基于节点选择的能量高效的协作频谱感知算法

针对认知无线电中协作频谱感知能耗高的问题,提出一种基于节点选择的能量高效的协作频谱感知算法。该算法在保证感知性能的同时优化感知节点数量,从而达到高效节能的目的,其核心是采用能量高效的节点选择算法。该算法综合考虑了感知性能和能耗,仿真结果表明该算法在保证感知性能的同时能有效降低协作频谱感知的系统能耗。     

认知无线电中一种感知节点集自适应选择算法

本文主要考虑认知网络中感知节点集的选择问题。联合谱感知技术虽然可以极大地提高认知系统的感知性能,但是随着参与感知的认知节点数目的增加,对系统资源的占用也会越来越多,使系统的传输效率下降。本文首先给出了认知网络中最优感知节点集的概念,接着分析了最优感知节点集的节点数目和平均接收信噪比所必须满足的条件,最后通过推导得到了在固定虚警概率条件下最优感知节点集的检测概率与它的节点数目和平均接收信噪比之间的关系表达式,并在此基础上提出了一种最优感知节点集的自适应选择算法。该算法不但能在认知网络中寻找最优感知节点集,同时还可以适应认知网络的动态拓扑变化。仿真结果证明了该算法的有效性。      

认知无线电中协作压缩频谱感知算法

针对宽带频谱认知无线电环境中,传统能量检测法在信噪比较低时,容易出现误检而使系统的检测性能下降的问题,文中提出了一种新型高性能的协作频谱感知算法,它是基于压缩理论的多节点频谱感知方法,各节点之间采用基于双判决门限的协作方式。仿真结果显示,双门限协作压缩频谱感知算法在低信噪比的情况下,检测性能明显优于传统能量检测法。     

认知无线电中协作频谱感知技术研究

由于无线通信用户的增多和采用的固定频谱分配模式,使频谱资源越来越紧缺,认知无线电的出现极大地提高了频谱的利用率。该文对基于能量检测的单节点检测和协作检测技术进行了分析和讨论,重点分析了协作检测中的数据融合算法,并对几种融合算法和能量检测算法进行了性能的仿真分析。通过分析及仿真结果可以看出,协作检测与单节点检测相比,有效地提高了检测概率。     

认知无线电网络中面向多通道的协作感知优化模型及启发式求解方法

针对认知无线电网络中多个认知节点对多个通道进行协作感知的系统建立优化模型。该模型在各通道错误接入概率小于给定阈值的约束下,以最大化系统吞吐量为目标,对包括感知时间和各SU对各通道检测结果的权重系数在内的参数进行优化,是一个约束非线性规划模型。为求解该模型,提出了一种启发式的顺序参数优化方法(SPO,sequential parameters optimization method)。该方法首先推导出优化问题的下界并转而对该下界进行优化,随后通过构建了一系列仅含有权重系数的子优化问题并采用拉格朗日方法求解出优化的权重系数,待权重系数确定后,最后采用黄金分割搜索法确定优化的感知时间。仿真实验的结果表明了SPO的有效性并验证了提出的模型在提高系统吞吐量方面的优势。     

认知无线电网络的一种协作频谱感知方案

认知无线电技术能够让非授权用户利用已经分配给授权用户的频段.为了不对首要用户的工作造成干扰,认知用户需要对频谱进行不间断的监测来判断首要用户是否存在.因此,频谱的感知是认知无线电技术的关键.协作频谱感知能够充分的利用网络资源,提高网络中的认知用户的检测概率.文中笔者简单地介绍了一种协作频谱感知的方案.仿真结果表明,通过该方法能够提高网络中认知用户的检测概率,提高网络的检测灵敏度.     

基于改进群搜索优化算法的认知无线电协作频谱感知

提出了一种基于改进群搜索优化的认知无线电协作频谱感知方法。用改进的群搜索优化算法求解线性协作感知模型中的权重向量,并将本文方法与基于单节点感知、选择合并、等增益合并和MDC的频谱感知方法进行了比较,仿真结果表明基于改进群搜索优化算法的协作频谱感知较传统的群搜索优化算法具有更好的收敛性,可获得更高的检测概率,且检测性能随感知用户数的增加而提高、随噪声环境的恶化而降低。仿真结果验证了本文方法的优越性。     

认知无线电协作频谱感知技术综述

频谱感知是实现认知无线电功能的前提条件,也是认知无线电领域的一个研究热点.近年来人们提出了很多种频谱感知方法,尤其协作感知技术日益受到关注.综述了协作频谱感知技术的最新研究进展,先描述典型的认知无线电协作频谱感知模型,然后讨论了协作感知中信息融合及性能分析等关键问题,最后指出了协作感知的研究挑战和发展趋势.     

认知无线电网络基于D-OFDM的中继选择算法

认知无线电技术能有效提高频谱利用率,而协作中继是解决信号在无线媒介中消失问题的关键技术。文章基于认知无线电网络,通过创建协作表,提出了一种利用协作中继提高频谱利用率和数据传输速率的中继选择算法。其中的协作表包括信道增益、信道可用率和认知节点的不同频谱。该算法通过选择合适的中继节点以最大化源节点的传输速率。仿真结果表明了该中继选择算法的有效性。     

基于改进人工鱼群算法的协作频谱感知用户选择策略

选择次用户是协作频谱感知的一个关键环节。针对次用户选择问题的特点,在基本人工鱼群算法AFSA基础上,通过取消鱼群密度、取消人工鱼的随机游动、改变公告板记录规则、保留每次迭代最优位置、增加最优人工鱼的觅食次数并缩小视野提出改进的人工鱼群算法次用户选择策略。仿真结果表明,对于最优次用户组选择问题,本文提出的修正AFSA在寻优成功率和运行时间等方面优于传统的AFSA。     

Optimization algorithm of periodical cooperative spectrum sensing in cognitive radio

To decrease the interference to the primary user (PU) and improve the detected performance of cognitive radio (CR), a single‐band sensing scheme wherein the CR periodically senses the PU by cooperative spectrum sensing is proposed in this paper. In this scheme, CR first senses and then transmits during each period, and after the presence of the PU is detected, CR has to vacate to search another idle channel. The joint optimization algorithm based on the double optimization is proposed to optimize the periodical cooperative spectrum sensing scheme. The maximal throughput and minimal search time can be respectively obtained through the joint optimization of the local sensing time and the number of the cooperative CRs. We also extend this scheme to the periodical wideband cooperative spectrum sensing, and the joint optimization algorithm of the numbers of the sensing time slots and cooperative CRs is also proposed to obtain the maximal throughput of CR. The simulation shows that the proposed algorithm has lower computational quantity, and compared with the previous algorithms, when SNR = 5 dB, the throughput and search time of the proposed algorithm can respectively improve 0.3 kB and decrease 0.4 s. The simulation also indicates that the wideband cooperative spectrum sensing can achieve higher throughput than the single‐band cooperative spectrum sensing. Copyright © 2012 John Wiley & Sons, Ltd.     

Parameters optimization for cooperative sensing in multi‐channel cognitive radio networks

In this paper, we propose a relatively complete and robust optimization model under the scenario where multisecondary users cooperatively sense multi‐channels. The objective of this model is to maximize the system throughput, meanwhile aims to jointly optimize the parameters including the sensing time and the weight coefficients of the sampling results. Because this model is a nonlinear optimization model, we instead adopt a heuristic sequential parameters optimization method (SPO) to solve the model. The method begins with deriving the lower bound of the objective function of the optimization model. Then, it maximizes this lower bound by optimizing the weight coefficients through solving a series of suboptimal problems using Lagrange method. Given that the weight coefficients are found, it finally transforms the problem into another monotonic programming problem and exploits a fast‐convergent polyblock algorithm to find an optimized sensing time parameter. We finally conduct extensive experiments by simulations. The results demonstrate that, in terms of the throughput gained by the system, SPO can deliver a solution that is up to 99.3% of the optimal on average, which indicates that SPO can solve the proposed optimization model effectively. In addition, we also show the performance advantage of the proposed model on improving the system throughput by comparing with other state‐of‐the‐art optimization models. Wireless Communications and Mobile Computing. Copyright © 2012 John Wiley & Sons, Ltd.     

认知自组网分布式频谱感知最优协作算法

认知无线电技术使得自组织网络节点能够充分利用空闲频谱资源,提高了传输性能。通过协作频谱感知,可有效解决由于无线信道存在阴影、噪声和衰落等情况导致的单节点感知准确性偏低。为了解决梯度算法随着协作节点数量增大后计算复杂度变高,文中提出部分梯度算法ψ-GBCS,该模型通过基于SNR的动态阈值保证了感知准确性,同时通过最佳协作节点数提高了感知效率。仿真结果表明,该模型下,综合评估系统效率和性能的J函数值提高37%,能耗降低50%,有效保证大规模认知自组网频谱感知的鲁棒性,降低了对主用户的干扰及设备功耗。     

Cluster‐based coordination scheme for cooperative cognitive radio networks

In this paper, a cluster‐based two‐phase coordination scheme for cooperative cognitive radio networks is proposed considering both spectrum efficiency and network fairness. Specifically, candidate secondary users (SUs) are first selected by a partner selection algorithm to enter the two‐phase cooperation with primary users (PUs). In phase I, the selected SUs cooperate with PUs to acquire a fraction of time slot as a reward. In phase II, all SUs including the unselected ones share the available spectrum resources in local clusters; each of which is managed by a cluster head who participated in the cooperation in phase I. To improve the total network utility of both PUs and SUs, the maximum weighted bipartite matching is adopted in partner selection. To further improve the network performance and communication reliability, network coding is exploited during the spectrum sharing within the cluster. Simulation results demonstrate that, with the proposed cluster‐based coordination scheme, not only the PUs' transmission performance is improved, but also SUs achieve spectrum access opportunities. Copyright © 2015 John Wiley & Sons, Ltd.     

认知网络中协作感知吞吐量的优化

为了提高集中式认知网络的吞吐量,提出了基于信任度的吞吐量优化算法.该算法在主用户充分保护的前提下,以认知用户的吞吐量为目标函数,融合中心采用双门限值对本地感知结果进行融合.从理论上证明了吞吐量是全局漏检概率的增函数,当全局漏检概率等于门限值时,吞吐量达到最大值.并利用牛顿迭代法求出单节点概率,然后采用遍历法可得到认知用户吞吐量最大值.仿真结果表明,当信噪比为-14 dB时认知用户融合优化算法相对"AND准则"OR准则"以及"HALF准则"归一化吞吐量分别提高了0.62、0.3和0.09.     

An enhanced two‐phase SVM algorithm for cooperative spectrum sensing in cognitive radio networks

Cognitive radio (CR) networks have emerged recently to address the problem of spectrum scarcity. As reliable spectrum sensing (SS) is vital in low signal‐to‐noise ratio (SNR) for CR networks, we propose a novel method of enhancing support vector machines (SVM) classifier named as 2‐Phase SVM for the task of SS in a cooperative sensing structure. In this study, the vectors containing energy levels of primary users (PU) are considered as feature vectors and are fed into the classifier during training and test phase. First, the classifier is trained; afterward, the test feature vectors are labeled as channel available class or channel unavailable class in an online fashion by using 2‐Phase SVM, which is applied during two phases compared with the conventional SVM algorithm. The performance of suggested cooperative SS method is evaluated by receiver operating characteristic (ROC) curve and the functionality of our proposed algorithm is qualified in terms of misclassification error rate in addition to misclassification risk. The results reveal that 2‐Phase SVM outperforms previous methods since it not only increases the classification accuracy and reduces the misclassification risk but also enhances the detection probability.     

Improved cooperative spectrum sensing based on the reputation in cognitive radio networks

Cognitive radio (CR) is a promising technology to improve the utilisation of wireless spectrum resources. Spectrum sensing is the core functionality in CR networks (CRN). When there exist malicious users (MUs) in CRN and MUs start to attack the network after accumulating reputation to some extent, the performance is deteriorated. In this paper, a scheme is proposed by employing Orthogonalized Gnanadesikan–Kettenring (OGK) to mitigate the effect of MUs without the assistance of trusted nodes, and it can improve the robustness of CRN. Simulations verify the effectiveness of the proposed scheme.