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1、毕 业 设 计（论 文）外 文 参 考 资 料 及 译 文译文题目： 一个新的协作频谱感知算法认知无线电网络 学生姓名： 学号： 专业： 通信工程 所在学院： 龙蟠学院 指导教师： 田甜 职称： 讲师 2011年 12月 1日说明：要求学生结合毕业设计（论文）课题参阅一篇以上的外文资料，并翻译至少一万印刷符（或译出3千汉字）以上的译文。译文原则上要求打印（如手写，一律用400字方格稿纸书写），连同学校提供的统一封面及英文原文装订，于毕业设计（论文）工作开始后2周内完成，作为成绩考核的一部分。 A New Cooperative Spectrum Sensing Algorithmfor Cog
2、nitive Radio NetworksAbstractspectrum sensing is a critical phase in building a cognitive radio network. However, the bandwidth for reporting secondary users sensing results will be insufficient, when the number of secondary user is very large. In this paper, we propose a new cooperative spectrum se
3、nsing algorithm to alleviate the bandwidth problem of reporting channel. Compared with conventional method, only the secondary users with reliable information are allowed to report their sensing results. When no user with reliable information, only the secondary user with highest reputation will rep
4、ort its sensing result. Simulation results show that our algorithm achieves better sensing performance and the average number of sensing bits decrease greatly.Keywordscognitive radio; cooperative spectrum sensing; double threshold; reputation. INTRODUCTION Due to the increasingly development of wire
5、less applications, more and more spectrum resources are needed to support numerous emerging wireless service. However, recent measurements by Federal Communication Commission (FCC) have shown that 70% of the allocated spectrum in US is not utilized 1. In order to increase the efficiency of spectrum
6、utilization, cognitive radio technology was recently proposed 2, 3. A requirement of cognitive radios is that their transmission should not cause harmful interference to primary users. Namely, the secondary users can use the licensed spectrum as long as the primary user is absent. However, when the
7、primary user comes back into operation, the secondary users should vacate the spectrum instantly to avoid interference with the primary user. Accordingly, spectrum sensing is a crucial phase in building a cognitive radio system. One of the great challenges of implementing spectrum sensing is the hid
8、den terminal problem which caused by the fading of the channels and the shadowing effects. In order to deal with the hidden terminal problem, cooperative spectrum sensing has been studied to improve the spectrum sensing performance 4, 5. In6, due to control channel for each cognitive radio to report
9、 its sensing result is usually bandwidth limited, a censoring method which has two thresholds is given to decrease the average number of sensing bits to the common receiver. By censoring the collected local observations, only the secondary users with enough information will send their local decision
10、s to the common receiver. In this paper, we present a new double threshold cooperative spectrum sensing method with reputation. In our system, every cognitive user will firstly obtain an observation independently and only the users with reliable information send their local decisions to the common r
11、eceiver based on double thresholds. If no user is reliable, only the cognitive user with the highest reputation is selected to sense the spectrum. Simulation results show that the spectrum sensing performance under AWGN channels is improved and the communication traffic is also reduced as opposed to
12、 the conventional method. The rest of the paper is organized as follows. In section , system model is briefly introduced. Sensing performance is analyzed in Section . In Section , we present the simulation results of our cooperative spectrum sensing method. Finally, we draw our conclusions in Sectio
13、n .II. SYSTEM MODEL In cognitive radio systems, spectrum sensing is a critical element as it should be firstly performed before allowing secondary users to access a vacant licensed channel. Cooperative spectrum sensing has been widely used to detect the primary user with a high agility and accuracy.
14、 The essence of spectrum sensing is a binary hypothesis-testing problem:primary user is absent;:primary user is present. For implementation simplicity, we restrict ourselves to energy detection in the spectrum sensing. The local spectrum sensing is to decide between the following two hypotheses: (1)
15、 Where is the signal received by secondary user, is primary users transmitted signal,is AWGN, and is the temporary amplitude gain of the channel. According to energy detection theory 7, we have the following distribution: (2) Where is the energy value collected by secondary user, is instantaneous SN
16、R and follows exponentially distribution with the mean value , is the time bandwidth product of the energy detector,represents a central chi-square distribution with 2m degrees of freedom and. represents a non-central chi-square distribution with degrees of freedom and a non-centrality parameter . I
17、n conventional energy detection method, the local decision is made by comparing the observation with a pre-fixed threshold as Fig.1 (a). When the collected energy exceeds the threshold , decision will be made. Otherwise decision will be made. In contrast, the system model which has two thresholds of
18、 our interest is shown inFig.1 (b). Where “ Decision ” and “Decision ” represent the absence and the presence of licensed user, respectively.“No decision” means that the observation is not reliable enough and the th cognitive user will send nothing to the common receiver. But when all the secondary
19、users dont send their local decisions, only the cognitive user with the highest reputation is selected to sense spectrum based on conventional energy detection method, and send its local decision to the common receiver. Reputation is obtained based on the accuracy of cognitive users sensing results.
20、 The reputation value is set to zero at the beginning. Whenever its local spectrum sensing report is consistent with the final sensing decision, its reputation is incremented by one; otherwise it is decremented by one. Under this rule, assuming the th cognitive users reputation value is 1, the last
21、sensing report of cognitive user send to common receiver is , and the final decision is ,then is updated according to the following relation: For the cognitive radio users with the energy detector, the average probabilities of detection, the average probabilities of missed detection, and the average
22、 probabilities of false alarm over AWGN channels are given, respectively, by 7: (3) (4) (5) Where , are complete and incomplete gamma function respectively, and is the generalized Marcum function. In this paper, we consider cooperative spectrum sensing with 1bit quantization. Let represent the norma
23、lized D=0 D=1(a)(b)0 D=0 D=1ND0Fig1. (a)Conventional detection method (b)Double threshold energy detection methodaverage number of sensing bit. Let and represent he event that there are K unlicensed users reporting 1-bit decision and N-K users not reporting to the common receiver, respectively. The
24、, .and then the average number of sensing bits for our method can be derived as: （6）For simplicity, we define: ， （7）Let denote the normalized average number of sensing bits, then, we obtain as follows： （8）From (8), It can be seen that, the normalized average number of sensing bits is always smaller
25、than 1. the communication traffic of our method is are deduced as opposed to the conventional energy detection method.III. THE PERFORMANCE ANALYSIS OF SPECTRUM SENSINGIn this section, the spectrum sensing performance of the proposed method will be analyzed. Assume the control channel between the unl
26、icensed users and the common receiver is perfect, the local decisions are reported without any error. Let and denote the cumulative distribution function (CDF) of the local test statistic under the hypothesis and , respectively. Then, we have 10: (9) (10)Obviously,，.If no any local decision is repor
27、ted to the common receiver, i.e., K=0 , we call that fail sensing. For this case, the common receiver will request the user which has the highest reputation to send its local decision based on conventional energy detection method. Let and denote the probability of fail sensing under hypothesis and ,
28、 respectively. Here we have: (11) (12)Apparently, and .In our scheme, the false alarm probability ,the detection probability,and the missing probability : (13) = (14) (15)For simplicity, we assume the channel between the unlicensed users and the base station are ideal, the local decision will be rep
29、orted without any error. So stand for the probability of the event that under hypothesis , all the K users claim and other N-K users make no local decisions. = = （16） （17） （18） （19）IV. SIMULATION RESULTSIn this section, some simulation results are presented to illustrate the system performance of ou
30、r cooperative spectrum sensing algorithm based on reputation. The results of the conventional one threshold energy detection method are also shown for a comparison. In our simulation, the common simulation parameters are given as follows: Table 1. Simulation parametersFig.2 depicts the performance o
31、f cooperative spectrum sensing and .It can be observed that, compared it with the conventional method, the detection performance has improved significantly. For example, while = 0.001, our method achieves extra 0.019 detection probability. Fig.3 shows the decrease of the normalized transmission bits
32、 for different values of fail sensing, i.e. = 0, 0.001, 0.01, 0.1. Compared with conventional method, i.e., when = 0, the normalized average number of sensing bits is dramatically decreased and bandwidth limited problem of the reporting channel is relieved. For example, when = 0.01, almost 44% and 3
33、8% reduction of the normalized average number of sensing bits can be obtained for = 0.001 and = 0.01, respectively. In our algorithm, is upper bounded and lower bounded because of the probability of fail sensing and the false alarm probability are based on (7), (13).Fig 2.vs., Fig 3.vs.,=00,0.001,0.
34、01,0.1V. CONCLUSIONIn this paper, a new scheme in cooperative spectrum sensing for cognitive radio networks under bandwidth constraints was proposed. In our method, only the secondary users with reliable information are allowed to report their sensing results. When no user has reliable information,
35、only he secondary user with highest reputation will report its sensing result. We analyzed the closed expression for the probability of the detection and the false-alarm. From the preliminary simulation results, we demonstrated the average number of sensing bits decrease greatly and the sensing perf
36、ormance is also improved.REFERENCES1 Federal Communications Commission. Spectrum Policy Task Force, Rep. ET Docket no. 02-135 R. Nov. 2002.2 J. Mitola and G. Q. Maguire. Cognitive radio: Making software radios more personalC,IEEE Personal Communication. vol. 6, pp. 1318, Aug. 1999.3 S. Haykin. Cogni
37、tive radio: brain-empowered wireless communications J. IEEE J. Sel. Areas Communication. vol. 23, pp. 201220, Feb. 2005.4 AKYLDIZ IF. Next generation/dynamic spectrum access/cognitive radio wireless networks: A Survey J. ELSEVIER Computer Networks, 2006(50):2127-2159.5 D. Cabric, S. M. Mishra, and R
38、. W. Brodersen. Implementation issues in spectrum sensing for cognitive radiosC/ in Proc. Of A silomar Conf. on Signals, Systems, and Computers, Pacific Grove,CA, USA, Nov. 7-10, 2004, pp. 772 - 776.6 A.Ghasemi and E. S. Sousa. Collaborative spectrum sensing for opportunistic access in fading enviro
39、nmentsC/ in Proc. 1st IEEES ymp. New Frontiers in Dynamic Spectrum Access Networks, Baltimore, USA, Nov. 811, 2005, pp. 131136.7 Chunhua Sun, Wei Zhang, Letaief K.B. Cooperative spectrum sensing for cognitive radios under bandwidth constraintsC/ in Proc. IEEE WCNC, March 11-15, 2007, pp. 1-5.8 H. Ur
40、kowitz. Energy detection of unknown deterministic signals C. Proceedings of IEEE, vol.55, pp. 523-531, April 1967.9 Ruiliang Chen, Jung-Min Park, Kaigui Bian. Robust Distributed Spectrum Sensing in Cognitive Radio NetworksC. in Proc. IEEEINFOCOM, April 2008, pp. 1876-1884.10 F. F. Digham, M. -S. Alo
41、uini, and M. K. Simon. On the energy detection of unknown signals over fading channelsC. in Proc. IEEE ICC, Anchorage, AK, USA, May 11-15, 2003, pp. 35753579. 译文：摘要频谱遥感是一个关键阶段构建认知无线电网络。然而，带宽报告认知用户的检测结果是不够的，当一些次要用户非常大。首先每个认知用户基于双检测门限独立进行频谱感知，但只有部分可靠的认知用户通过控制信道向认知无线网络基站发送本地感知结果。当所有的用户都不可靠时，选取信任度最高的认知用
42、户发送本地感知结果进行判决。理论分析和仿真表明，同常规能量检测算法相比较，该算法能够在控制信道带宽受限条件下，以较少的网络开销获得更好的频谱感知性能。关键词：认知无线电；频谱感知；信任度；双门限1引言随着无线通信技术的飞速发展，有限的频谱资源与不断增长的无线通信需求的矛盾越来越突出。然而根据现有的固定分配频谱资源策略，绝大多数频谱资源得不到有效利用。据FCC 的调查统计，70%的已分配频谱资源没有得到有效利用。为了提高频谱资源的利用率，认知无线电技术由Joseph Mitola 提出并得到了广泛的关注。频谱感知技术是认知无线电网络的支撑技术之一。通常它又可以分为能量检测法、匹配滤波器法和循环平
43、稳特征法4。能量检测算法因为应用简单且无需知道任何授权用户信号的先验知识成为研究热点。认知用户在接入授权频带之前，必须首先感知该频带空闲即授权用户没有工作，否则会对授权用户造成干扰。一旦授权用户重新工作，认知用户必须退避，实现在不对授权用户产生干扰的情况下对频谱资源的共享。由于实际信道中的多径和阴影效应，单个认知用户频谱感知的性能并不乐观，针对这个问题D. Cabric等人提出了协同频谱感知算法5-6。协同频谱感知算法性能较好，但是当认知用户数量很大的时候，控制信道的带宽将不够用。文献7中提出了一种在控制信道带宽受限条件下的基于双检测门限的频谱感知算法，该算法能够以较小的网络开销，获得接近普通
44、单门限频谱检测算法的性能。针对认知无线电频谱感知的需要，本文提出了认知无线电环境下一种基于信任度的双门限协同频谱感知算法。该算法中每个认知用户基于双检测门限独立进行频谱感知，但只有部分可靠的认知用户通过控制信道向认知无线网络基站发射感知报告。当所有的用户都不可靠时，选取信任度最高的认知用户发射感知报告进行判决。本文对该算法进行了性能分析并通过仿真表明，本文方法比较常规能量检测算法，在减小网络开销的同时提高了检测性能。2系统模型假设一个认知无线电网络有N个认知用户和一个认知无线网络基站，如图1 所示。认知无线网络基站负责管理和联系N个认知用户，在收到认知用户的检测报告后做出最终判决。频谱感知的实
45、质是一个二元假设问题，即 （1）其中x(t)代表认知用户接收到的信号，s(t)表示授权用户的发送信号，h(t)代表授权用户与认知用户之间信道的衰落因子。代表授权用户没有工作，代表授权用户正在工作。设是认知用户接收信号的能量，根据能量检测理论8，服从以下分布： （2）其中表示瞬时信噪比，并且其服从均值为的指数分布，表自由度为2m的中心卡方分布，代表自由度为非中心参数为的卡方分布，表示时间带宽积。在能量检测算法本地判决中，每个认知用户把接收到的能量跟预设的门限进行比较，如图2（a）所示。当时，本地能量检测器做出本地判决，表示授权用户在工作，否则判决 D 为 0。而双门限能量检测算法本地判决如图3(b)所示，本地能量检测器判决规则如下： （3）其中ND表示
