Channel assignment scheme in clustered multichannel cognitive radio networks with outdated CSI over Rayleigh fading channels

This work investigates channel assignment for cooperative spectrum sensing in multichannel cognitive radio networks, where the heterogeneity of primary user (PU) activity and the effect of varying channel condition on the received signal‐to‐noise ratio during cluster formation are considered. With the objective to minimize interference to the PU while enhancing multiple spectrum utilization of the secondary user (SU), an overlapping cluster‐based assignment is formulated into a nonlinear integer optimization problem. To obtain an efficient solution, the nonlinear integer problem is transformed into a mixed integer linear problem, based on which, this paper proposes an exact solution and then two new heuristic algorithms for suboptimal solutions, respectively. Furthermore, a comparative study of four different cluster head selection schemes with respect to their performance in cooperative spectrum sensing, under cluster's heterogeneity in terms of SUs distribution relative to PU transmitter location is presented. Based on the study, a robust cluster head selection scheme is proposed. Simulation results show that good sensing performance and increased opportunistic spectrum utilization in multichannel cognitive radio networks are two sides of a coin that depend on the ratio of the SUs to the number of PU channels. How far away the PU is from the cluster center is also seen to be key in the optimal selection of cluster heads in cooperative spectrum sensing.     

Optimal energy-efficiency capacity and sensing tradeoff for hybrid spectrum sharing in CRN

This paper investigates the tradeoff between energy-efficiency capacity and spectrum sensing under hybrid spectrum sharing model,where the spectrum sharing method is based on sensing results of seconda...     

基于DOA估计的认知无线网络频谱感知

认知无线电技术中频谱感知性能的优劣直接影响认知通信系统的性能。针对该特点提出了认知无线电网络中基于波达方向（ DOA)估计的主用户频谱感知模型,即单主用户多次用户和多主用户多次用户的系统模型,选取基于特征分解的多重信号分类（ MUSIC)算法分析两种模型的感知性能,包括虚警概率、漏检概率、最小总错误概率、算法复杂度等,获得了闭值表达式,最后在两种模型下对算法进行了仿真。仿真结果表明：各参数主要影响虚警概率,而漏检概率几乎不受影响,验证了方法的有效性。     

A Novel Evolutionary-Based Cooperative Spectrum Sensing Mechanism for Cognitive Radio Networks

Cognitive radio (CR) is considered as a feasible intelligent technology for 4G wireless networks or self-organization networks and envisioned as a promising paradigm of exploiting intelligence for enhancing efficiency of underutilized spectrum bands. In CR, one of the main concerns is to reliably sense the presence of primary users, to attain protection against harmful interference caused by the potential spectrum access of secondary users (SUs). In this paper, evolutionary algorithms, namely, genetic algorithm (GA) and particle swarm optimization (PSO) are investigated. An imperialistic competitive algorithm (ICA) is proposed to minimize error detection at the common soft data fusion (SDF) center for structurally centralized cognitive radio network (CRN). By using these techniques, evolutionary operations are invoked to optimize the weighting coefficients applied on the sensing measurement components received from multiple cooperative SUs. The proposed method is compared with other evolutionary algorithms, as well as other conventional deterministic, such as maximal ratio combining- (MRC-), modified deflection coefficient- (MDC-), normal deflection coefficient- (NDC-) based SDF schemes and OR-rule HDF based. MATLAB simulations confirm the superiority of the ICA-based scheme over the PSO-, GA-based and other conventional schemes in terms of detection performance. In addition, the ICA-based scheme also shows promising convergence and time running performance as compared to other iterative-based schemes. This makes ICA an adequate solution to meet real-time requirements.     

传感能力约束下的认知无线网络的多波段光谱感应优化

This paper addresses the problem of joint optimization of subchannel selection and spectrum sensing time for multiband cognitive radio networks under the sensing capability constrains. In particular, we construct a multiband spectrum sensing framework, and derive the probabilities of detection and false alarm taking the different subchannel gain into account. Furthermore, we formulate the multiband sensing as a two-parameter optimization problem under the sensing capability constrains and guaranteeing the QoS of the secondary user. Moreover, we develop a semi-analytical optimization scheme to achieve the optimal solution.     

Secondary User Selection Scheme Using Adaptive Genetic Algorithms for Cooperative Spectrum Sensing Under Correlated Shadowing

Cooperative spectrum sensing has been shown to be an effective approach to improve the detection performance by exploiting the spatial diversity among multiple secondary users (or unlicensed users). However, due to correlated shadowing and cooperation overhead in practical cognitive radio networks, it is desired to select an appropriate set of secondary users which have little correlation with each other to participate in cooperation so as to achieve the effective tradeoff between detection performance and cooperation overhead. In this paper, we first study the hypothesis testing model and detection performance of cooperative spectrum sensing under the correlated log-normal shadowing scenario. Afterwards, based on whether the false-alarm and missed-detection probabilities are constrained, three optimization problems are formulated to find the optimal set of secondary users participating in cooperation, which take into account the tradeoff between detection performance and cooperation overhead. Then the solutions using adaptive genetic algorithms are presented for the optimization problems. Finally, simulation experiments demonstrate that our proposed schemes are very effective.     

Cross‐layer optimization for full‐duplex cognitive radio network with cooperative spectrum sensing

In this paper, the cross‐layer optimal design of multihop ad hoc network employing full‐duplex cognitive radios (CRs) is investigated. Firstly, the analytical expressions of cooperative spectrum sensing performance for multihop CR networks over composite fading channels are derived. Then, the opportunistic throughput and transmit power of CRs are presented based on the derivation of false alarm and missed detection probability. Finally, a multiobjective optimization model is proposed to maximize the opportunistic throughputs and minimize the transmitting power. Simulation results indicate that Pareto optimal solution of sensing duration, decision threshold, and transmit power can be achieved by cross‐layer multiobjective optimization, it can balance the conflicts between different objective functions and reap the acceptable outcomes for multihop CR network.     

Wavelet transform based novel edge detection algorithms for wideband spectrum sensing in CRNs

The primary task in cognitive radio is to dynamically explore the radio spectrum and reliably detect the co-existing licensed primary transmissions across a wide-band spectrum. This paper focuses on wavelet transform (WT) based wide-band sensing techniques, which identify the edges of the multiple frequency bands simultaneously. Novel edge detection algorithms are proposed based on continuous WT (CWT) and discrete WT (DWT) techniques, applied on wide-band power spectrum. In CWT based spectrum sensing, logarithmic scaling preceded by a thresholding is performed on the CWT coefficients to enhance the small modulus maxima values at the edges, resulting in better detection probability. Since the logarithmic scaling magnifies the spurious edges, the proposed algorithm increases the false alarm probability at high noise variance. To alleviate this problem, DWT based algorithms are proposed, where DWT performs simultaneous denoising and edge detection. To achieve good detection performance at poor SNR scenario, a moving average filtering strategy is adopted at different levels of DWT based algorithms and better performance is achieved even with lower scale value of DWT, thereby reducing the computation time. Comparative studies show that the proposed algorithms outperform the existing WT based edge detection algorithms in the dynamic and frequency selective channels as well.     

Cooperative multiband joint detection in cognitive radio networks using artificial immune system

In cognitive radio technology, spectrum sensing enables users to sense the environment and find spectrum holes. Cooperative sensing is a good idea for reliable detection of primary users in shadowed environments. In this study, spatial spectral joint detection with some constraints that keep the interference at the primary user below a suitable level is considered as the optimization problem for collaborative sensing. Convex optimization is able to obtain near-optimal solutions because of the non-convexity nature of the optimization problem. In this paper, we use artificial immune system (based on the clonal selection theory) to obtain the optimal solutions without any reformulations or mathematical costs. Numerical results show that our proposed algorithm outperforms the genetic algorithm used in the previous works.     

An artificial intelligence-based spectrum sensing methodology for LoRa and cognitive radio networks

The artificial intelligence-based spectrum sensing approach is extremely important in terms of effective bandwidth utilization for low power wide area networks (LPWANs) based on cognitive radio networks (CRNs). Most studies perform spectrum detection with CRNs using optimization or deep neural network methods. However, optimization-based spectrum detection approaches based on current LPWANs are scarce. For this purpose, in this study, a hybrid optimization methodology integrated with CRNs is proposed for LoRa, which is one of the most compatible LPWAN technologies in the Internet of Things (IoTs) recently. In the particle swarm optimization (PSO) part of this hybrid methodology, agent users are created so that secondary users (SUs) could use the licensed band of primary users (PUs) in cognitive radio. On the genetic algorithm side, LoRa error rates are minimized in order to further improve the performance of the proposed method. In this way, effective spectrum sensing is performed in the LoRa network. Various LoRa-CRN experiments have been carried out in the simulation environment, and the probability of detection and false alarm performances have been compared with both theoretical and proposed approaches in terms of quality estimation parameters. It is clear from the results that the proposed methods give successful results for the LoRa-CRNs.     

采用支持向量机的宽带频谱感知算法

认知无线电系统中,压缩感知理论已广泛运用于宽带频谱检测。但是,压缩感知中的重构问题造成频谱检测算法计算复杂度高,且在低信噪比下检测效果不佳。本文提出了采用支持向量机的宽带频谱感知算法,该算法利用支持向量机建立频谱检测分类器,代替信号的重构与检测过程。根据系统对实时性的要求,分别设计了多级二元分类器感知算法和单级多元分类器感知算法。前者适用于分级数有限且实时性要求不高的场景,后者可大幅降低系统的算法复杂度,降低感知时间,适用于实时检测系统。仿真结果表明,与基于重构的能量检测算法相比,本文提出的两种算法均可以有效改善系统对噪声的鲁棒性,提高在较小信噪比下的检测性能。      

多天线认知无线电网络多信道联合频谱感知策略

当前,以单天线认知用户组成的认知无线电网络同时协作检测多个信道的分配策略得到了广泛研究,而在认知用户可以灵活选取自身天线进行空间分集接收的条件下,当联合感知多信道时,多天线认知无线电网络如何获取最优的天线分配策略仍有待进一步研究。为解决这一问题,在限制各信道最大虚警概率的前提下,以最小化所有信道漏检概率之和为目标,建立了优化模型,并提出了基于分支定界的算法和基于贪婪思想的启发式算法。前者可以获得最优策略,但复杂度较高,后者以牺牲较小检测性能为代价,明显降低了复杂度,有效实现了检测性能与复杂度的平衡,并且在保护各个信道上主用户免受认知用户干扰层面,一定程度上兼顾了公平性。      

基于滤波器组的认知无线电频谱检测

认知无线电的首要任务是动态、可靠地检测无线电频谱环境。因此,每个认知无线电终端必须具有频谱检测的功能。文章在简述认知无线电频谱检测技术的基础上,提出了基于滤波器组的频谱检测方案,详细描述了多相滤波器组的设计方法和功率谱估计算法,并在加性高斯白噪声环境下进行了仿真。仿真结果表明,在恰当设置判决门限的情况下基于滤波器组的认知无线电频谱检测具有良好的性能。     

认知无线电网络中合作频谱感知机制的优化

为了提高检测效率和频谱利用率,该文对认知无线电中合作频谱感知的感知机制进行了优化。针对信道监测和信道搜索两个不同的感知场景,分别给出了检测周期、检测时间和搜索时间的选取和优化方法,并提出了新的信道搜索方式。研究分析和仿真结果可以看出,合作频谱感知和主动感知方式的引入可以提高检测性能和传输速率,对感知机制和策略的选取和优化有效地提高系统吞吐量,降低感知时延和开销,这样可以提高认知无线电网络的性能和灵敏性,在保证授权用户不被干扰的同时最大化空闲频谱的接入机会。     

认知无线电中基于信息简约的最大似然协同频谱感知算法

该文针对控制带宽受限条件下认知用户的协同频谱感知问题,提出了基于信息简约的最大似然协同频谱感知算法。算法使用信息简约模块来降低本地感知信息传递需要的网络开销,在基站处使用基于最大似然准则的检测来提高感知性能。理论分析和仿真表明,该算法能够以较少的网络开销有效提高频谱感知性能,特别是在认知用户接收信噪比相差较大的情况下,其性能优势更为明显。     

Maximizing the lifetime of energy‐constrained cooperative spectrum sensing sensor network

Adding the cognitive capability to wireless sensor networks allows the sensors to monitor the spectrum and identify the spectrum holes to operate in different frequencies according to the radio environment which result in better spectrum utilization. Spectrum sensing is a main component in any cognitive radio network. In this paper, we propose a new cooperative sensing scheme based on energy detection for cognitive sensor networks which is constrained by the energy limitation of the wireless sensor elements. The proposed scheme minimizes the sensing energy for individual sensor and carefully selects the suitable participant sensors in each cooperative sensing process. This results in maximizing the lifetime of energy‐constrained wireless sensor networks. The proposed scheme also takes into consideration the constraints on the detection accuracy. The simulation results show that the proposed scheme prolonged the lifetime of the cognitive network, makes efficient usage of available spectrum by secondary users, and satisfy the target detection performance.     

随机共振在认知无线电频谱感知中应用的最新研究进展

频谱感知是认知无线电网络的一项关键技术.低信噪比(SNR)环境下频谱检测的性能会大幅降低,而随机共振(SR)能有效提高信号信噪比,所以将其应用到频谱感知中,能增强认知用户对主用户(PU)的检测性能.首先介绍了随机共振在认知无线电频谱感知中应用的最新研究进展,包括随机共振在本地感知中(如能量检测、协方差矩阵频谱感知、循环平稳特征检测)及协作感知中的应用,然后指出了随机共振在认知无线电频谱感知中还有待解决的问题,并提出了下一步的研究方向.     

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...     

一种低速采样的协同宽带频谱感知方法

频谱感知是通信系统抗干扰和智能化的关键能力.针对认知无线电系统窄带频谱感知技术受制于数模转换器件的发展水平,难以解决认知无线电系统宽带、实时频谱感知的问题,提出一种多节点协作的认知无线电系统宽带频谱感知方法.该方法设计由多个认知节点对目标频段执行次奈奎斯特采样来降低采样速率,采用能量检测方式对采样矢量进行集中式融合判决,实现宽频段范围内干扰信号的谱定位和判断,降低各个感知节点的采样速率,支撑认知网络系统构建高实时、宽频带频谱感知的能力.计算机仿真试验结果表明,所提方法达到90％检测概率时压缩比要求为0.025,具有可靠性与有效性.