用于认知无线电协作频谱感知的混合蛙跳算法群体初始化技术

提出了一种用于认知无线电线性加权协作频谱感知的改进混合蛙跳算法(shuffled frog leaping algorithm, SFLA) 的群体初始化技术, 提出在SFLA初始群体中包含基于修正偏差因子所得的解, 从而改进算法初期性能. 仿真结果表明相比于传统群体初始化技术, 本文所提出的群体初始化技术能够以更快的速率得到期望解, 从而节约计算时间, 更有利于实时应用 关键词： 认知无线电 频谱感知 混合蛙跳算法 群体初始化     

认知无线电网络中利用FEC和DE的多节点频谱感知算法

针对认知无线电网络(CRN)中空闲频谱感知困难的问题,本文提出了基于前向纠错和差分进化算法的多节点频谱感知算法。首先,利用基于差分进化算法的协同检测完成信号感知;然后,研究了信道噪声对频谱感知性能的影响;最后,分析了前向纠错技术在信道存在噪声时对频谱感知性能的影响。仿真实验将纠错和无纠错控制信道的不同信噪比作为依据,采用三种不同的检测方法评估了本文算法。仿真实验结果表明,在存在噪声的认知无线电网络中,本文算法提高了系统的性能和检测概率,且协同感知算法的性能随着节点数目的增加而提高,该算法适合应用于实时性要求较高的应用程序。     

基于信任度的双门限协作频谱感知算法

提出了一种兼顾认知无线电系统可靠性和低负载的基于信任度的双门限协作频谱检测算法.系统首先使满足双门限要求的认知节点参与协作感知,当满足双门限要求的认知节点数目不足时,增加满足信任度参数要求的认知节点参与协作感知.融合中心存储了认知节点的检测记录,并以此为局部检测结果设置融合权重.理论分析和仿真结果表明,该算法所需传输的感知参数减少了,占用的信道带宽降低.同时,由于不可靠用户的减少,算法的检测性能进一步提高了.此外,算法通过调整参数nt使系统适应于不同类型的无线业务,具有一定的灵活性.     

基于量子遗传算法的认知无线电频谱分配

提出了基于量子遗传算法的认知无线电频谱分配算法,通过仿真比较了本文算法与颜色敏感图论着色频谱分配算法的性能.结果表明基于量子遗传算法的频谱分配算法性能明显优于颜色敏感图论着色算法,它能更好地实现网络效益最大化;当用户数和频带数较少时,量子遗传算法在进化代数很少时就能找到理想最优解,而颜色敏感图论着色算法所得到的解与理想最优解偏差较大. 关键词： 认知无线电 频谱分配 量子遗传算法 图论着色     

基于改进的稀疏度自适应匹配追踪算法的宽带压缩频谱感知

针对在实际宽带压缩频谱感知中难以预先获知宽带频谱稀疏度的问题,提出一种改进的稀疏度自适应匹配追踪(modified sparsity adaptive matching pursuit, MSAMP)算法,该算法在支撑集选择过程中对稀疏度进行了预估计。结合序贯压缩检测技术,给出了一种基于该算法的多认知用户合作场景下的宽带压缩频谱感知方法,理论分析和实验仿真结果表明,该方法可在频谱稀疏度先验知识缺少的情况下,有效提高宽带频谱感知性能。     

基于改进的稀疏度自适应匹配追踪算法的宽带压缩频谱感知（英文）

针对在实际宽带压缩频谱感知中难以预先获知宽带频谱稀疏度的问题,提出一种改进的稀疏度自适应匹配追踪(modified sparsity adaptive matching pursuit,MSAMP)算法,该算法在支撑集选择过程中对稀疏度进行了预估计。结合序贯压缩检测技术,给出了一种基于该算法的多认知用户合作场景下的宽带压缩频谱感知方法,理论分析和实验仿真结果表明,该方法可在频谱稀疏度先验知识缺少的情况下,有效提高宽带频谱感知性能。     

基于移动Agent和混合蛙跳算法的网格计算资源管理

为了克服传统网格系统采用Globus集中式模型或P2P网络分布式模型具有的无法兼顾全局控制性和扩展性的问题,提出了一种基于移动Agent和混合蛙跳算法的网格资源管理模型;首先,引入移动Agent将资源管理模型划分为应用管理模块、资源管理模块和资源表示模块,然后通过在经典的混合蛙跳算法加入差分扰动设计了一种改进的混合蛙跳算法,最后,在资源管理模块中的资源分配Agent中运行改进的混合蛙跳算法实现用户任务到资源的调度;采用GridSim工具进行仿真,实验结果表明,文中的资源管理模型具有较强的全局控制能力和扩展性能,资源利用率高达95.65%,较其它方法具有较小的时间跨度和较高的资源利用率,是一种适用于网格环境的有效资源管理模型。     

一种基于授权信道特性的认知无线电频谱检测算法

针对认知无线电系统中频谱检测的频率直接影响系统容量以及与授权用户产生冲突的概率问题,分析了授权用户频谱使用的特性, 对授权用户行为进行统计建模, 提出一种自适应频谱检测算法. 引入控制因子, 在保证认知无线电系统稳定性的约束下, 自适应调整频谱感知的频率从而提高频谱利用率并减小系统冲突概率和检测开销, 进而降低了系统的能量消耗. 仿真结果表明, 该算法在保证不对授权用户产生干扰和一定的系统稳定性条件下, 有效地提高了系统的容量,并且具有良好的实用性和灵活性. 关键词： 认知无线电 自适应频谱检测 绿色通信 最大似然     

基于二进制粒子群算法的认知无线电决策引擎

提出了基于粒子群算法的认知无线电决策引擎,并提出了一种种群自适应粒子群算法,利用粒子群算法调整优化无线电参数,运用多载波系统对算法性能进行了仿真分析.实验结果表明基于二进制粒子群算法的认知决策引擎在收敛速度、收敛精度和算法稳定性上都要明显优于经典遗传算法,基于种群自适应粒子群算法的决策引擎则能进一步提高算法初期性能,满足认知无线电实时性要求. 关键词： 认知无线电 粒子群算法 遗传算法 认知决策引擎     

标志位频谱感知方法研究

文章提出了一种新的标志位频谱感知方法,主要由数据采集和频谱感知两部分组成.前者主要基于标准的压缩感知技术,研究了一种标志位数据采集方法,仅保留测量数据的标志位信息,从而减少了测量数据的存储量.后者基于一致恢复原理和共轭梯度算法来构造频谱感知算法.仿真结果表明,标志位频谱感知方法可以在降低数据采集量及存储量的同时完美获取原始信号的频谱信息.该方法可以应用于无线通信、电子对抗、智能吸波结构以及感知无线电的前端频谱测量设计阶段中. 关键词： 频谱感知 标志位压缩感知 智能吸波结构     

Cooperative spectrum sensing based on a modified shuffled frog leaping algorithm in 5G network

The explosion of different types of wireless communications is leading to an impending spectrum famine. As a result, spectrum sensing has gained increasing interest from governments, industry and regulators. In this paper, a novel approach for cooperative spectrum sensing is proposed based on a modified shuffled frog leaping algorithm (SFLA). This approach is to fuse the perceived results of multiple nodes, and to improve the detection reliability. Simulations are used to compare the performance of the modified SFLA to the conventional one. The performance of the proposed cooperative spectrum sensing method based on the modified SFLA and that of the cooperative spectrum sensing method using modified deflection coefficient (MDC) are also compared. Results show that the proposed SFLA outperforms the traditional SFLA, and the proposed cooperative spectrum sensing method based on the modified SFLA gives higher miss detection probability than the MDC-based method, which validates the effectiveness of the modified SFLA-based cooperative sensing method.     

Quadratic covariance matrix-based cooperative spectrum sensing method by using an evolutionary algorithm

Cognitive radio (CR) is a practical technology to solve the current low utilization of spectrum resources, and spectrum sensing is the most critical technique in a CR network. In this paper, a genetic simulated annealing algorithm based on quadratic covariance matrix and information geometry is proposed for cooperative spectrum sensing (CSS) to enhance the performance in the low signal-noise ratio (SNR). Firstly, the quadratic covariance matrix of cooperative secondary users (SUs) is used as the characteristic matrix to perform feature extraction. Secondly, based on the information geometry, the characteristic matrix is mapped on the statistical manifold to avoid information loss. Furthermore, the genetic simulated annealing algorithm is used to obtain a classifier on the statistical manifold, and the mutation process is improved by a new mutation operator to accelerate the convergence speed of the whole algorithm. Finally, the classifier is employed to implement spectrum sensing. In the simulation analysis, the proposed method has better spectrum sensing performance than the popular various methods under low SNR and faster convergence speed.     

5G-based wideband cognitive radio system design with cooperative spectrum sensing

Since the 5G bandwidth is very large, there are a large number of non-continuous idle spectrum in 5G communication. In this paper, we have designed transmitter and receiver of a 5G-based wideband cognitive radio (CR) system with cooperative spectrum sensing, in order to improve transmission performance and avoid interference signals. Each CR user marks the spectrum availability for getting the sub-basis function through doing Inverse Fast Fourier Transform (IFFT) with the product of spectrum marker vector and random phase vector. The cooperative spectrum sensing can be realized by cascading the sub-basis functions of all the users. Multiple access of the CR system is also proposed to access much non-continuous idle spectrum. The simulation results have shown that the proposed CR system can avoid the interference effectively and outperform the spread spectrum system obviously.     

基于随机矩阵最大特征值分布的频谱感知算法

现有频谱感知算法在低信噪比时检测性能较低且受虚警概率影响大,针对此问题,提出了一种基于wishart矩阵样本协方差矩阵最大特征值的分布特性的频谱感知算法。该算法利用最大特征值与几何平均特征值的比值,不需要主用户的先验知识,不敏感于噪声,对相关信号和独立同分布信号均具有较高的检测性能。仿真结果表明,所提算法受虚警概率的影响较小,检测性能高,并且在采样点数、协作用户数、信噪比及虚警概率较小的情况下,也能获得较好的检测性能。     

Cooperative wideband spectrum sensing under reporting channel errors: Analysis and algorithm

This paper focuses on the performance analysis and the algorithm development for cooperative wideband spectrum sensing (CWSS) under imperfect reporting channels for cognitive radio (CR). The centralized approach with multiple distributed cooperating secondary users (CSUs) and a fusion center (FC) is considered for CWSS. Nakagami fading is used to model the channel between PUs and the CSUs. The CSUs equipped with multiple diversity antennas are considered to take advantage of both space and antenna diversity. In this work, the reporting channel model under erroneous reporting is proposed. The repetition code-based CWSS (RC-CWSS) algorithm is proposed for performance improvement. First, the modified, improved theoretical analysis of an existing algorithm called partial band Nyquist sampling-based CWSS (PBNS-CWSS) under no reporting errors is given. The theoretical analysis under an imperfect reporting channel is then carried out for the same algorithm. Then, the complete theoretical analysis for the proposed RC-CWSS algorithm is carried out. The theoretical analyses are verified using Monte-Carlo simulations. The analysis shows that the performance of CWSS is greatly affected by the reporting channel errors. Also, the RC-CWSS outperforms recently proposed state-of-the-art algorithms. Finally, the effects of different parameters on the performance of the proposed algorithm are also studied.     

Joint optimal fair cooperative spectrum sensing and transmission in cognitive radio

Since the sensing power consumption of cooperative spectrum sensing (CSS) will decrease the throughput of secondary users (SU) in cognitive radio (CR), a joint optimal model of fair CSS and transmission is proposed in this paper, which can compensate the sensing overhead of cooperative SUs. The model uses the periodic listen-before-transmission method, where each SU is assigned a portion of channel bandwidth, when the primary user (PU) is estimated to be free by the coordinator. Then, a joint optimization problem of local sensing time, number of cooperative SUs, transmission bandwidth and power is formulated, which can compensate the sensing overhead of cooperative SUs appropriately through choosing suitable compensating parameter. The proposed optimization problem can be solved by the Polyblock algorithm. Simulation results show that compared with the traditional model, the total system throughput of the fairness cooperation model decreases slightly, but the total throughput of the cooperative SUs improves obviously.     

TCVQ-SVM algorithm for narrowband spectrum sensing

Spectrum sensing is viewed as the basic and crucial technology for cognitive radio. To improve the accuracy of spectrum sensing in low signal to noise ratio (SNR), this paper presents an efficient TCVQ-SVM method based on machine learning for narrowband spectrum sensing. Firstly, trace of covariance matrix and variance of quadratic covariance matrix (TCVQ) is extracted as feature vectors and combined as training samples of spectrum sensing. Then, the classification model can be achieved by training samples based on support vector machine (SVM), which can avoid setting threshold and adjusting classification hyperplane by its self-learning ability. Lastly, the result of spectrum sensing can be obtained. By utilizing trace and variance as input features of SVM, the algorithm can make full use of the eigenvalue difference and structure characteristic of the received signal, and at the same time, achieve good performance in low SNR. Theoretical analysis reveals that the proposed method has low computational complexity. Simulation results and experiments on the hardware platform illustrate that the proposed algorithm is effective and robust.