基于噪声不确定性的双门限协作频谱感知算法

传统的双门限协作频谱感知算法忽略了两门限之间认知用户的本地感知信息,而利用这部分信息可以进一步提高认知无线电系统的感知性能。为此,在等增益合并的基础上,提出一种顺序自适应分步合并算法。两门限之间的认知用户根据接收信噪比分步上传本地感知信息,融合中心自适应地调整参与协作的认知用户数,以减小系统的上传数据开销。推导在Rayleigh衰落信道条件下,认知用户上传数据的平均比特数以及全局检测概率的闭式表达式。理论分析和仿真结果表明,该算法能以较小的数据开销获得较优的协作感知性能。     

基于双门限和证据理论的合作频谱感知算法

从提高合作频谱感知结果的可靠性以及减小网络开销和融合中心计算负担的角度出发,提出了一种新的基于双门限和证据理论的合作频谱感知算法。该算法在进行本地感知的时候,认知用户通过双门限能量检测直接得到本地判决结果,将认知用户感知到的能量值在两个门限之间的未能进行本地判决的结果的信任度函数发送给融合中心,这样大大减少了融合中心利用证据理论进行软融合的计算量以及传输感知结果带来的网络开销。理论分析和仿真表明,提出算法的检测概率比其他检测算法的检测概率性能提升30%,其接收机操作性能曲线（ROC）性能比性能较好的D-S证据理论提升3%。     

基于可信度的双门限DMM协作频谱感知算法

针对已有的双门限特征值频谱感知算法存在忽略本地感知用户可靠性差异及融合判决方式开销大的缺点,提出了一种基于可信度的双门限DMM协作频谱感知算法(DT-CDMM),用于进一步提升协作感知性能。所提算法在最大最小特征值差(DMM)算法的基础上,建立了基于特征极限分布的双门限DMM算法作为本地感知,采用触发式的软、硬判决相结合的判决机制来减少系统开销,以本地感知性能与可信度加权的方式得到全局判决结果,并对硬判决进行自适应补偿。仿真结果表明, 较已有的双门限特征值算法以及双门限能量检测算法,DT-CDMM算法在噪声不确定的环境下提升了多用户协作检测的概率。     

基于多融合准则的双门限协作频谱感知算法

认知无线网络中,现有协作频谱感知技术未考虑根据感知用户的可靠性程度,对其判决结果采用不同的融合策略。针对上述问题,提出了一种新的基于多融合准则的双门限协作频谱感知算法。该算法以双门限协作检测为基础,根据感知用户判决结果的可靠程度不同,采用AND准则和OR准则相结合的判决方法,对参与感知的用户判决结果进行分类融合。理论分析和仿真结果表明,该算法与协作频谱感知算法相比,能够显著改善认知无线网络的频谱感知性能。     

Rayleigh信道下双门限协作频谱感知研究

频谱感知是认知无线电系统有效通信的前提,其感知速度和准确性是衡量频谱感知性能的重要指标.现有算法仅考虑理想AWGN信道下双门限协作频谱感知,应用中衰落信道下的频谱感知作深入研究,具有实际意义.为了提高频谱感知在实际应用中的可靠性,将双门限多节点协作方法用于Rayleigh衰落信道仿真检测.仿真实验表明,Rayleigh衰落信道下双门限多节点协作检测方法的可靠性优于高单门限协作检测方法,其检测结果对Rayleigh信道环境下频谱感知系统的设计具有重要参考价值.     

已知噪声不确定性的双门限协作频谱感知

频谱感知是认知无线电的关键技术之一。能量检测广泛应用于频谱感知中,然而噪声不确定性严重影响其检测性能。建立了噪声不确定性模型,提出了双门限协作频谱感知算法,并分析了融合中心接收到感知用户信息数量进行有效感知的概率。仿真结果表明,在一定条件下,所提算法能够达到较好的检测性能。     

基于可信度的加权协作频谱检测算法

协作频谱检测技术可以有效解决认知无线网络本地检测存在的信道衰落、隐藏终端等问题。论文研究了加权协作频谱检测技术,提出一种基于可信度的协作频谱检测算法。每个感知节点基于最大最小值特征值检测完成本地频谱检测,并与融合中心的全局检测结果进行比较估计各自感知节点的频谱检测可信度;融合中心利用切尾平均法计算参与频谱协作检测的可信度门限,并选择可信度大于门限的感知节点参与协作频谱检测。该算法有效降低了认知网络协作检测的复杂性,提高了频谱检测性能,在噪声波动环境下具有良好的鲁棒性。仿真结果表明,算法频谱检测性能要优于其它加权算法1~3 dB,节省系统开销43.75%左右。     

基于分步融合的双门限协作频谱感知改进算法

针对在噪声不确定通信环境下,传统双门限协作频谱感知算法在同时提高频谱检测性能与降低数据传输量中的不足,提出一种基于分步融合的双门限协作频谱感知改进算法。该算法首先对所有认知用户进行过滤,剔除深度衰落用户对检测性能的不利影响;然后根据噪声不确定度自适应设置双门限值,增强感知系统对噪声不确定环境的适应性;最后在融合中心采取分步融合的策略,合理折中高检测性能与低数据传输量间的矛盾。理论分析与仿真结果表明,与传统双门限频谱感知算法相比,该算法可完全避免感知失败,在保证低数据传输量的同时可有效提升检测性能,当噪声不确定度较大时,提升效果更明显。     

认知无线电网络中基于信任度的频谱感知技术

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。     

基于D—S理论的双门限频谱感知算法研究

针对传统双门限频谱感知算法不能充分利用2个门限之间感知用户信息的问题,提出了一种基于D-S理论的双门限频谱感知算法。当感知用户接收到的信号能量介于2个门限之间时,该算法不直接给出判决结果,而是进行本地判决,即采用D-S理论进行数据融合。仿真结果表明,与传统双门限频谱感知算法相比,基于D-S理论的双门限频谱感知算法的检测概率较高,提高了整体的频谱感知性能。     

协作感知下基于双门限能量检测的融合方案研究*

将双门限能量检测作为本地感知算法,为在感知性能和系统开销之间取得合理折中,给出了三种将决策融合与数据融合相结合的综合融合方案。研究和比较了这三种新型融合方案的感知性能,分析了各自的优缺点及其适用范围,讨论了门限值系数、协作用户数、协作可信度、平均信噪比等参数的变化对感知性能的影响。蒙特卡洛仿真的结果表明,通过选择合适的系统参数,综合融合方案能在保证较低系统开销的前提下,具有明显优于传统硬融合方案的感知性能。     

Soft decision cooperative spectrum sensing with noise uncertainty reduction

Cognitive radio (CR) is a potential strategy for opportunistic access of idle resources to solve the conflicts between spectrum scarcity and underutilization. Spectrum sensing (SS) constitutes the most critical part in CR systems since the CR needs to detect the presence of primary signals reliably and quickly. Energy detection (ED) based SS is considered as the most preferable SS technique due to its simplicity and applicability. However, it is influenced by the effect of noise uncertainty which highly degrades its sensing performance. Cooperative spectrum sensing (CSS) is also introduced to mitigate some sensing problems such as multipath fading, shadowing, and hidden node problems. In this paper, we propose an enhanced fusion center (FC) rule for soft decision CSS using ED, which highly alleviates the noise uncertainty effect and enhances the sensing performance of cognitive radio networks (CRNs). In the proposed fusion rule, to increase the probability of detection and decrease the probability of false alarm, two dynamic thresholds are utilized by the FC. These thresholds are toggled based on predicting the current activity of the primary user (PU), and their values are dynamically changed based on estimating the noise uncertainty factor of the collected energy measurements from the CRs. To effectively predict (estimate) the current PU activity (the noise uncertainty factor), simple successive averaging processes over the collected energy measurements (the estimated noise variances) are performed by the FC, respectively. Theoretical analysis is performed on the proposed fusion rule for soft decision CSS to evaluate its enhanced false alarm and detection probabilities using different data combining schemes. Performance evaluations are also investigated to confirm the theoretical claims and to prove the effectiveness of the proposed scheme over the conventional ED based soft decision CSS.     

CRN中基于自适应阈值和OR-决策规则的频谱感知策略

针对传统认知无线电网络(CRN)的频谱感知策略没有考虑噪声不确定性问题,提出一种基于噪声功率估计自适应阈值和OR-决策规则的频谱感知策略。首先,将各接收器数据构建成一个数据矩阵,并计算矩阵的协方差矩阵。然后,计算协方差矩阵的特征值,并根据特征值的均值来获得噪声的最大似然估计。接着,根据估计的噪声和能量信号的检验统计量来确定决策阈值。最后,各节点根据决策阈值作出局部决策并上传融合中心(FC),FC利用OR-决策规则作出最终决策。实验结果表明,该方案的决策阈值能够随噪声自适应调整,有效提高了检测率,对噪声不确定性具有很好的鲁棒性。     

Cooperative spectrum sensing based on stochastic resonance in cognitive radio networks

A new censoring cooperative spectrum sensing scheme based on stochastic resonance(SR)technique in cognitive radio(CR)network is proposed in this paper.The observations of the cooperative secondary users(SUs)whose statistics fall into the censoring interval are processed by SR system in the proposed scheme.The hard fusion and the soft fusion for the censoring cooperative spectrum sensing scheme are analyzed respectively.Theoretical analyses and simulation results show that the proposed censoring cooperative spectrum sensing scheme has the same detection performance as and lower computational complexity than the method that each cooperative SU performs spectrum sensing using SR-based energy detection,and its detection performance is superior to that of the conventional method that all the cooperative SUs perform spectrum sensing using energy detection in hard fusion.In soft fusion,the proposed censoring cooperative spectrum sensing based on equal gain combination can achieve the optimal sensing performance approximately.     

基于噪声估计的加权协同频谱感知技术

频谱感知是认知无线电研究的关键技术之一,能量检测法是典型的频谱感知方法。针对传统的能量检测法和协同频谱感知存在的局限性,提出一种基于估计噪声的加权协同频谱感知方法,不同认知用户分别通过估计被测频段相邻保护（过渡）频带的噪声特性,近似作为授权用户频带内的噪声特性,以此估计噪声作为信任度进行加权协同数据融合。仿真结果表明,该方法能有效提高频谱检测性能。     

Optimal MAC based cooperative spectrum sensing in cognitive radio networks

This paper considers a cognitive radio (CR) system in non-ideal fading wireless channels and pro-poses cooperative spectrum sensing schemes based on coherent multiple access channels (MAC),serving as an alternative way to improve the cooperative spectrum sensing performance and provide space diversity for spec-trum sensing.Sufficient statistics are transmitted using a common channel from the secondary users (SUs) to a fusion center (FC) where the global decision is obtained.The optimal scaling factors of the proposed schemes are obtained by maximizing the detection probability under a target false alarm probability and a transmit power constraint.Because the proposed optimal MAC scheme has high computational complexity,a sub-optimal solu-tion based on maximization of the deflection coefficient (DC) is also proposed.Simulation results show that the proposed algorithms can significantly improve the spectrum sensing performance and approach the detection baseline.