认知无线电中基于自相关矩阵的协作盲感知

为了克服传统的认知无线电频谱感知算法存在的不足,提出了一种基于自相关矩阵的协作式盲频谱感知方法,以提高频谱感知能力.该方法从样本自相关矩阵中提取检测统计量,采用双门限混合判决方法得到本地检测结果.认知基站将本地检测信息分成两类,并对软判决信息实施可靠性融合,最后给出全局判决结果.该方法是一种盲感知方法,不需要知道主用户...     

基于Dempster-Shafer证据理论的协同频谱感知算法

认知无线电的首要任务是频谱感知,单个认知用户的频谱感知结果容易受到衰落和多径的影响,本文结合证据理论研究了多个认知用户的协同频谱感知问题.为了在融合中心未知先验信息的条件下实现对授权用户的有效检测,文章基于Dempster-Shafer证据理论,对认知用户的本地感知结果提取证据,然后在融合中心进行融合并判决,提出了一种基于Dempster-Shafer证据理论的协同频谱感知算法.仿真结果表明,该算法能够在融合中心未知先验信息情况下,获得较好的频谱感知性能.     

基于SNR比较的协作频谱感知研究

频谱感知是认知无线电中通过发现可用频率资源提高频谱利用率的关键技术。协作频谱感知能一定程度上消除多径和阴影的影响,为了进一步提高频谱检测性能,提出了一种基于SNR比较的协作频谱感知算法。在此算法中,各个认知用户将各自的本地判决结果和自身的SNR值发送到数据融合中心,通过SNR比较,对各CR用户进行筛选,选出符合算法要求的CR用户参加协作检测,进行数据融合。计算机仿真表明,该算法能有效提高检测性能,并减少判决融合的节点数量。     

基于非参量累积和的合作频谱感知

针对认知无线电网络中频谱感知的检测时延降低问题,提出了基于非参量累积和的合作频谱感知算法。本地认知用户预处理频谱观测数据,获得观测数据相对于信念值的正向漂移和负向漂移。为了缩短检测延迟,认知用户只将数据的正向漂移同步传输至融合中心。融合中心融合正向漂移得到判决信息,采用非参量累积和算法依时间序列顺序累加判决信息,判断主用户是否正在使用授权频段。为了解决不传输负向漂移引起的虚警问题,改进算法提出融合中心可以保留首次判决,经过等待时间间隔后再作出最终判决。相对于传统的软融合算法,改进融合规则的合作频谱感知算法具有较低的检测延迟。     

认知无线电中基于感知信息量化的合作频谱感知

针对认知无线电频谱感知性能的提高与传输开销的矛盾,本文提出了一种基于感知信息量化的合作频谱感知方案。该方法对各认知用户的本地检测结果采用3个判决门限进行2比特位的量化,在融合中心处对量化信息加权处理后,得到最终主用户是否存在的判决。本文详细研究了采用该方案的检测概率,虚警概率和吞吐量,理论分析和仿真结果表明,该方法在每个认知用户只增加1bit传输开销的前提下能够极大的改善检测性能,实现了检测性能与传输开销的较好权衡。     

认知无线网络中的智能协作频谱感知机制研究

作为智能体,认知无线电应具有智能学习和智能判决的能力。为充分发掘认知用户的智能体特性,本文提出一种基于支持向量机和模糊积分的智能协作频谱感知机制。该机制将协作频谱感知模型转化成基于模糊积分的多分类器融合模型,其中每个认知用户均被看作一个独立的支持向量机分类器,单个感知周期内得到的采样数据作为分类器的输入,分类器的概率输出将被发送至融合中心,融合中心采用模糊积分算法将各分类器得到的结果进行融合并判决。该机制充分挖掘了认知用户在频谱感知阶段的“智能学习”能力和信息融合阶段的“智能判决”能力,仿真结果进一步表明,与单一的分类模型相比,本文提出的智能协作频谱感知机制具有更高的检测概率和更低的虚警概率。      

基于自适应决策融合的合作频谱感知方法

频谱感知是认知无线电(CR)的关键技术之一。在该机制中,对主用户(PU)信号的可靠检测是实现CR的前提。提出一种基于自适应决策融合的合作频谱感知算法用于频谱感知,该算法通过估计PU的先验概率与各个CR用户(SU)的漏检及虚警概率,然后运用Chair-Varshney准则对局部判决进行决策融合以得到全局判决。仿真结果表明,采用该方案的全局虚警和漏检概率明显低于单个SU,可有效提高CR系统频谱感知的可靠性。     

一种SSDF攻击检测的证据理论合作频谱感知算法

现有的基于证据理论的合作频谱感知认为所有认知用户都是诚实的,没有考虑恶意用户的存在。当恶意用户篡改本地感知的结果,发送错误的数据到数据融合中心,将会降低合作频谱感知的性能,这种攻击称为频谱感知数据篡改（spectrum sensing data falsification,SSDF）攻击。由于恶意用户发送的证据与其它认知用户的证据存在差别,本文使用Jousselme距离来衡量证据的可信度,提出一种SSDF攻击检测算法。数据融合中心接收所有认知用户的证据,让可信度低的证据不参与融合判决,可信度高的证据根据可信度进行加权融合。仿真结果表明,所提出的SSDF攻击检测算法在恶意用户发起SSDF攻击时可以很好的改善频谱感知性能。      

集中式双非参量累积和的合作频谱感知技术

针对认知无线电网络频谱感知的检测时延降低问题，提出了一种采用双非参量累积和的合作频谱感知方法。在单主用户认知网络中，本地认知用户执行非参量累积和算法，处理能量观测数据，以缩短检测时延，减少对主用户先验信息的需求，同时为了降低带宽开销，只向融合中心传输1比特的预判决结果。融合中心在噪声干扰下接收融合预判决结果，执行非参量累积和算法，累加判决统计量，对主用户信号是否存在进行最终判决。仿真结果表明，在10%的虚警概率下，相比于传统的非参量合作频谱感知算法，双非参量累积和算法具有较低的检测延迟。      

利用自相关矩阵的量化合作频谱感知方法

为了改善认知无线电的频谱感知性能,提出一种基于自相关矩阵的量化合作频谱感知方法,对各认知用户的本地检测结果采用多个判决门限进行有限位的量化,在融合中心采用加权投票准则对该量化信息综合处理,得出最终判决.该方法是一种盲感知方法,不需要知道主用户信号的先验知识和噪声方差信息.具体讨论了一种2 bit量化合作频谱感知方案,并将其与传统的基于能量检测的合作频谱感知方案作比较.理论分析和仿真结果表明,该方案可以有效提高频谱感知能力.     

频谱协作认知检测算法研究

为实现对低信噪比主用户信号进行可靠的检测,论文结合基于循环平稳的主用户信号具有不同循环频率的特性,以及次用户间的协作检测能有效地克服无线环境中多径衰减和阴影效应等不利因素影响的特点,提出了基于循环平稳特性的协作频谱检测算法。算法利用次用户对主用户信号的多个不同循环频率进行协作检测,并对各个次用户独立的检测结果进行权值数据融合,从而实现最终全局判决。同时,为进一步改善检测性能,论文研究了基于最大比合并的权值优化方法。仿真实验表明即使信号在较低信噪比情况下,论文研究的算法亦具有较好的检测性能。      

Blind Spectrum Sensing Algorithms for Cognitive Radio Networks

In a cognitive radio network, the spectrum that is allocated to primary users can be used by secondary users if the spectrum is not being used by the primary user at the current time and location. The only consideration is that the secondary users have to vacate the channel within a certain amount of time whenever the primary user becomes active. Thus, the cognitive radio faces the difficult challenge of detecting (sensing) the presence of the primary user, particularly in a low signal-to-noise ratio region, since the signal of the primary user might be severely attenuated due to multipath and shadowing before reaching the secondary user. In this paper, a blind sensing algorithm is derived, which is based on oversampling the received signal or by employing multiple receive antennas. The proposed method combines linear prediction and QR decomposition of the received signal matrix. Then, two signal statistics are computed from the oversampled received signal. The ratio of these two statistics is an indicator of the presence/absence of the primary signal in the received signal. Our algorithm does not require the knowledge of the signal or of the noise power. Moreover, the proposed detection algorithm in this paper is blind in the sense that it does not require information about the multipath channel distortions the primary signal has undergone on its way to reaching the secondary user. Simulations have shown that our algorithm performs much better than the commonly used energy detector, which usually suffers from the noise uncertainty problem.      

Sensing Transmission Tradeoff Over Penalty for Miss Detection in Cognitive Radio Network

In this paper, a challenge issue ‘sensing and data transmission trade-off’ is addressed. Different from existing efforts, penalty is taken as a rule of regulation for spectrum sensing and access in order to possibly protect primary’s activity. Specifically, secondary user needs to pay rental fee to primary user for opportunistic access. However, penalty will be charged from secondary user as well if it has transmitted on the miss estimated spectrum, in which, an negative secondary raw utility will be generated when the penalty value is properly decided. Thus the scheme will further force secondary user to improve its sensing accuracy and select the best transmission strategy when compared with conventional sensing access model. Based on the scheme, in this research, the bounds for proper rental and penalty prices are derived. Further, within both proper prices region, an optimization problem for setting sensing duration and transmission power is formulated. Through investigation, convexity of the problem is hard to be obtained at global view. But thanks to the special property that the problem is local convex when fixing any one of targeted variables, a Lagrange method based iterative algorithm is presented to find solution within a decent convergence of no more than 10 iterations.     

Cumulant Analysis of Dual Sequential Ratio Testing for Cognitive Radio Spectrum Sensing

In this paper we present a novel methodology to evaluate the cumulants of the sample random distribution in Sequential Analysis. By means of this we present a modification to the Dual Sequential Ratio Test algorithm used for primary user detection in cognitive radio networks. In the considered scenario, the secondary users utilize the sequential ratio test to sense the wireless channel looking for the presence of a transmitting primary user. A Fusion Centre (FC) gathers the decisions from the secondary users in order to perform a sequential ratio test and achieve a final verdict. The instance when the FC starts collecting the decisions is optimized in such way that the total time to make a decision is minimized. This allows for a better energy usage from the secondary users along with a reliable and fast detection of the primary user. Some numerical examples are considered in order to validate our results.     

利用功率谱极值和几何平均的频谱感知算法

为了提高基于功率谱的频谱感知算法抗噪声不确定性、抗频偏及低信噪比下检测性能,本文利用功率谱的部分样本平均估计最大值,以降低信号频偏对频谱感知性能影响;利用功率谱的最大值与最小值之差与功率谱几何平均之比作为判决统计量,以尽可能消除噪声影响及保留主用户信号;推导得到了检测门限表达式,表明该算法对噪声不确定性不敏感。加性高斯白噪声信道和瑞利衰落信道下的仿真结果表明:该算法频谱感知性能优于已有的基于功率谱的频谱感知算法,降低了未知载波频偏和噪声不确定性对频谱感知算法性能的影响,该算法能够有效检测实际信号。      

Cooperative spectrum sensing scheduling scheme based on discrete particle swarm optimization algorithm

The statistical characteristics of the network state changes were analyzed by using the CTMC model.Considering the difference of each secondary user’s sensing ability,two integer programming problems on cooperative sensing scheduling scheme were established from two aspects:the primary users and the secondary users respectively.A discrete particle swarm optimization algorithm was proposed to solve the integer programming problems,and compared with the traditional random scheduling scheme and greedy scheduling scheme based on SNR.The simulation results show that the cooperative sensing scheduling scheme based on discrete particle swarm optimization algorithm is superior to random scheduling scheme and greedy scheduling scheme based on the SNR,which gets a higher spectrum sensing accuracy.     

优化的最佳中继协作频谱感知

在基于中继的协作频谱感知中,尽管通过引入认知中继可有效提高协作频谱感知性能,然而认知中继的引入也会带来额外的系统开销及复杂度增加问题。为了节约系统开销,本文在前期取得研究成果的基础上,进一步提出了一种基于删余的最佳中继协作频谱感知方案,只有当次用户检测到主用户信号且目标次用户的报告信道衰落严重时,才申请认知中继的协作传输,同时目标次用户将其检测到的感知信息发送到认知中继;最后,分别从检测性能和次系统可获得的容量角度对所提方案下的协作频谱感知性能进行了理论分析。分析和仿真结果表明,所提方案可以有效提高检测性能,当确保主用户受到足够保护的前提下,利用所提方案可以获得更高的次系统容量。      

Zone-Based Sharable Sensing Scheme in Decentralized Cognitive Radio Networks

In cognitive radio networks, secondary users conduct local sensing to use underutilized spectrum bands. However, every secondary user’s local sensing usually gives rise to much sensing overhead, and it requires the proper quiet period. Moreover, a secondary user should keep the quiet periods when the secondary users within its interference range perform spectrum sensing. As a result, both the local sensing overhead and quiet periods may reduce the channel utilization of a secondary user. To cope with these problems, in this paper, we propose a zone-based sharable sensing scheme, in which multiple secondary users alternate in local sensing and share the sensing results with others. We defined a certain area, so-called sensing zone, in which the sensing result by one secondary user is valid for others within the given area. The secondary user in charge of sensing in a sensing zone is elected in a distributed way. Each of the other secondary users can determine its quiet period schedules in accordance with the distance between itself and the elected sensing node. Numerical analyses and computer simulations show that our proposed sensing achieves a significantly reduced sensing overhead as well as an improved channel access opportunity.     

基于空间谱的频谱感知算法及性能分析

现有的基于特征值或谱密度的频谱感知算法,多分别使用近似高斯分布和Tracy-Widom分布来分别分析求解检验统计量在信号是否存在时的分布,未能给出统一的解析表达式。该文提出均匀线阵(ULA)条件下基于空间谱密度比的频谱感知算法,并且基于顺序统计量的最新研究成果,给出检验统计量统一的闭合表达式。该算法基于离散空间谱密度最大最小值的比建立检验统计量。仿真结果表明,对于8阵元的ULA,在采样点数为1000、检测概率为0.9时,所提算法比最大最小特征值(MME)比算法有约1.7 dB的性能优势,同时也有效验证了检验统计量理论分布的准确性。     

采用T2统计量和拟合优度的盲频谱感知算法

针对低信噪比和采样数少时频谱感知性能差的问题,首先利用样本检验矩阵的样本特征构造T2统计量,再结合似然比准则构造新的检验统计量,最后推导了频谱空闲时检验统计量的概率密度函数,提出基于贝塔分布的盲频谱感知算法。但此算法在采样数较少时,检测性能较差,因此结合Anderson Darling准则,提出基于贝塔分布的拟合优度检验算法。在高斯信道和瑞利衰落信道下对所提算法进行仿真,并与已有盲频谱感知算法仿真结果进行对比,所提算法具有更好的检测性能,且不需要主用户信息,不用进行特征分解,不受噪声方差影响。