritized traffic[J]. Vehicular Technology, IEEE Transactions on.2012,61(4): 1895-1906.
[84]Chu T., Phan H., Zepernick H. On the Performance of Underlay Cognitive Radio Networks Using M/G/1/K Queueing Model [J].2013.
[85]Jiang C, Chen Y., Liu K., et al. Renewal-Theoretical Dynamic Spectrum Access in Cognitive Radio Network with Unknown Primary Behavior[J]. Selected Areas in Communications, IEEE Journal on.2013,31(3):406-416.
[86]朱平.认知无线电关键技术研究[D].2010.中国科学技术大学博士论文
[87]铮官.离散时间并行调度两级服务与非对称门限服务轮询系统研究[D].2012.云南大学博士论文
[88]赵东风,丁洪伟,赵一帆,等.多级门限服务轮询系统MAC离散时间控制协议模型分析[J]电子学报.2010,38(7)：1495-1499.
[89]Saffer Z., Telek M. Unified analysis of BMAP/G/l cyclic polling models[J]. Queueing Systems. 2010,64(1):69-102.
[90]van der Mei R. D., Roubos A. Polling models with multi-phase gated service[J]. Annals of Operations Research.2012,198(1):25-56.
[91]De Haan, Roland Boucherie, Richard J, et al. Time-Limited Polling Systems With Batch Arrivals And Phase-Type Service Times[J].
[92]Chernova N., Foss S., Kim B. On the stability of a polling system with an adaptive service mechanism[J]. Annals of Operations Research.2012,198(1):125-144.
[93]Leskela L., Unger F. Stability of a spatial polling system with greedy myopic service[J]. Annals of Operations Research.2012,198(1):165-183.
[94]Kavitha V., Altman E. Continuous Polling models and application to ferry assisted WLAN[J]. Annals of Operations Research.2012,198(1):185-218.
[95]Boon M. A., Adan I. J.-B. F., Boxma O. J. A polling model with multiple priority levels[J]. Performance Evaluation.2010,67(6):468-484.
[96]陈佩树,朱翼隽,耿响.具有强占优先权的不耐烦顾客的M/M/m/k排队模型[J].系统工程与电子技术.2008,30(6)：1069-1073.
[97]Melikov A. Z., Fattakhova M. I. Simulation of wireless cellular networks with impatient calls[J]. Autcmatic Control and Computer Sciences.2010,44(1):31-41.
[98]彭懿.杨向群,吴锦标.带负顾客和不耐烦顾客的离散时间Geo/G/1重试排队[J].系统工程理论与实践.2011,31(12)：2373-2379.
[99]Sakuma Y., Inoie A. Stationary distribution of a multi-server vacation queue with constant impatient times[J]. Operations Research Letters.2012,40(4):239-243.
[100]Boon M. A. A polling model with reneging at polling instants[J]. Annals of Operations Research.2012,198(1)：5-23.
[101]Kim J., Kim B., Kang J. Discrete-time multiserver queue with impatient customers [J]. Electronics Letters.2013,49(1):38-39.
[102]鲁进,黄铭,杨志军.基于轮询调度的频谱接入机制研究[J].电信科学.2013,29(12)：60-65.
[103]Hu J., Yang L.-L., Hanzo L. Maximum Average Service Rate and Optimal Queue Scheduling of Delay-Constrained Hybrid Cognitive Radio in Nakagami Fading Channels[J]. IEEE Transactions on Vehicular Technology.2013.
[104]Lu J.,Ding H.. Zhou D., et al. Analysis of a Simplified Gated Polling Scheduling Model Based on Clustering Routing[C]. in Computer Science and Network Technology (1CCSNT),2012 International Conference on.2012.
[105]Lu J., Ding H., Zhao Y, et al. A Novel Simplified Limit-1 Polling Communication System[J]. Advanced Materials Research.2013,756:2300-2305.
[106]Lu J., Ding H., Zhou D., et al. Research on E-SG Mixed Polling Model Based on two Kinds of Businesses[C]. in Computer Science and Network Technology (ICCSNT),2012 International Conference on.2012.
[107]杨志军,赵东风,丁洪伟,等.两级优先级控制轮询系统研究[J].电子学报.2009(007)：1452-1456.
[108]Sharma A. K. Throughput Improvement Of Cognitive Radio Network Using Orthogonal Frequency Division Multiplexing[J]. International Journal of Advances in Engineering Sciences. 2011, 1(3):15-18.
[109]Young A. R., Bostian C. W. Simple and Low-Cost Platfonns for Cognitive Radio Experiments [Application Notes][J]. Microwave Magazine, IEEE.2013,14(1):146-157.
[110]Avila J., Thenmozhi K. Simulink Based Spectrum Sensing[J]. International Journal of Engineering & Technology (0975-4024).2013,5(2).
[111]Aravind H., Gandhiraj R., Soman K., et al. Spectrum Sensing Implementations for Software Defined Radio in Simulink[J]. Procedia Engineering.2012,30:1119-1128.
[112]Ding H., Yang J., Zhao Z. Adaptive dual threshold cooperative spectrum sensing based on grouping[J]. Journal of Jilin University(Engineering and Technology Edition).2011,41(2): 537-542.
[113]Sun J.-C. Automatic real-time spectrum sensing using energy detection in software defined radio[D].2012. California State University
[114]Tabassam A. A., Suleman M., Kalsait S., et al. Building cognitive radios in MATLAB Simulink-A step towards future wireless technology[C]. in Wireless Advanced (WiAd),2011. 2011. IEEE.
[115]宋连波.基于IEEE 802.22的WRAN系统级仿真平台设计与研究[D].2009.北京邮电大学
[116]王润亮.认知无线电频谱感知与用户监测关键技术研究[D].2012.解放军信息工程大学
[117]刘忠军,徐友云,李默.多PC协同下的认知网络仿真平台设计[J]. Computer Engineering.2011,37(1).
[118]孙剑锋.认知无线电关键技术研究及其在家庭基站中的应用[D].2012.北京邮电大学博士论文
[119]Gardellin V, Das S. K., Lenzini L. Self-coexistence in cellular cognitive radio networks based on the IEEE 802.22 standard[J]. Wireless Communications, IEEE.2013,20(2):52-59.
[120]Kang K.-M., Park J. C., Cho S.-I., et al. Deployment and coverage of cognitive radio networks in TV white space[J]. Communications Magazine, IEEE.2012,50(12):88-94.
[121]Ray G. G., Dandapat S. K. Applications and Simulation of Femtocells in a Cognitive Radio Environment[D].2012.
[122]Yang L., Zhang Z., Hou W., et al. Papyrus:A software platform for distributed dynamic spectrum sharing using SDRs[J]. ACM SIGCOMM Computer Communication Review.2011,41(1):31-37.
[123]Chen Y.-S., Cho C.-H., You I., et al. A cross-layer protocol of spectrum mobility and handover in cognitive LTE networks[J]. Simulation Modelling Practice and Theory.2011,19(8):1723-1744.
[124]高嵩.OPNET Modeler仿真建模大解密[M].2010.北京：电子工业出版社.
[125]史迩冬.小型认知无线网络的设计和实现[D].2012.苏州大学
[126]夏东瀛.认知无线电系统中频谱分配算法的研究[D].2013.东华大学
[127]陈寅.认知网络中二级用户访问策略的研究[D].2012.湖北大学
[128]马慧生.多跳认知无线电网络路由技术研究[D].2009.西安电子科技大学
[129]韩艳锋.动态频谱共享无线通信系统性能分析及验证[D].2011.电子科技大学
[130]Dham V. Link establishment in ad hoc networks using smart antennas[D].2003. Citeseer
[131]刘学勇.详解MATLAB/simulink通信系统建模与仿真[M].2011：电子工业出版社.
[132]刘维.实战Matlab之并行程序设计[M].2012.北京航空航天大学出版社.
[2]Yang H., Wilson D. Term Paper Spectrum Crunch Worldwide Issue[J]. Spectrum.2012,12:13.
[3]M.2078 I.-R. Spectrum requirements for the future development of IMT-2000 and IMT-Advanced[R].2006.
[4]郭永明,李宁,李军芳.未来智能无线电将对频谱管理带来深刻影响[J].电信科学.2012,28(7)：94-100.
[5]McHenry M. A. NSF spectrum occupancy measurements project summary[J]. Shared spectrum company report.2005.
[6]Patil K., Skouby K., Chandra A., et al. Spectrum occupancy statistics in the context of cognitive radio[C]. in Wireless Personal Multimedia Communications (WPMC),2011 14th International Symposium on.2011. IEEE.
[7]Dzulkifli M. R., Kamarudin M. R., Abdul Rahman T. Spectrum occupancy at UHF TV band for cognitive radio applications[C]. in RF and Microwave Conference (RFM),2011 IEEE International.2011. IEEE.
[8]Mehdawi M., Riley N., Ammar M., et al. Comparing historical and current spectrum occupancy measurements in the context of cognitive radio[C]. in Telecommunications Forum (TELFOR), 2012 20th.2012. IEEE.
[9]Xue J., Feng Z., Zhang P. Spectrum Occupancy Measurements and Analysis in Beijing[C]. in Electronic Engineering and Computer Science,2013 International Conference on 2013.
[10]Force F. S. P. T. Report of the spectrum efficiency working group[R].2002. Nov.
[11]Yin L., Yin S.-x., Wang S., et al. Quantitative spectrum occupancy evaluation in China:based on a large scale concurrent spectrum measurement[J]. The Journal of China Universities of Posts and Telecommunications.2012,19(3):122-128.
[12]Chen D., Yang J., Wu J., et al. Spectrum Occupancy Analysis Based on Radio Monitoring Network[C]. in ICCC2012, Beijing.2012.
[13]李红岩.认知无线电的若干关键技术研究[D].2009.北京邮电大学博士论文
[14]Mitola Ⅲ J., Maguire Jr G. Q. Cognitive radio:making software radios more personal[J]. Personal Communications, IEEE.1999,6(4):13-18.
[15]Staple G., Werbach K. IEEE spectrum:the end of spectrum scarcity[R].2004.
[16]Group I. W. W. IEEE 802.22-2011 Standard for Cognitive Wireless Regional Area Networks (RAN) for Operation in TV Bands[R].2011. Piscataway, NJ:IEEE Standards Association.
[17]Zhu J., Yin H. Enabling collocated coexistence in IEEE 802.16 networks via perceived concurrency[J]. Communications Magazine, IEEE.2009,47(6):108-114.
[18]Korakis T., Ercetin O., Krishnamurthy S., et al. Link quality based association mechanism in IEEE 802.11 h compliant wireless LANs[J]. Power.2005,87:5.
[19]Smith S. Essentials of an 802.11 y network-The recently approved standard will allow for high-powered Wi-Fi-enabled communications at distances of 3 miles or more[J]. Cabling Installation & Maintenance.2009,17(1):27.
[20]Prasad R. V, Pawelczak P., Hoffineyer J. A., et al. Cognitive functionality in next generation wireless networks:standardization efforts[J]. Communications Magazine, IEEE.2008,46(4): 72-78.
[21]Weiss T. A., Jondral F. K. Spectrum pooling:an innovative strategy for the enhancement of spectrum efficiency[J]. Communications Magazine, IEEE.2004,42(3):S8-14.
[22]Brodersen R. W., Wolisz A., Cabric D., et al. Corvus:a cognitive radio approach for usage of virtual unlicensed spectrum[J]. Berkeley Wireless Research Center (BWRC) White paper.2004.
[23]Huschke J., Leaves P. Dynamic radio for IP-services in vehicular environments (DRiVE)[R].
[24]OVERDRIVE I. Project,"Spectrum efficient uni-and multicast services over dynamic multi-radio networks in vehicular environments."[R].
[25]Bourse D., Muck M., Simon O., et al. End-to-end reconfigurability (E2R Ⅱ):Management and control of adaptive communication systems[C]. in 1ST Mobile Summit.2006.
[26]Buddhikot M. M., Kolodzy P., Miller S., et al. DIMSUMnet:new directions in wireless networking using coordinated dynamic spectrum[C]. in World of Wireless Mobile and Multimedia Networks,2005. WoWMoM 2005. Sixth IEEE International Symposium on a.2005. Ieee.
[27]Darpa. "The XG Vision Request for Comments" [J]. XG Working Group.2003, Version 2.
[28]Lockerd A., Breazeal C. Tutelage and socially guided robot learning[C]. in Intelligent Robots and Systems,2004.(IROS 2004). Proceedings.2004 IEEE/RSJ International Conference on.2004. IEEE.
[29]Zhang Q., Kokkeler A. B., Smit G. J. A reconfigurable radio architecture for cognitive radio in emergency networks[C]. in Wireless Technology,2006. The 9th European Conference on.2006. IEEE.
[30]Wang J., Ghosh M., Challapali K. Emerging cognitive radio applications:A survey[J]. Communications Magazine, IEEE.2011,49(3):74-81.
[31]宋志群,刘玉涛,王荆宁.认知无线电技术及其应用[M].2012：国防工业出版社.
[32]Robey F. C., Fuhrmann D. R., Kelly E. J., et al. A CFAR adaptive matched filter detector[J]. Aerospace and Electronic Systems, IEEE Transactions on.1992,28(1):208-216.
[33]Urkowitz H. Energy detection of unknown deterministic signals[J]. Proceedings of the IEEE.1967, 55(4):523-531.
[34]Ye Z., Grosspietsch J., Memik G. Spectrum sensing using cyclostationary spectrum density for cognitive radios[C]. in Signal Processing Systems,2007 IEEE Workshop on.2007. IEEE.
[35]Luo L., Neihart N. M., Roy S., et al. A two-stage sensing technique for dynamic spectrum access[J]. Wireless Communications, IEEE Transactions on.2009,8(6):3028-3037.
[36]Ejaz W., ul Hasan N., Azam M. A., et al. Improved local spectrum sensing for cognitive radio networks[J]. EURASIP Journal on Advances in Signal Processing.2012,2012(1):1-12.
[37]Ejaz W, Hasan N., Kim H. S. SNR-based adaptive spectrum sensing for cognitive radio networks[J]. Int. J. Innov. Comput. Inf. Control.2012,8(9):6095-6106.
[38]Nair P. R., Vinod A. P., Smitha K. G., et al. Fast two-stage spectrum detector for cognitive radios in uncertain noise channels[J]. IET communications.2012,6(11):1341-1348.
[39]FCC E. Docket No 03-222 Notice of proposed rule making and order[R].2003. December.
[40]Elahi I., Muhammad K., Balsara P. T. IIP2 and DC offsets in the presence of leakage at LO frequency[J]. Circuits and Systems Ⅱ:Express Briefs, IEEE Transactions on.2006,53(8): 647-651.
[41]Quan Z., Cui S., Sayed A. H. Optimal linear cooperation for spectrum sensing in cognitive radio networks[J]. Selected Topics in Signal Processing, IEEE Journal of.2008,2(1):28-40.
[42]Do T., Mark B. L. Joint spatial-temporal spectrum sensing for cognitive radio networks[J]. Vehicular Technology, IEEE Transactions on.2010,59(7):3480-3490.
[43]Kortun A., Ratnarajah T., Sellathurai M., et al. On the performance of eigenvalue-based cooperative spectrum sensing for cognitive radio[J]. Selected Topics in Signal Processing, IEEE Journal of.2011,5(1):49-55.
[44]石磊,周正,唐亮,等.认知无线电网络中压缩协作频谱感知[J].北京邮电大学学报.2011,34(5)：76-79.
[45]邹玉龙.认知无线电网络中协作中继技术研究[D].2012.南京邮电大学博士论文
[46]岳文静,陈志,郑宝玉,等.基于可靠次用户信息的协作频谱感知算法研究[J].电子与信息学报.2012,34(5)：1208-1213.
[47]Saberinia E. A Practical Multi-Bit Data Combining Strategy for Centralized Cooperative Spectrum Sensing[J].2013.
[48]杨黎,王晓湘,赵堃.多天线感知无线电中的协作频谱感知算法[J].电子与信息学报.2009,31(10)：2338-2342.
[49]Wang H., Wang H., Zhang L., et al. Cyclostationarity analysis on EBPSK and its optimizations[J]. Chinese Journal of Radio Science.2010,25(1):42-46.
[50]Alghamdi O. A., Ahmed M. Z. Optimal and suboptimal multi antenna spectrum sensing techniques with master node cooperation for cognitive radio systems[J]. Journal of Communications.2011, 6(7):512-523.
[51]Wenshan Y., Pinyi R., Zhou S., et al. A multiple antenna spectrum sensing scheme based on space and time diversity in cognitive radios[J]. IEICE Transactions on Communications.2011,94(5): 1254-1264.
[52]Tugnait J. K. On multiple antenna spectrum sensing under noise variance uncertainty and flat fading[J]. Signal Processing, IEEE Transactions on.2012.60(4):1823-1832.
[53]Shen L., Wang H., Zhang W., et al. Multiple Antennas Assisted Blind Spectrum Sensing in Cognitive Radio Channels[J]. Communications Letters, IEEE.2012,16(1):92-94.
[54]Singh A., Bhatnagar M. R., Mallik R. K. Cooperative spectrum sensing in multiple antenna based cognitive radio network using an improved energy detector[J]. Communications Letters, IEEE. 2012,16(1):64-67.
[55]赵友轩,周游,胡捍英,等.多天线认知系统中最优协作用户数的选择研究[J].信号处理.2013,29(5)：625-631.
[56]Tabesh A., Taherpour A., Pourgharehkhan Z. Multiple antenna spectrum sensing using statistical a-priori information in cognitive radios[J]. IET Signal Processing.2013,7(1):59-70.
[57]Ejaz W., ul Hasan N., Lee S., et al, I3S:intelligent spectrum sensing scheme for cognitive radio networks[J]. EURASIP Journal on Wireless Communications and Networking.2013,2013(1):26.
[58]Simon M. K., Alouini M.-S. A unified approach to the performance analysis of digital communication over generalized fading channels[J]. Proceedings of the IEEE.1998,86(9): 1860-1877.
[59]Yue W., Zheng B. Spectrum sensing algorithms for primary detection based on reliability in cognitive radio systems[J]. Computers & Electrical Engineering.2010,36(3):469-479.
[60]陈星,贺志强,吴伟陵.基于循环平稳的多天线感知无线电频谱检测[J].北京邮电大学学报.2008,31(2)：85-89.
[61]Zhang Q. A generic correlated Nakagami fading model for wireless communications[J]. Communications, IEEE Transactions on.2003,51(11):1745-1748.
[62]Karagiannidis G. K. Moments-based approach to the performance analysis of equal gain diversity in Nakagami-m fading[J]. Communications, IEEE Transactions on.2004,52(5):685-690.
[63]Zlatanov N., Hadzi-Velkov Z., Karagiannidis G. An efficient approximation to the correlated Nakagami-m sums and its application in equal gain diversity receivers[J]. Wireless Communications, IEEE Transactions on.2010,9(1):302-310.
[64]Digham F. F., Alouini M.-S., Simon M. K. On the energy detection of unknown signals over fading channels[J]. Communications, IEEE Transactions on.2007,55(1):21-24.
[65]张雯,杨家玮,闫琦,等.认知无线电中Nakagami信道下等增益合作检测性能分析[J].北京邮电大学学报.2010,33(006)：93-97.
[66]Chennakeshu S., Hassan A., Ozarow L. Average SNR interpretation for diversity reception[J]. Electronics Letters.1994,30(20):1641-1642.
[67]Yacoub M. D., da Silva C. M., Vargas Bautista J. E. Second-order statistics for diversity-combining techniques in Nakagami-fading channels[J]. Vehicular Technology, IEEE Transactions on.2001,50(6):1464-1470.
[68]Rahman M. A., Harada H. New exact closed-form PDF of the sum of Nakagami-m random variables with applications[J]. Communications, IEEE Transactions on.2011,59(2):395-401.
[69]da Costa D. B., Yacoub M. D., Santos Filho J. An Improved Closed-Form Approximation to the Sum of Arbitrary Nakagami-< formula formulatype=[J]. Vehicular Technology, IEEE Transactions on.2008,57(6):3854-3858.
[70]Yacoub M. D. The α-μ distribution:A physical fading model for the Stacy distribution[J]. Vehicular Technology, IEEE Transactions on.2007,56(1):27-34.
[71]Dharmawansa P., Rajatheva N., Ahmed K. On the distribution of the sum of Nakagami-m random variables[J]. IEEE transactions on communications.2007,55(7):1407-1416.
[72]胡罡.无线认知网络频谱感知与接入关键技术研究[D].2010.国防科学技术大学博士论文
[73]Song M., Xin C., Zhao Y., et al. Dynamic spectrum access:from cognitive radio to network radio[J]. Wireless Communications, IEEE.2012,19(1):23-29.
[74]Ren P., Wang Y., Du Q., et al. A survey on dynamic spectrum access protocols for distributed cognitive wireless networks[J]. EURASIP J. Wireless Comm. and Networking.2012,2012:60.
[75]Xing Y., Chandramouli R., Mangold S. Dynamic spectrum access in open spectrum wireless networks[J]. Selected Areas in Communications, IEEE Journal on.2006,24(3):626-637.
[76]郭彩丽,曾志民,冯春燕,等.机会频谱接入系统的切换请求排队机制及性能分析[J].电子与信息学报.2009,31(6)：1505-1508.
[77]Lee H., Cho D.-H. Capacity improvement and analysis of VoIP service in a cognitive radio system[J]. Vehicular Technology, IEEE Transactions on.2010,59(4):1646-1651.
[78]Li H., Han Z. Socially optimal queuing control in cognitive radio networks subject to service interruptions:To queue or not to queue?[J]. Wireless Communications, IEEE Transactions on. 2011,10(5):1656-1666.
[79]Wang L.-C., Wang C.-W., Feng K.-T. A queueing-theoretical framework for QoS-enhanced spectrum management in cognitive radio networks[J]. Wireless Communications, IEEE.2011, 18(6):18-26.
[80]Wang L.-C., Wang C.-W., Adachi F. Load-balancing spectrum decision for cognitive radio networks[J]. Selected Areas in Communications, IEEE Journal on.2011,29(4):757-769.
[81]Wang L.-C., Wang C.-W., Chang C.-J. Modeling and analysis for spectrum handoffs in cognitive radio networks[J]. Mobile Computing, IEEE Transactions on.2012,11(9):1499-1513.
[82]王玲,彭启琮,彭启航.基于收益的认知无线电多信道阶梯排队接入机制分析[J].电子与信息学报.2012,34：8.
[83]Tumuluru V. K., Wang P., Niyato D., et al. Performance analysis of cognitive radio spectrum access with prioritized traffic[J]. Vehicular Technology, IEEE Transactions on.2012,61(4): 1895-1906.
[84]Chu T., Phan H., Zepernick H. On the Performance of Underlay Cognitive Radio Networks Using M/G/1/K Queueing Model [J].2013.
[85]Jiang C, Chen Y., Liu K., et al. Renewal-Theoretical Dynamic Spectrum Access in Cognitive Radio Network with Unknown Primary Behavior[J]. Selected Areas in Communications, IEEE Journal on.2013,31(3):406-416.
[86]朱平.认知无线电关键技术研究[D].2010.中国科学技术大学博士论文
[87]铮官.离散时间并行调度两级服务与非对称门限服务轮询系统研究[D].2012.云南大学博士论文
[88]赵东风,丁洪伟,赵一帆,等.多级门限服务轮询系统MAC离散时间控制协议模型分析[J]电子学报.2010,38(7)：1495-1499.
[89]Saffer Z., Telek M. Unified analysis of BMAP/G/l cyclic polling models[J]. Queueing Systems. 2010,64(1):69-102.
[90]van der Mei R. D., Roubos A. Polling models with multi-phase gated service[J]. Annals of Operations Research.2012,198(1):25-56.
[91]De Haan, Roland Boucherie, Richard J, et al. Time-Limited Polling Systems With Batch Arrivals And Phase-Type Service Times[J].
[92]Chernova N., Foss S., Kim B. On the stability of a polling system with an adaptive service mechanism[J]. Annals of Operations Research.2012,198(1):125-144.
[93]Leskela L., Unger F. Stability of a spatial polling system with greedy myopic service[J]. Annals of Operations Research.2012,198(1):165-183.
[94]Kavitha V., Altman E. Continuous Polling models and application to ferry assisted WLAN[J]. Annals of Operations Research.2012,198(1):185-218.
[95]Boon M. A., Adan I. J.-B. F., Boxma O. J. A polling model with multiple priority levels[J]. Performance Evaluation.2010,67(6):468-484.
[96]陈佩树,朱翼隽,耿响.具有强占优先权的不耐烦顾客的M/M/m/k排队模型[J].系统工程与电子技术.2008,30(6)：1069-1073.
[97]Melikov A. Z., Fattakhova M. I. Simulation of wireless cellular networks with impatient calls[J]. Autcmatic Control and Computer Sciences.2010,44(1):31-41.
[98]彭懿.杨向群,吴锦标.带负顾客和不耐烦顾客的离散时间Geo/G/1重试排队[J].系统工程理论与实践.2011,31(12)：2373-2379.
[99]Sakuma Y., Inoie A. Stationary distribution of a multi-server vacation queue with constant impatient times[J]. Operations Research Letters.2012,40(4):239-243.
[100]Boon M. A. A polling model with reneging at polling instants[J]. Annals of Operations Research.2012,198(1)：5-23.
[101]Kim J., Kim B., Kang J. Discrete-time multiserver queue with impatient customers [J]. Electronics Letters.2013,49(1):38-39.
[102]鲁进,黄铭,杨志军.基于轮询调度的频谱接入机制研究[J].电信科学.2013,29(12)：60-65.
[103]Hu J., Yang L.-L., Hanzo L. Maximum Average Service Rate and Optimal Queue Scheduling of Delay-Constrained Hybrid Cognitive Radio in Nakagami Fading Channels[J]. IEEE Transactions on Vehicular Technology.2013.
[104]Lu J.,Ding H.. Zhou D., et al. Analysis of a Simplified Gated Polling Scheduling Model Based on Clustering Routing[C]. in Computer Science and Network Technology (1CCSNT),2012 International Conference on.2012.
[105]Lu J., Ding H., Zhao Y, et al. A Novel Simplified Limit-1 Polling Communication System[J]. Advanced Materials Research.2013,756:2300-2305.
[106]Lu J., Ding H., Zhou D., et al. Research on E-SG Mixed Polling Model Based on two Kinds of Businesses[C]. in Computer Science and Network Technology (ICCSNT),2012 International Conference on.2012.
[107]杨志军,赵东风,丁洪伟,等.两级优先级控制轮询系统研究[J].电子学报.2009(007)：1452-1456.
[108]Sharma A. K. Throughput Improvement Of Cognitive Radio Network Using Orthogonal Frequency Division Multiplexing[J]. International Journal of Advances in Engineering Sciences. 2011, 1(3):15-18.
[109]Young A. R., Bostian C. W. Simple and Low-Cost Platfonns for Cognitive Radio Experiments [Application Notes][J]. Microwave Magazine, IEEE.2013,14(1):146-157.
[110]Avila J., Thenmozhi K. Simulink Based Spectrum Sensing[J]. International Journal of Engineering & Technology (0975-4024).2013,5(2).
[111]Aravind H., Gandhiraj R., Soman K., et al. Spectrum Sensing Implementations for Software Defined Radio in Simulink[J]. Procedia Engineering.2012,30:1119-1128.
[112]Ding H., Yang J., Zhao Z. Adaptive dual threshold cooperative spectrum sensing based on grouping[J]. Journal of Jilin University(Engineering and Technology Edition).2011,41(2): 537-542.
[113]Sun J.-C. Automatic real-time spectrum sensing using energy detection in software defined radio[D].2012. California State University
[114]Tabassam A. A., Suleman M., Kalsait S., et al. Building cognitive radios in MATLAB Simulink-A step towards future wireless technology[C]. in Wireless Advanced (WiAd),2011. 2011. IEEE.
[115]宋连波.基于IEEE 802.22的WRAN系统级仿真平台设计与研究[D].2009.北京邮电大学
[116]王润亮.认知无线电频谱感知与用户监测关键技术研究[D].2012.解放军信息工程大学
[117]刘忠军,徐友云,李默.多PC协同下的认知网络仿真平台设计[J]. Computer Engineering.2011,37(1).
[118]孙剑锋.认知无线电关键技术研究及其在家庭基站中的应用[D].2012.北京邮电大学博士论文
[119]Gardellin V, Das S. K., Lenzini L. Self-coexistence in cellular cognitive radio networks based on the IEEE 802.22 standard[J]. Wireless Communications, IEEE.2013,20(2):52-59.
[120]Kang K.-M., Park J. C., Cho S.-I., et al. Deployment and coverage of cognitive radio networks in TV white space[J]. Communications Magazine, IEEE.2012,50(12):88-94.
[121]Ray G. G., Dandapat S. K. Applications and Simulation of Femtocells in a Cognitive Radio Environment[D].2012.
[122]Yang L., Zhang Z., Hou W., et al. Papyrus:A software platform for distributed dynamic spectrum sharing using SDRs[J]. ACM SIGCOMM Computer Communication Review.2011,41(1):31-37.
[123]Chen Y.-S., Cho C.-H., You I., et al. A cross-layer protocol of spectrum mobility and handover in cognitive LTE networks[J]. Simulation Modelling Practice and Theory.2011,19(8):1723-1744.
[124]高嵩.OPNET Modeler仿真建模大解密[M].2010.北京：电子工业出版社.
[125]史迩冬.小型认知无线网络的设计和实现[D].2012.苏州大学
[126]夏东瀛.认知无线电系统中频谱分配算法的研究[D].2013.东华大学
[127]陈寅.认知网络中二级用户访问策略的研究[D].2012.湖北大学
[128]马慧生.多跳认知无线电网络路由技术研究[D].2009.西安电子科技大学
[129]韩艳锋.动态频谱共享无线通信系统性能分析及验证[D].2011.电子科技大学
[130]Dham V. Link establishment in ad hoc networks using smart antennas[D].2003. Citeseer
[131]刘学勇.详解MATLAB/simulink通信系统建模与仿真[M].2011：电子工业出版社.
[132]刘维.实战Matlab之并行程序设计[M].2012.北京航空航天大学出版社.