基于能量检测的协作频谱感知

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201406_仪器仪表学报_基于差分能量检测的双门限协作频谱感知算法

第35卷第6期2014年6月仪器仪表学报Chinese Journal of Scientific InstrumentVol.35No.6Jun．2014收稿日期：2014-01Ｒeceived Date ：2014-01*基金项目：国家自然科学基金（61001077，61170276，61271334）资助项目基于差分能量检测的双门限协作频谱感知算法*张学军1，2，严金童2，田峰1，孙知信1（1．南京邮电大学宽带无线通信与传感网技术教育部重点实验室南京210003；2．南京邮电大学电子科学与工程学院南京210003）摘要：在传统双门限能量检测的基础上，对处于双门限之间的认知用户采用差分能量检测方法，提出了一种基于差分能量检测的双门限协作频谱感知（简称差分双门限能量检测）算法。

该算法不仅考虑了噪声的不确定性对系统性能的影响，而且有效克服了传统双门限检测算法中的感知失败问题。

仿真结果表明，差分双门限能量检测可以有效提高系统的频谱感知性能。

另外仿真实验分析了判决系数和信道信噪比对系统性能的影响，结果显示，信噪比越高，判决系统对系数性能影响越显著，且在合理的取值范围内，系统性能随着判决系数取值的减小而提高。

关键词：认知无线电；协作频谱感知；双门限；差分能量检测；噪声不确定性中图分类号：TN915．01文献标识码：A国家标准学科分类代码：510．50Dual-threshold cooperative spectrum sensing algorithmbased on differential energy detectionZhang Xuejun 1，2，Yan Jintong 2，Tian Feng 1，Sun Zhixin 1（1．Key Laboratory of Broadband Wireless Communication and Sensor Network Technology of Ministry of Education ，Nanjing University of Posts and Telecommunications ，Nanjing 210003，China ；2．School of Electronic Science and Engineering ，Nanjing University of Posts and Telecommunications ，Nanjing 210003，China ）Abstract ：A dual-threshold cooperative spectrum sensing algorithm based on differential energy detection （“differential dual-threshold energy detection ”for short ）is proposed ，which applies a novel differential energy detection algorithm for the secondary users between the two thresholds of traditional dual-threshold energy detection algorithm．In the differential dual-threshold energy detection algorithm ，the influence of the noise uncertainty on system performance is considered and the sensing failure problem in traditional dual-threshold detection is effectively overcome．Simulation results show that the proposed algorithm can effectively improve the spectrum sensing per-formance．Besides ，the impacts of the decision coefficient and the channel SNＲon system performance are also analyzed in the simula-tion．The result shows that the SNＲis higher ，the impact of the decision coefficient is more significant．And within a reasonable range ，as the decision coefficient decreases the system detection performance increases．Keywords ：cognitive radio ；cooperative spectrum sensing ；dual-threshold ；differential energy detection ；noise uncertainty1引言随着无线电技术的发展，无线频谱资源日益匮乏，认知无线电技术可以有效提高频谱的利用率。

基于能量检测的认知无线电频谱感知算法研究

收稿日期：2019-03-18 修回日期：2019-03-29
H1，否则判定为 H0。能量检测算法是将接收信号的能量 T 与预先设定的门限
值 γ 进行比较，即：
T < γ , T > γ ,
H0 H1
（3）
T = ∑ y(n) 2 N
（4）
在假设 H0 下，即主用户信号不存在时，T 服从自由度为 N 的中心卡方分布；在假设 H1 下，即主用户信号存在时，T 服从自由度为 N 的非中心卡方分布。
在寻找可由次用户使用的频带。认知无线电的研究热点之一是基于能量检测的频谱感知，该算法具有简单易用、计算成本低和易于实现等优点。
当主用户的传输参数未知时，可以选用能量检测方法。 [5-6] 在能量检测过程中，将某一频带中的接收信号与设定好的门限值进行比较，以确定主用户是否存在。这其实是一个二元决策问题。如果检测到的信号能量高于门限值，则判
定频带中正在进行传输，即信道繁忙；如果信号能量低于
门限值，则认为该频带是空闲的，次用户可以用其进行
传输。该问题可表述为：令 y 作为由信号和噪声之和得到的长
度为 N 的矢量。
y(n) = x(n)+ w(n)
（1）
式中：x（n）表示主用户信号；y（n）表示次用户接收到的信号； w（n）表示噪声信号，噪声假定为加性高斯白噪声且其均值为 0，方差为 σw2；N 表示感知期间内的样本数；n 表示样本序列号。
仿真研究了在噪声不确定情形下检测所需的样本数量和信噪比墙，以确保实现给定的检测概率和虚警概率。
关键词：认知无线电；频谱感知；能量检测；噪声不确定性；信噪比墙；频带
中图分类号：TP393

小样本能量检测中的双门限协作频谱感知

小样本能量检测中的双门限协作频谱感知陈晓思;杭燚灵【摘要】为了克服传统的能量检测方法需要大量的采样样本,且在低信噪比时检测性能不佳的问题,提出了一种小样本能量检测中的双门限协作频谱感知方法.该方法采用双门限有效减少了在低信噪比的情况下认知用户对主用户的干扰,利用多维高斯近似处理检测结果实现小样本能量检测,并且在融合中心使用硬判决中最适合实际应用的"大多数投票"原则做出最终判决.仿真结果表明,与传统能量检测、小样本能量检测双门限以及小样本能量检测单门限协作频谱感知等方法相比,小样本能量检测中的双门限协作频谱感知算法具有在小样本和低信噪比情况下也可以有效减少频谱感知过程中认知用户对主用户的干扰程度,降低能量检测的漏检概率,提高系统的检测性能等优点.%In order to overcome the drawbacks of demanding large quantity of samples for the conventional energy detection method and poor detection performance in the low SNR,a double-threshold cooperative spectrum sensing way is proposed in small sample energy de-tection. It adopts double-threshold to effectively reduce the interference of cognitive users to primary users in the low SNR,making the use of the cube-of-Gaussian approximation approach to implement small sample energy detection. In the center fusion, the proposed method takes a majority-voting rule which is the most suitable for practical using in hard decisions for the final decision. The simulation results show that compared to the conventional energy detection method,double-threshold energy detection method in small sample and single-threshold cooperative spectrum sensing method in small sample energy detection,the double-threshold cooperative spectrum sens-ing method in small sample energy detection can effectively reduce the interference of cognitive users to primary users in the condition of small sample size and low SNR,which can greatly reduce the miss detection probability of energy detection and improve the detection performance in the system.【期刊名称】《计算机技术与发展》【年(卷),期】2017(027)003【总页数】5页(P193-196,200)【关键词】协作频谱感知;小样本;能量检测;双门限;碰撞概率【作者】陈晓思;杭燚灵【作者单位】南京邮电大学通信与信息工程学院,江苏南京210003;南京邮电大学通信与信息工程学院,江苏南京210003【正文语种】中文【中图分类】TP31认知无线电(Cognitive Radio,CR)技术允许认知用户动态接入空闲的授权频谱，是一种有效解决频谱资源稀缺的方法。

基于数据融合的协作频谱感知方法研究毕业设计精品

基于数据融合的协作频谱感知方法研究摘要：认知无线电(Cognitive Radio，CR)是无线通信领域中为改善和提高频谱资源利用率而提出的一种新方法。

它作为一种革命性的智能频谱共享技术，已成为无线通信领域新的研究热点。

频谱感知技术是认知无线电最关键的技术之一。

基于此，本文以单用户能量检测方法为基础，对检测概率、漏检概率、虚警概率、门限值、信噪比等参数进行了仿真分析，并对其检测性能进行了分析。

针对于单用户能量检测受到信道衰落、阴影效应和噪声不确定性等因素的影响产生检测性能下降的情况，提出了数据融合的协作频谱感知方法，如与准则、或准则、K-N准则，并分析了不同融合方法的特点、性能及使用场景。

关键词：认知无线电；频谱感知；数据融合；协作检测Research of Cooperative Spectrum Sensing Based on Data Fusion Abstract: To improve and enhance the utilization of the spectrum resource in the field of the wireless communication，cognitive radio technology has been proposed as a new method．As a revolutionary intelligence technology，cognitive radio is becoming a hot research topic in the field of the wireless communication．Spectrum sensing technology is one of the most critical technologies in cognitive radio．Thus，this paper is based on the energy detection method in a sigle user．Meanwhile，this paper simulates and analyses some parameters such as the prob ability of detection，the probability of undetected，false alarm probability，threshold，SNR and so on．Besides，its detection performance is analyzed.Due to fading channel，shadowing effection，noise uncertains and other factors，the decline in detection performance happened，and cooperative spectrum sensing data fusion methods have been proposed，such as AND guidelines，OR guidelines，K-N guidelines．Lastly，this paper analyses the characteristics of the different fusion methods，performance and usage scenarios.Keywords: cognitive radio;spectrum sensing;data fusion;cooperative detection目录前言 (1)第1章绪论 (2)1.1研究的背景和意义 (2)1.2什么是认知无线电 (4)1.3国内外认知无线电技术的研究现状 (5)第2章认知无线电中的频谱感知技术 (7)2.1认知无线电频谱感知研究 (7)2.2基于接收机检测 (8)2.2.1基于干扰温度的检测 (8)2.2.2本振泄露功率控制 (9)2.3基于发射机检测 (10)2.3.1匹配滤波器检测 (10)2.3.2能量检测 (11)2.3.3周期平稳特征检测 (12)2.3.4频谱感知算法优缺点比较 (13)2.4协同检测 (13)2.5本章小结 (14)第3章单用户频谱感知的性能分析 (15)3.1 理想信道下能量检测法的算法分析 (15)3.2 不同信道下的能量检测性能分析 (18)3.2.1AWGN信道 (18)3.2.2Rayleigh衰落信道 (20)3.2.3 Rician衰落信道 (22)3.3 瑞利信道和高斯信道实际仿真验证 (24)3.4 能量检测各参数之间关系的研究 (26)3.5 单用户频谱感知的不足 (28)3.7 本章小结 (29)第4章数据融合的协作频谱感知的性能分析 (30)4.1 硬判决基本原理 (30)4.2 ...AND‟准则.. (31)4.3 ...OR‟准则.. (34)4.4 ...K-N‟准则 (36)4.5 实际无线通信环境下的联合频谱感知 (39)4.6 本章小结 (40)第5章总结与展望 (41)5.1 论文工作总结 (41)5.2 下一步研究方向 (41)参考文献 (43)致谢 (45)附录1 主要源程序 (46)附录2 外文翻译 (54)前言随着信息时代的到来，无线频谱已成为现代社会不可或缺的宝贵资源。

基于能量检测的频谱感知方法

基于能量检测的频谱感知方法
基于能量检测的频谱感知方法是一种常用的频谱感知技术。

该方法通过检测接收信号在不同频率上的能量水平来判断频谱的利用情况。

具体来说，首先使用无线接收设备接收环境中的信号，然后对接收到的信号进行能量检测，即测量信号在每个频率上的能量水平。

如果某个频率上的能量水平高于一个设定的阈值，则判定该频率正在被占用。

如果某个频率上的能量水平低于阈值，则判定该频率空闲可用。

基于能量检测的频谱感知方法具有简单、易实现的优点。

它不需要事先了解环境中可能存在的信号特性，也不需要对信号进行解调和解码。

只要能量水平的阈值合适地设定，就可以有效地感知到频谱的利用情况。

然而，基于能量检测的频谱感知方法也存在一些问题。

首先，由于不需要事先了解信号特性，该方法无法提供对信号类型和调制方式的详细信息。

其次，受到了噪声干扰的影响，可能存在误判的情况。

特别是在低信噪比环境下，会出现虚警和漏警的问题。

此外，该方法对于带有周期性占用的信号，如脉冲信号或频率切换信号等，可能会导致检测精度下降。

综上所述，基于能量检测的频谱感知方法在无线通信系统中有一定的应用价值，尤其适用于一些低复杂度和实时性要求不高的场景。

但在一些对频谱利用情况要求较高的场景，可能需要采用其他更加复杂的频谱感知方法。

基于FFT谱能量检测和互相关检测的快速频谱感知方法及其性能分析

Vol43 No5May2021第43卷第5期2021 年5 月系统工程与电子技术SystemsEngineeringandElectronics文章编号!001-506X(2021)051406-07网址 !www sys-elecom基于FFT 谱能量检测和互相关检测的快速频谱感知方法及其性能分析常虹，赵小强！，石海杰2（1西安邮电大学通信与信息工程学院,陕西西安710121；2.西北工业大学电子信息学院,陕西西安710129）摘要：为了解决无人机通信中频谱资源紧张的问题，利用认知无线电技术实现无人机动态共享地面通信的频谱资源°针对单用户频谱感知的遮挡效应问题，利用单用户移动检测，提出基于主用户空间范围内的频谱能量检测和互相关检测结合的二次检测方法，分析并给出所提算法在主用户随机出现情况下的虚警概率和检测概率的解析表示°仿真结果表明，所提算法在运算量较低情况下获得比已有单用户检测算法更好的检测性能°关键词：频谱感知；单用户移动检测；随机出现；二次检测中图分类号：TN 929.5 文献标志码：A DOI ：10. 12305/j. issn. 1001-506X. 2021 05. 30JointFFTspectrumenergyandcorrelationdetectionbasedfastspectrumsensingmethodandperformanceanalysisCHANG Hong 1* , ZHAO Xiaoqiang 1 , SHI Haijie 2(1. School of Communications and Information Engineering , Xi'an University of Posts and Telecommunications ,Xi'an 710121 , China# 2. School of Electronics and Information , Northwestern PolytechnicalUniversity , Xi an 710129 , China)Abstract ：In order to solve the problem of spectrum resource shortage in UAV Communication ,cognitiveradiotechnologyisusedtorealize UAV dynamicsharingofspectrumresourcesingroundcommunication Aimingatthe problem of sheltering for the single user spectrum sensing ,a double detection methodbasedonsingleuser mobile detectionwasproposed ,whichiscomposedofjointspectrumenergydetectionandcooperativedetectioninthescopeofprimaryuserspace Theanalyticrepresentationofthedetectionprobabilityandfalseprobabilityisanalyzed with randoma r ivalanddepartmentofprimaryuser Simulationresultsshowthattheproposedalgorithmhasbe t erdection performancethanothersingleuserdetectionalgorithminthecaseoflowcomputationKeywords ：spectrumsensing #singleusermobiledetection #randomarrival #doubledetection0引言无人机通信可以解决特殊环境下的应急通信问题，利用无人机空中自组织网络可以为地面用户提供无线覆盖，但是多跳传输会造成无人机网络容量不足的问题,利用频谱共享机制可以缓解频谱紧张的问题$目前的认知无线电主要利用授权信号的空时频空洞机会，利用常见的空域、频域、时域的频谱感知方法发现闲置频谱，供无人机使用无人机的工作频率范围是0. 84〜2. 4 GHz,与地面蜂窝基站工作频率和民用卫星通信频率重叠，因此无人机可以共享地面蜂窝网频谱56）$由于无人机飞行高度可达1 km,在搭载雷达或者装载频谱检测设备时可实现在大视野范围内频谱感知, 受地形影响较小,而且运动中的无人机需要快速切换于地面基站频率之间以适应地面基站分布密集的现状78 ,因此研收稿日期：2020 -07 - 13；修回日期：2020 - 08 - 18；网络优先出版日期：2020 - 12 - 21。

基于能效的井下认知传感器网络联合频谱检测

基于能效的井下认知传感器网络联合频谱检测梁泉泉【摘要】随着无线技术的发展,越来越多的通信标准被应用于井下环境中.本文讨论了利用认知无线电技术将基于802.15.4标准的无线传感器网络同其他井下无线网络融合的问题.在认知无线传感器网络中,引入联合频谱感知机制,即一组传感器节点协作完成频谱感知任务.越多传感器节点的参与将会获得越准确的检测结果,但也将造成更多的能量损耗.因此如何得到检测准确性与能量有效性的折中是该网络的一个关键问题.首先,分析得到了单节点检测能耗与检测性能之间的函数关系,求得在满足一定检测性能要求下的最短感知时间.其次,采用基于投票准则的融合机制,得到了最终检测性能与能耗之间的关系.【期刊名称】《测试科学与仪器》【年(卷),期】2014(005)001【总页数】5页(P46-50)【关键词】认知传感器网络;联合频谱检测;能效【作者】梁泉泉【作者单位】山东科技大学信息与电气工程学院,山东青岛266590【正文语种】中文【中图分类】TP393.03With the development of wireless communications and electronicstechnologies, wireless sensor networks (WSNs), have been widely used for environment monitoring in underground coal mine and attracted more and more attention[1].Curecently, IEEE802.15.4 standard[2] for low-rate wireless personal area networks (WPANs) is generally considered as one of technology candidates for WSNs[3], which can operate in any one of three frequency bands: around 868 MHz, 915 MHz, and Industrial, Scientific and Medical (ISM) band at 2 450 MHz.The unlicensed 2.45 GHz ISM band can host various networks with different standards, such as IEEE 802.11g wireless local area networks (WLANs), IEEE 802.16e wireless metropolitan area networks (WMANs) and IEEE 802.15.4 WPANs.Thus, the coexistence of these networks challenges the reasonable and efficient use of the scarce spectrum.Fortunately, cognitive radio technique has been proposed to implement opportunity sharing.It can not only sense the spectral environment over a wide frequency band, but also opportunistically provide wireless links according to spectrum sensing information, which satisfies the user communications requirements optimally[4].Concerning the devices operating in 2.45 GHz ISM band, the output power of 802.15.4 sensor nodes is very low, whereas the output power of 802.11g or 802.16e devices is very large.So using cognitive radio technique in wireless sensor networks will avoid the interference from other wireless users and improve the utility of scarce spectrum.Both wireless sensor networks and cognitive radio are very hot research topics.The coexistence of 802.11 WLANs and 802.15.4 sensor networks inISM band was investigated in Ref.[5].Pollin et al.proposed some distributed adaptation strategies for 802.15.4 sensor nodes to minimize impact of 802.11 interference.The proposed algorithms are based on increased spectrum scanning (at the cost of energy consumption and additional hardware requirements) or increased learning[6] to consider the lifetime maximization problem in wireless cognitive radio sensor networks.A joint design of cognitive radio and multi-carrier modulation to achieve high power efficiency was presented.In Ref.[7], a sensor-network-based sensing architecture was introduced that not only overcomes the issues identified, but also addresses the interference temperature model effectively.In cognitive radio sensor networks, spectrum sensing must be performed before the sensor nodes access the spectrum to avoid the interference from the other wireless users[8].In order to improve the reliability of the spectrum sensing, cooperation among sensor nodes need to be introduced.This spatial diversity can alleviate the problem of unreliable detection by single node.It should have a better chance of detecting the existence of other users through cooperation hence to avoid the interferences.However, too many sensor nodes involved in the sensing will result in excess energy consumption.To the best of our knowledge, the most important consideration is energy efficiency when a wireless sensor network is designed.On the other hand, for cognitive radio, a satisfactory sensing performance is a key target.Both of these characteristics should be considered in cognitive radio sensor networks.In this paper, according to the precondition that the detection performance should be satisfied, wetake energy efficiency and energy consumption balance into account.The relationship between energy consumption and detection performance is analyzed.As a result, a detection schedule matrix is proposed for the cooperative sensing in cognitive radio sensor networks.The paper is organized as follows.System model is introduced in section 1.In section 2, we first analyze the relationship between energy consumption and detection performance using energy detector for a single node.Then, we use voting fusion rule for the final detection decision making.The relationship between the final detection performance and energy consumption is analyzed.Finally, we conclude the paper in section 3.1 System modelWe consider a cluster-based 802.15.4 wireless sensor network shown in Fig.1, where n sensor nodes and one cluster head are involved in the cluster.The sensors may operate in one frequency channel among 16 possible ones for 802.15.4 networks operating in 2.45 GHz ISM band.The center frequency of these channels is defined asFc=2 405+5i, i=0,1, (15)(1)The measurement unit of Fc is MHz.The sub-bandwidth of each channel is 2.0 MHz.Due to the coexistence of multiple standards, there may be some other wireless users (e.g., WLAN/WMAN users, called primary users hereafter) occupying some of the channels.In order to avoid the interference caused by these users, the sensor network first detects the 16 spectrum bands.The ones not occupied will be used by the sensors forcommunication.Cooperative sensing is employed, not only to improve the detection performance, bu t also to reduce each node’s energy consumption and balance the whole network.The cluster head, which has stronger processing ability and energy storage, is responsible for scheduling the sensors to sense the spectrum.Assuming that the prior knowledge of the primary users is unknown, the sensors exploit the energy detector[9] for local spectrum sensing, and make binary hypothesis that local decision about whether a certain channel is occupied or not.Then the cluster head collects these local decisions to make a final one.Basic assumptions in our research are listed as follows:1) All users experience additive white Gaussian noise (AWGN) channels.2) Uniform energy detectors are placed in the sensor nodes.Fig.1 Cluster-based cognitive sensor networks2 Energy-aware cooperative spectrum sensingIn most cases, the sensor nodes are powered by batteries that can not be replaced.The energy efficiency is the crucial prerequisite in designing the wireless sensor networks.Hence, different from other cognitive radio systems, the cognitive radio sensor networks are more sensitive to energy consumption.So in this section, we consider the energy-aware cooperative spectrum sensing strategy.Firstly, we give the relationship between detection time, which impacts the sensing energy consumption directly and the local detection performance.Moreover, the relationship between the final detection performance using voting fusion rule and energy consumption is analyzed.2.1 Local sensingFor each sensor node, we consider the condition of detecting the presence or absence of signals from the primary users in 16 interest bands.By sampling the band of interest at Nyquist rate, a binary hypothesis testing problem is adopted aiming to distinguish the following hypotheses:H0: x[n]=w[n]n=0,1,…,N-1,H1: x[n]=s[n]+w[n]n=0,1,…,N-1,(2)where s[n] are independent and identically distributed signal samples with the distribution and w[n] are independent and identically distributed noise with w[n]～N(0,σ2).For energy detector, we can describe decision statistic as(3)where λ is decision threshold.In this case[10],(4)Then the probability of detection and false alarm can be generally computed byPd=Pr{T(X)>λ; H1}=Pf=Pr{T(X)>λ; H0}=(5)For large N, using central limit theorem, we can get Gaussian approximations of Pd and Pf as and respectively.The sample complexity of detection for energy detector then can be calculated byN=2[(Q-1(Pf)-Q-1(Pd))SNR-1-Q-1(Pd)]2,(6)where is a variable denoting signal-to-noise ratio (SNR).According to Nyquist sampling theorem, the least sensing time of each sensor node is given by(7)where W is the bandwidth of the interest band.Thus, we can get the least energy consumption of a single node according to the detection performance requirements of Pd and Pf,where Psen is sensing power of the sensor node, and in most cases it equals the receiving power.Fig.2 shows the energy consumption according to different requirements of detection, where Psen=48 mW, W=2 MHz. Fig.2 Energy consumption for local sensing according to detection performance requirements2.2 Final decision makingTo minimize transmission energy consumption and overhead of sensingdata, every sensor node will transmit 1-bit local sensing decision to the cluster head for fusion.Considering the energy and processing constraint of the sensor nodes, in this paper we will use voting fusion rule[11] for simplification.For an n-sensor cluster, the final sensing decision is given by(9)where ui is the local decision made by the i-th sensor.The fusion rule means that when at least k out of n sensor nodes detects the primary user, the cluster head decides that the primary user is present.In this case, the detection and false alarm probability of the final decision are obtained as(10)where and are the detection probability and false alarm probability of the i-th sensor node, respectively.It is obvious that the value of k will affect the detection probability and false alarm probability of the final decision.In the case of k=1, the voting rule turns to be OR rule.Moreover, the rule turns to be AND rule when k=n. Simulation results are presented in Fig.3 to evaluate the energy consumption vs.Pd_final with different values of k.In the simulation, we take a 10-sensor cluster as an example.The final probability of false alarm is fixed to be 10-6.Other parameters are set as Psen=48 mW, W=2 MHz and SNR=3 dB.The energy consumption is defined as the sum of energyconsumed by the nodes.From Fig.3, we can find that the energy performance becomes better and better as k decreases.When k=3, the energy performance achieves the best.Fig.3 Energy consumption vs.final detection probability using voting fusion ruleFig.4 Average energy consumption at different value of kHowever, the simulation above is just for detection one time, and simulation results shown in Fig.4 gives the performance of average energy consumption for each node with the detection of 50 times.In this case, we can find that for a certain value of the total detection probability, whenk=1, the performance of the average energy consumption for each node is not the best.There is no obvious trend for the average energy consumption, which means the energy consumption among the sensor nodes is lopsided.3 ConclusionIn design of the underground cognitive radio sensor networks, both the characteristics of cognitive radio systems and wireless sensor networks should be considered.In this paper, we consider a cluster-based cognitive radio wireless sensor network which is established on IEEE 802.15.4 standard.We explore a design for the tradeoff energy efficiency and detection performance after cooperative spectrum sensing in such a network.At first, we investigate how to fix the detection time for the sensing according to the detection performance requirements.Then, the voting fusion rule is adopted for the final decision making.Finally, therelationship between the final detection performance and the energy consumption is analyzed.References[1] Akyildiz I F, SU Wei-lian, Sankarasubramanian Y, et al.A survey on sensor networks.IEEE Communications Magazine, 2002, 40(8): 102-114. [2] IEEE Std 802.15.4-2003.IEEE standard for information technology-telecommunications and information exchange between systems-local and metropolitan area networks specific requirements part 15.4: wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs).2003.[3] Gutiérrez J A, Callaway E H, Barrett R L.Low-rate wireless personal area networks.IEEE Press, 2004.[4] Haykin S.Cognitive radio: brain-empowered wireless communications.IEEE Journal on Selected Areas in Communications, 2005, 23(2): 201-220.[5] Pollin S, Ergen M, Timmers M, et al.Distributed cognitive coexistence of 802.15.4 with 802.11.In: Proceedings of the 1st International Conference on Cognitive Radio Oriented Wireless Networks and Communications, Mykonos Island, Greek, 2006: 1-5.[6] GAO Song, QIAN Li-jun, Vaman D R, et al.Energy efficient adaptive modulation in wireless cognitive radio sensor networks.In: Proceedings of IEEE International Conference on Communications, 2007: 3980-3986. [7] Nandagopal S, Cordeiro C, Challapali K.Spectrum agile radios: utilization and sensing architectures.In: Proceedings of the 1st IEEEInternational Symposium on New Frontiers in Dynamic Spectrum Access Networks, Baltimore, USA, 2005: 160-169.[8] Cabric D, Mishra S M, Brodersen R W.Implementation issues in spectrum sensing for cognitive radios.In: Proceedings of Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, USA, 2004: 772-776.[9] Urkowitz H.Energy detection of unknown deterministic signals.In: Proceedings of the IEEE, 1967, 55(4): 523-531.[10] Proakis J G.Digital communications.4th edition.New York: McGraw Hill, 2000.[11] Klein L A.Sensor and data fusion concepts and applications.Society of Photo-Optical Instrumentation Engineers (SPIE), Bellingham, WA, USA, 1999.。

基于能量检测的频谱感知方法分析

文章编号　100426410(2009)0320070203基于能量检测的频谱感知方法分析陈　艳1,2,杨新伦1(1.广西工学院计算机工程系,广西柳州　545006;2.电子科技大学通信抗干扰技术国家级重点实验室,四川成都　611731)摘　要:简要介绍了认知无线电中基于能量检测的频谱感知方法,在分析能量检测法原理和性能基础上,研究了噪声的不确定性对检测性能的影响.仿真表明,能量检测法是一种简单、有效的频谱感知方法.关　键　词:认知无线电;频谱感知;能量检测法;噪声不确定中图分类号:TN929.5 文献标识码:A收稿日期:2009-07-13基金项目:广西工学院自然科学基金(0704212)资助.作者简介:陈艳(1979-),女,安徽东至人,广西工学院计算机工程系教师,在读研究生.0　引言随着无线通信技术的快速发展,频谱匮乏问题逐渐凸显,频谱资源已成为非常宝贵的资源之一.而FCC (美国联邦通讯委员会)的调查表明,已分配的频谱在时间和空间上的利用率极低.例如,某些频段划分给某种行业,但该行业并没有全部利用整个频段,有相当一部分频段闲置不用.当前分配的绝大多数频谱利用率为15%～85%.因此,FCC 认为当前存在的主要问题并不是没有频谱可用,而是现有的静态频谱分配方式导致频谱资源没有被充分利用[1]. 认知无线电(Cognitive Radio ,简称CR )的概念最早由Mitola 于1999年提出的.它通过对频谱环境的感知,将特定时间、特定空间上的空闲频谱分配给未授权用户,以提高频谱利用率.这种动态的频谱共享方式能大大提高频谱利用率[2]. 频谱检测是认知无线电的关键技术,CR 用户必须实时监测频谱变化,以避免与授权用户发生碰撞.频谱检测的精度和可靠度决定了是否会侵害授权用户的正常通信.大量文献对频谱检测技术进行了深入研究,目前研究较多的频谱检测方法有:匹配滤波器法、循环平稳特征检测法、能量检测法等.匹配滤波法需要知道授权用户信号的先验信息,如调制类型、脉冲成型、分组格式等;循环平稳特征检测法利用信号的谱相关特征进行检测,低信噪比情况下有较好的检测性能,但计算复杂度较大;能量检测法是一种传统的检测方法,最大优点是无需知道信号的先验知识,而且实现简单,因此,可以将能量检测法应用到认知无线电中,作为预先的粗检测以提高检测效率.由于能量检测法对噪声的适应能力有限,在认知无线电中还需要进一步研究噪声对检测性能的影响[3].本文主要研究能量检测法的性能,并通过仿真验证其有效性.1　能量检测法原理及性能分析能量检测法是一种有效的信号检测方法,是指将信号在特定时间内的能量,与预先设定的门限值进行比较、判决,获得检测结果.能量检测法用在指定频段内即为基于能量检测的频谱感知方法.1.1　在噪声确定条件下能量检测法的模型及算法分析基于能量检测的频谱感知方法的研究可以看作是二元假设检验问题:H 0:x (n )=w (n ),n =0,1,…,N -1,(1)第20卷　第3期广西工学院学报 Vol 120　No 13　2009年9月　JOURNAL OF GUAN GXI UN IV ERSIT Y OF TECHNOLO GY Sep 12009　H 1:x (n )=s (n )+w (n ),n =0,1,…,N -1.(2) 其中,信号s (n )是均值为0,方差为σ2s 的高斯过程;噪声w (n )假定为加性高斯白噪声(AW GN ),零均值,双边功率谱密度为N 02,它与信号是相互独立的;N 为采样数(检测时间).奈曼—皮尔逊准则是当前认知无线电中评估检测性能的主要准则,包含两个重要参数:检测概率(P d )和虚警概率(P f ).频谱感知的任务就是要根据该准则区分上面两种不同的假设,从而判定在该时刻、该频段范围是否存在授权用户在通信.可采用恒虚警的方法,首先确定虚警概率P f ,然后计算不同信噪比(S N R )情况下的检测概率来衡量检测性能.认知无线电要求尽可能地保护授权用户不受到干扰,即主要授权用户的检测概率P d 越大越好. 图1给出了能量检测的流程图.接收信号首先被带宽为W 的带通滤波器滤波,然后通过一个平方律检测器和一个观察时间为T 的积分器,最后进行比较判决.　input noise 　pre 2filter (W ) x (t ) (・)2 V =1T ∫t t -T (・)2d τ V <V T ∶H 0　V >V T ∶H 1图1　能量检测法的流程根据假设检验模型,检测统计量为V ′=1N 02∫T 0x 2(t )d t (3) 只有噪声存在时,检验统计量V ′服从自由度为2TW 的中心χ2分布,即H 0∶V ′～χ22TW ;有信号存在时,检验统计量V ′服从自由度为2TW 的非中心χ2分布,非中心参数为λ,即H 1∶V ′～χ′22TW (λ).其中λ=E s /N 02为信噪比S N R ,E s 为信号能量.在噪声功率确定的情况下,虚警概率:P f =P{V ′>V T ′|H 0}=P{χ22TW >V ′T }(4)检测概率:P d =P{V ′>V T ′|H 1}=P{χ′22TW (λ)>V ′T }(5) 从上面的分析可以看出,当确定了虚警概率P f 后,就可以由(4)式计算出检测门限V ′T .将V ′T 代入式(5),就可以得到不同信噪比情况下的检测概率P d [425].1.2　嗓声不确定对检测性能的影响在上面的讨论中,假设接收噪声是高斯噪声,而且它的能量值在所有频段上都是不变的.但是实际上,噪声不仅仅来自于接收机和热噪声,还来自于一些不可知的外部环境,所以实际噪声只是接近于高斯噪声,噪声能量在一些频带上也不确定.为了研究噪声的不确定性对检测性能的影响,假设接收噪声还是高斯噪声,噪声能量是一个在已知范围内取值的未知变量[627].例如,取噪声不确定度为x dB ,噪声能量在[σ2no min al ,α3σ2no min al ]内取值,其中α=10x/10.因此,σ2∈[σ2no min al ,α3σ2no min al ](5)σ2≤σ2no min al +σ2s (6)S N R no min al ∈[S N R no min al ,S N R no min al +x ](7) 由式(5)、(6)、(7)得:S N R no min al ≤10log 10(10x/10-1)(8)2　实验与仿真结果 IEEE802.22系统要求检测器能够在-10dB 以下接收首要用户信号信噪比,并在保持虚警概率P f =1%～10%的同时提供P d =90%～95%的检测概率[829]. 利用蒙特卡罗仿真方法,构建仿真模型:假设发送信号为BPSK 信号,信道噪声为加性高斯白噪声,分别做了以下两组仿真加以说明.图2是在噪声确定的条件下,对检测概率与信噪比之间关系进行的仿真.图3是噪声不确定度x 分别取0dB 、1dB 、2dB ,虚警概率P f 取1%时,对检测概率及信噪比之间关系进行的仿真.17　第3期陈　艳等:基于能量检测的频谱感知方法分析从图2、图3中可以看出:(1)在噪声确定条件下,虚警概率越低,检测概率越高,其检测性能比较令人满意;(2)随着噪声不确定程度的加大,检测概率Pd 的值在减小,检测的可靠性也在降低;(3)在信噪比接近-2dB 时,检测结果都值得是信赖的.图2　噪声确定条件下检测性能图3　噪声不确定条件下检测性能3　结语频谱感知技术是认知无线电系统中的关键技术之一,准确的感知出在任意时刻、任意频段上,是否存在授权用户是频谱感知所要解决的问题.能量检测法是常用的一种频谱感知方法,它只需要知道被检测频段内信号的能量而不需要其他的先验知识.但是能量检测法的判决门限较难设置,当噪声不确定时,对检测性能的影响很大.另外,能量检测法不能区分出有用信号、干扰及噪声,因此更不能区分出接收信号的类型和调制方式等.这些缺陷限制了能量检测法在某些情况下的使用.参　考　文　献:[1]Ian F.Akyildiz ,Wou 2Y eol Lee ,Mehmet C.Vuran ,et al.NeXt generation/dynamic spectrum access/cognitive radio wirelessnetworks :a survey[J ].Computer Networks ,2006,50:212722159.[2]Cabric D ,Mishra S M ,Brodersen R W.Implementation issues in spectrum sensing for cognitive radios[C].Sacramente ,USA :Signals ,Systems ,and Computers ,2004(1):7722776.[3]A.Sahai ,D.Cabric.S pectrum sensing :Fundamental limits and practical challenges[C].Mary land :DySPAN 2005tutorial partI ,2005:12138.[4]Harry Urkowitz.Energy detection of unknown deterministic signals[J ].Proceedings of the IEEE ,1967,55(4):5232531.[5]D.Cabric ,achenko ,R.W.Broadersen.Experimental study of spectrum sensing based on energy detection and networkcooperation :Proceedings of the first international workspop on Technology and policy for accessing spectrum [C ].Boston ,Mas 2sachusetts :ACM ,2006.[6]R.Tandra ,A.Sahai.SNR walls for signal detection[J ].IEEE Journal of Selected Topics in Signal Processing ,2008,2(1),4217.[7]Tandra R ,Anant Sahai.Fundamental limits on detections in low SNR under noise uncertainty[C].Piscataway NJ ,USA :IEEESignal Processing ,2005(1):4642469.[8]IEEE 802.22205/0007r46,Functional requirements for the 802.22WRAN standard[S].[9]Shellhammer S J ,Shankar R ,Dandra J.Performance of power detector sensors of DTV signals in IEEE 802.22WRANs[C].New Y ork ,USA :Proceedings of the first international Workshop on Technology and Policy for accessing spectrum ,2006:4212.(下转第77页)27广西工学院学报第20卷[9]蔡伟刚.Nios Ⅱ软件架构解析[M ].西安:西安电子科技大学出版社,2007.Implementation of 128364display interface modulebased on processor Nios ⅡL IU Tong ,CAI Qi 2zhong(Electronic Engineering Department ,Guangxi University of Technology ,Liuzhou 545006,China )Abstract :This paper introduces the system of the embedded processor Nios Ⅱbased on SOPC technology.For the 128364LCD module interface ,we use HDL to finish logic interface on the basis of the timing analysis ,give the method of custom logic and basic programming applications in Nios ⅡIDE ,and achieve the application of the 128364LCD in the system of embedded processor Nios Ⅱ.K ey w ords :LCD ;Nios Ⅱ;SOPC ;HD61202(上接第72页)Analysis on spectrum sensing based on energy detection in cognitive radioCHEN Yan 1,2,YAN G Xin 2lun 1(puter Engineering Department ,Guangxi University of Technology ,Liuzhou 545006,China ;2.National K ey Laboratory of Communication ,University of ElectronicScience and Technology of China ,Chengdu 611731,China )Abstract :This paper briefly presents the spectrum sensing method based on energy detection in cognitive radio ,analyses the theory and performance of energy detection method ,and discusses the impact of noise power uncer 2tainty on detection puter simulation shows that the energy detection method is a simple and effective method of spectrum sensing.K ey w ords :cognitive radio ;spectrum sensing ;energy detection method ;noise power uncertainty 77　第3期刘　彤等:基于Nios Ⅱ处理器的128364液晶显示接口的实现。

基于改进型能量检测的加权协作频谱感知算法

基于改进型能量检测的加权协作频谱感知算法赵小龙;赵杭生;曹龙;许金勇【摘要】针对单节点能量检测法存在的“隐藏终端”和检测准确性低以及协作频谱感知算法大多采用等权重进行数据融合，未考虑不同节点所处的通信环境对检测性能的影响等问题，提出一种基于改进型能量检测的自适应加权协作频谱感知算法。

该算法通过对单节点能量检测方法的改进，在单节点检测错误概率最小的条件下，导出了信噪比与判决门限的关系式，利用二分法求得不同信噪比下的动态门限值，得到相应的虚警概率和检测概率，以虚警概率和检测概率的函数作为加权因子进行数据融合。

仿真结果表明，所提算法使协作感知系统在低信噪比条件下也能获得可靠的检测性能。

%The single-node energy detection has the drawbacks of low detection accuracy and“hidden terminal”while coopera-tive sensing algorithms usually use equivalent weights for data fusion instead of considering the influence to the detection perfor-mance resulting from communication environment of different nodes. In order to solve the above problems, this paper proposes an adaptive weighted cooperative spectrum sensing algorithm based on improved energy detection which improves single node energy detection and derives the relationship between Signal-to-Noise Ratio(SNR)and decision threshold under the condition of minimal error probability. This paper adopts dichotomy to get dynamic threshold values and corresponding false probability and detection probability at different SNR. It regards the function of false probability and detection probability as the weighted factor to do data fusion. The simulation results show that the proposed algorithm canachieve reliable detection performance of cooperative sensing system with low SNR.【期刊名称】《计算机工程与应用》【年(卷),期】2013(000)024【总页数】5页(P61-64,129)【关键词】认知无线电;协作频谱感知;自适应加权;二分法【作者】赵小龙;赵杭生;曹龙;许金勇【作者单位】解放军理工大学通信工程学院，南京210007; 南京电讯技术研究所，南京210007;南京电讯技术研究所，南京210007;解放军理工大学通信工程学院，南京 210007; 南京电讯技术研究所，南京 210007;南京电讯技术研究所，南京210007【正文语种】中文【中图分类】TP393频谱感知技术[1]作为实现认知无线电的关键技术之一，其自认知无线电诞生以来，已经获得了广泛的研究，常用的频谱感知技术有能量检测、匹配滤波器检测和周期循环平稳特征检测[2]，其中能量检测因为运算量和复杂度比较低，且不要求信号的先验信息而受到广泛关注。

基于能量检测的频谱感知方法

Paf = P ( T >γ| H0 ) , 则 Q
2 γ - Nσw
,是有一定的优越性的 . 由于能量检测法对噪
声的适应能力有限 , 在认知无线电中还需要进一步研究噪声对检测性能的影响 . 同时 ,由于无线电波在传输过程中的衰减和损耗 , 单纯依靠一个终端或节点的检测会出现“ 隐藏终端 ” 问题
Abstract: Cognitive radio ( CR ) is viewed as a novel app roach for im p roving utilization of the p recious radio spectrum. This paper first review s the background and concep ts of CR , and then investigates energy2detection based spectrum sensing by focusing on the spectrum sensing function. To deal w ith the p roblem of local sensing characteristic in w ireless signal detections in the energy detection p rocess, a distributed M 2cooperative detection schem e is p resented. Analytical detection p robability based on the
第 15 卷第 1 期 2009 年 2 月
上海大学学报 (自然科学版 )
JOURNAL OF SHANGHA I UN I V ERSITY ( NATURAL SC IENCE )

课程报告-基于邻域侦听的协作频谱感知方法

基于邻域侦听的协作频谱感知方案1 引言认知无线电(cognitive radio,CR)作为下一代通信领域的关键技术，可以有效地解决由于当前条块化的固定频谱使用模式导致的频谱利用率低下问题[1]-[2]。

频谱感知技术是CR 的关键技术之一，它一方面要求次级用户(Secondary User, SU)能够准确的感知多维频谱空洞，另一方面SU 需要控制对主用户(Primary User, PU)的传输干扰，保护PU 利益。

由于单个感知节点进行频谱感知通常检测性能通常容易受到信道的多径衰落、阴影效应以及隐藏终端等因素影响导致检测性能不理想，因而利用多感知节点进行协作频谱感知，从而可以获得分集增益以提升感知性能[3]。

然而，在单个感知节点能量受限的约束条件下，协作感知网络面临的挑战之一就是全网的能量开销即如何节省全网能量延长节点寿命。

通常，协作频谱感知的能耗主要包括各次级感知节点的本地感知和信息上传两部分能耗。

当次级感知节点的数量较大时，次级感知节点向融合中心上传感知结果的传输能耗不可忽略。

文献[4]给出了一种选择性信息上传机制，即次级感知节点感知到信道处于某一个指定状态(空闲/繁忙”)时才发送决策结果，这样在一定程度上降低了感知节点能耗，延长节点寿命。

文献[5]提出了一种基于删除的协作频谱感知策略，以减小无信息量的感知的上传带来的额外的能量消耗，文献[6]基于这一思想，将其扩展至有差异化的删除上传机制，充分利用相邻感知时隙结果的相关性从而获得显著能量的消耗。

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