频谱感知技术外文翻译文献

频谱感知技术外文翻译文献(文档含中英文对照即英文原文和中文翻译)译文：一种新的协作频谱感知算法摘要该文提出了一种在认知无线网络控制信道带宽受限条件下基于信任度的双门限协同频谱感知算法。

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]1[。

为了提高频谱资源的利用率，认知无线电技术由Joseph Mitola Ⅲ提出并得到了广泛的关注]5[]2[ 。

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。

图1. 认知无线电网络示意图频谱感知的实质是一个二元假设问题，即01(),,()()()(),n t H x t h t s t n t H ⎧=⎨⋅+⎩ （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。

0H 代表授权用户没有工作，1H 代表授权用户正在工作。

设θ是认知用户接收信号的能量，根据能量检测理论[8]，θ服从以下分布：⎪⎩⎪⎨⎧122022),(,~H X H X m m γθ （2） 其中γ表示瞬时信噪比，并且其服从均值为_γ的指数分布，22m X 表自由度为2m 的中心卡方分布，)(22γm X 代表自由度为m 2非中心参数为γ的卡方分布，m 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θ跟预设的门限λ进行比较，如图2（a ）所示。

当λθ>时，本地能量检测器做出本地判决1=D ，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 表示认知用户接受到的能量值不可靠，认知用户不作出任何判决，发送感知报告给认知无线电网络基站。

如果出现所有认知用户都不作出判决的情况，则选择信用度最高的认知用户依据单门限能量检测算法作出本地判决。

并发送感知报告给认知无线电网络基站。

⎪⎩⎪⎨⎧<<<<<<=21211,1,0,0λθλλθλλθND D图2.（a ）一般能量检测算法本地判决示意图（b ）双门限能量检测算法本地判决示意图信用度获取方法采取文献[9]的方法：在最开始阶段，认知无线电网络基站把每个认知用户数目的可信度设为0，当某认知用户本地判决结果与认知无线电网络基站的最终判决结果一致时，该认知用户可信度加1，否则减1。

假设认知用户i 的可信度是i γ，则其更新过程如（4）：u u i i i +-+←)1(γγ （4） 其中u 是认知用户传送给认知无线电网络基站的判决结果，1u 是认知无线电网络基站的最终判决结果。

据文献[8]可知，认知用户在高斯信道下的平均检测概率、平均漏检概率和平均虚警概率如下所示： ),2()|(1λγλθm d Q H P P =>= (5) d m p H p P -=>=1)|(1λθ (6))()2/,()|(0m m H p P f ΓΓ=>=λλθ (7) 出于对授权用户的保护，认知无线电网络基站最终采用OR 准则作出判决。

3频谱感知性能分析3.1网络开销在1bit 量化条件下，avg K 代表归一化平均感知位数，K T 和K N T -分别代表K 个已向认知无线电网络基站发送数据和N-K 个未向认知无线电网络基站发送报告。

则：K i K p T P )](1[}{21λθλ<<-=，K N i K N P T P --<<=)]([}{21`λθλ。

设}{11H P P=和}{00H P P =，则划归一划平均感知位数acg K 如式8所示：∑∑==-=⎪⎪⎭⎫ ⎝⎛+⎪⎪⎭⎫ ⎝⎛=N K N K k N k K N k avgH T P H T P K N K P H T P H T P K N K P K 111110'00}|{}|{}|{}|{ (8)定义：)|(02110H P λθλ<<=∆，)|(02110H P λθλ<<=∆ 则：11001∆-∆-=P P K a v g (9) 由9式可得：1<avg K可知：基于双门限的协同频谱检测算法的网络开销始终小于常规的能量检测算法。

3.2检测性能分析设)(λF 和)(λG 别表示 θ在假设0H 和1H 下的概率分布，则根据文献[10]可知:⎰ΓΓ-==λλθθλ00)()2/,(1)|()(m m d H F F (10) ⎰=λθθλ01)|()(d H f G =⎥⎥⎥⎦⎤⎢⎢⎢⎣⎡⎪⎪⎪⎭⎫ ⎝⎛+-⨯⎪⎪⎪⎭⎫ ⎝⎛++⎪⎭⎫ ⎝⎛⎩⎨⎧-∑∑==+-==--202__2)1(2201_22)1(2!11211m n m n m n e e n eγγλγγλλγλλ (11) 显然)()(210λλF F -=∆，)()(121λλG G -=∆。

假设0β，1β分别代表在授权用户在工作和授权用户未工作情况下没有认知用户发送感知报告，即当K=0时，则：NN F F H K P 01200))()((}|0{∆=-===∆λλβ (12)NN G G H K P 11211))()((}|0{∆=-===∆λλβ (13)基于双门限的频谱感知算法在瑞利信道下的虚警概率f Q ，漏检概率m Q 和检测概率d Q 分别为：)1)(1(}1,|1{}|1{}|0,1{0000A f P K H u P H K P H K u P Q --=≥=≥=≥==β (14)m Q =d Q H K u P -=≥=1}|0,0{1(15) )1)(1(}1,|1{}|1{}|0,1{1111βP a K H u P H K P H K u P Q d --=≥=≥=≥==(16)其中：d A Q H K u P P -=≥==1}|0,0{1=∑=--⎪⎪⎭⎫ ⎝⎛NK K N k F F F K N 1121))()(()(λλλ =02)(βλ-N F (17) 12)(βλ-=G P B (18) 则：))(1)(1(020βλβ+--=N f F Q (19)))(1)(1(121βλβ+--=N f G Q (20) 由上式可知当0β=0时，此算法与常规算法相同。

当参与协同的认知用户数目N 较大时，00→β，则基于双门限的频谱检测算法的检测性能与常规能量算法的检测性能近似，可知在控制信道带宽受限制的情况下以较小的性能损失大大降低了网络开销。

4 仿真及分析本节通过计算机仿真来评估所提出的基于信任度的双门限协同频谱感知算法的性能。

仿真参数设置如表1 所示。

表1 仿真参数设置图3 给出了在1.00=∆的情况下算法的检测性能。

可以看出同常规能量检测算法相比较，本文所提出算法的检测性能得到了明显的改善。

例如当001.0=f Q 时，基于信任度的双门限协同频谱感知算法的检测概率d Q 比常规能量检测算法高出0.019。

图3检测性能示意图图4 描述了在不同0β的条件下，基于信任度的双门限协同频谱感知算法对网络开销的影响。

同常规能量检测算法即0β=0时相比较，本文所提出算法的归一化平均感知位数avg K 急剧下降，控制信道带宽与认知用户数量之间的矛盾得到了缓解。

例如当01.0=f Q ，0β= 0.01 时，基于信任度的双门限协同频谱感知算法的归一化平均感知位数avg K 下降了38%。

当01.0=f Q ，0β=0.001时，归一化平均感知位数avg K 则下降了44%图4 不同0β条件下算法对网络开销的影响5结束语频谱感知技术是认知无线电网络的支撑技术之一。

当认知用户数量很大的时候，控制信道的带宽将不够用。

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。

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。

参考文献[1] 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 personal[C],IEEEPersonal Communication. vol. 6, pp. 13–18, Aug. 1999.[3] S. Haykin. Cognitive radio: brain-empowered wireless communications [J]. IEEE J. Sel. AreasCommunication. vol. 23, pp. 201–220, Feb. 2005.[4] AKYLDIZ IF. Next generation/dynamic spectrum access/cognitive radio wireless networks: ASurvey [J]. ELSEVIER Computer Networks, 2006(50):2127-2159.[5] D. Cabric, S. M. Mishra, and R. W. Brodersen. Implementation issues in spectrum sensing forcognitive radios[C]// 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 fadingenvironments[C]// in Proc. 1st IEEES ymp. New Frontiers in Dynamic Spectrum Access Networks, Baltimore, USA, Nov. 8–11, 2005, pp. 131–136.[7] Chunhua Sun, Wei Zhang, Letaief K.B. Cooperative spectrum sensing for cognitive radios underbandwidth constraints[C]// in Proc. IEEE WCNC, March 11-15, 2007, pp. 1-5.[8] H. Urkowitz. 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 CognitiveRadio Networks[C]. in Proc. IEEEINFOCOM, April 2008, pp. 1876-1884.[10] F. F. Digham, M. -S. Alouini, and M. K. Simon. On the energy detection of unknown signalsover fading channels[C]. in Proc. IEEE ICC, Anchorage, AK, USA, May 11-15, 2003, pp.3575–3579.原文：A New Cooperative Spectrum Sensing Algorithmfor Cognitive Radio Networks Abstract—spectrum 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 sensing 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 report its sensing result. Simulation results show that our algorithm achieves better sensing performance and the average number of sensing bits decrease greatly. Keywords—cognitive radio; cooperative spectrum sensing; double threshold; reputationⅠ. INTRODUCTIONDue to the increasingly development of wireless 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 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 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 hidden 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 hasbeen studied to improve the spectrum sensing performance [4], [5].In[6], due to control channel for each cognitive radio to report 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 decisions 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 receiver 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 the conventional method.The rest of the paper is organized as follows. In section Ⅱ, system model is briefly introduced. Sensing performance is analyzed in Section Ⅲ. In Section Ⅳ, we present the simulation results of our cooperative spectrum sensing method. Finally, we draw our conclusions in Section Ⅴ.II. SYSTEM MODELIn 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. The essence of spectrum sensing is a binary hypothesis-testing problem:H:primary user is absent;H:primary user is present.1For implementation simplicity, we restrict ourselves to energy detection in the spectrum sensing. The local spectrum sensing is to decide between the following two hypotheses:01(),,()()()(),n t H x t h t s t n t H ⎧=⎨⋅+⎩ (1) Where )(t x is the signal received by secondary user, )(t s is primary user’s transmitted signal,)(t n is AWGN, and )(t h is the temporary amplitude gain of the channel.According to energy detection theory [7], we have the following distribution:⎪⎩⎪⎨⎧122022),(,~H X H X m m γθ (2) Where θ is the energy value collected by secondary user,γ is instantaneous SNR and follows exponentially distribution with the mean value _γ, m is the timebandwidth product of the energy detector,22m χrepresents a central chi-square distributionwith 2m degrees of freedom and. )(22γχm represents a non-central chi-square distribution with m 2degrees of freedom and a non-centrality parameter γ2.In 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 θ exceeds the threshold λ, decision 0H will be made. Otherwise decision 1H will be made. In contrast, the system model which has two thresholds of our interest is shown inFig.1 (b). Where ― Decision 0H ‖ and ―Decision 1H ‖ represent the absence and the presence of licensed user, respectively.“No decision‖ means that the observation is not reliable enough and the i th cognitive user will send nothing to the common receiver. But when all the secondary users don’t 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 us er’s sensing results. 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 i th cognitive user’s reputation value is 1, the last sensing report of cognitive user i send to common receiver is u , and the final decision is i u ,then i γ is updated according to thefollowing relation: u u i i i +-+←)1(γγFor the cognitive radio users with the energy detector, the average probabilities of detection, the average probabilities of missed detection, and the average probabilities of false alarm over AWGN channels are given, respectively, by [7]:),2()|(1λγλθm d Q H P P =>= (3)d m p H p P -=>=1)|(1λθ (4))()2/,()|(0m m H p P f ΓΓ=>=λλθ (5) Where )(a Γ, ),(b a Γare complete and incomplete gamma function respectively, and ),(b a Q m is the generalized Marcum function.In this paper, we consider cooperative spectrum sensing with 1bit quantization. Let avg K represent the normalizedFig1. (a)Conventional detection method(b)Double threshold energy detection methodaverage number of sensing bit. Let K T and 'K N T -represent he event that there are K unlicensed users reporting 1-bit decision and N-K users not reporting to the commonreceiver, respectively. The K i K p T P )](1[}{21λθλ<<-=, K N i K N P T P --<<=)]([}{21`λθλ.andthen the average number of sensing bits for our method can be derived as:∑∑==-=⎪⎪⎭⎫ ⎝⎛+⎪⎪⎭⎫ ⎝⎛=NK N K k N k K N k avg H T P H T P K N K P H T P H T P K N K P K 111110'00}|{}|{}|{}|{ （6）For simplicity, we define:)|(02110H P λθλ<<=∆ ，)|(02110H P λθλ<<=∆ （7）Let navg K denote the normalized average number of sensing bits, then, we obtain navg K as follows ：11001∆-∆-=P P K a v g （8） From (8), It can be seen that, the normalized average number of sensing bits navg K is always smaller than 1. the communication traffic of our method is are deduced as opposed to the conventional energy detection method.III. THE PERFORMANCE ANALYSIS OF SPECTRUMSENSINGIn this section, the spectrum sensing performance of the proposed method will be analyzed. Assume the control channel between the unlicensed users and the common receiver is perfect, the local decisions are reported without any error. Let )(λF and )(λG denote the cumulative distribution function (CDF) of the local test statistic θ under the hypothesis 0H and 1H , respectively. Then, we have [10]:⎰ΓΓ-==λλθθλ00)()2/,(1)|()(m m d H F F (9) ⎰=λθθλ01)|()(d H f G (10)Obviously,)()(210λλF F -=∆，)()(121λλG G -=∆.If no any local decision is reported 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 0βand 1βdenote the probability of fail sensing under hypothesis 0H and 1H , respectively. Here we have:NN F F H K P 01200))()((}|0{∆=-===∆λλβ (11)NN G G H K P 11211))()((}|0{∆=-===∆λλβ (12)Apparently,N00∆=β and N 11∆=β.In our scheme, the false alarm probabilityf Q ,the detection probability d Q ,and the missing probability m Q :)1)(1(}1,|1{}|1{}|0,1{0000A f P K H u P H K P H K u P Q --=≥=≥=≥==β (13)m Q =d Q H K u P -=≥=1}|0,0{1 (14))1)(1(}1,|1{}|1{}|0,1{1111βP a K H u P H K P H K u P Q d --=≥=≥=≥== (15)For simplicity, we assume the channel between the unlicensed users and the base station are ideal, the local decision will be reported without any error. So A P stand for the probability of the event that under hypothesis 0H , all the K users claim 0H and other N-K users make no local decisions.d A Q H K u P P -=≥==1}|0,0{1=∑=--⎪⎪⎭⎫ ⎝⎛NK K N k F F F K N 1121))()(()(λλλ =02)(βλ-N F （16） 12)(βλ-=G P B （17） ))(_1)(1(020βλβ+-=N f F Q （18） ))(_1)(1(121βλβ+-=N f G Q （19）IV. SIMULATION RESULTSIn this section, some simulation results are presented to illustrate the system performance of our 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:Fig.2 depicts the performance of cooperative spectrum sensing d Q and f Q .1.00=∆.It can be observed that, compared it with the conventional method, the detection performance has improved significantly. For example, while f Q = 0.001, our method achieves extra 0.019 detection probability. Fig.3 shows the decrease of the normalized transmission bits for different values of fail sensing, i.e. 0β= 0, 0.001, 0.01, 0.1. Compared with conventional method, i.e., when 0β = 0, the normalized average number of sensing bits is dramatically decreased and bandwidth limited problem of the reporting channel is relieved. For example, when f Q = 0.01, almost 44% and 38% reduction of the normalized average number of sensing bits can be obtained for 0β= 0.001 and 0β = 0.01, respectively. In our algorithm, f Q is upper bounded and lower bounded because of the probability of fail sensing 0βand the false alarm probability are based on (7), (13).Fig 2.d Q vs.f Q , 1.00=∆Fig 3.navg K vs.f Q ,0 =00,0.001,0.01,0.1V. 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, 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. 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