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。     

认知无线电的合作频谱检测分析

认知用户需要持续快速地检测主用户的出现,对基于认知无线电的合作频谱检测机制进行了分析。在理想的两用户网络中,认知用户采用合作机制可以提高系统的平均侦测时间。在多认知用户网络中,参与合作的用户越多,单个认知用户对主用户的侦测概率越高。对于多用户网络,还考虑了整体的实现。     

Optimized Reliable Data Combining Cooperative Spectrum Sensing Method in Cognitive Radio Networks

Spectrum sensing is one of the most important tasks of each cognitive radio network. Cooperation among secondary users, by increasing the sensing accuracy can improve the network throughput, but also increases the energy consumption of cognitive radio network. In this paper, we propose the reliable data combining method for cooperative spectrum sensing, according to which the fusion center by using two threshold values, makes the final decision only if it is confident enough in validity of received local data. Otherwise, an additional sensing will be performed. Throughput maximization problems under interference constraints are formulated for both soft and hard fusion schemes and the optimal sensing time and threshold values are obtained. Simulations show that for all SNRs, higher absolute throughput and also higher throughput per energy consumption are accessible, rather than conventional cooperative sensing. Moreover, for a large range of SNRs the less energy is consumed.     

认知无线网络中动态双门限协作频谱感知性能的优化方案

频谱感知作为认知无线电的关键技术,得到广泛深入的研究。其中衡量协作频谱感知性能的主要参数为全局虚警概率和全局漏检概率,它们之和被定义为全局错误概率。本文研究基于双门限能量检测的协作频谱感知性能的优化方案,首先,固定双门限能量检测的检测门限值,对表决融合准则的投票门限进行优化,使得在该能量检测门限值条件下,协作频谱感知的全局错误概率最小;然后在表决融合准则的投票门限取最优值的前提下,对双门限能量检测的检测门限值进行了优化,在不同接收信噪比条件下,最优的检测门限值是动态的,所以要根据信噪比确定最优的检测门限值,使得协作频谱感知的全局错误概率在各信噪比条件下都达到最小值,从而提高了协作频谱感知的性能。仿真结果表明,表决融合准则的投票门限和双门限能量检测的检测门限值取得各自的最优值时,全局错误概率最低,检测性能最好。      

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.     

Fault-Tolerant Cooperative Spectrum Sensing Scheme for Cognitive Radio Networks

In order to solve the uncertainty resulting from shadowing effect and resist the attack from malicious cognitive radio (CR) users, we propose a fault-tolerant cooperative spectrum sensing scheme for CR networks, where an energy detection-based local spectrum sensing is performed at each CR user, a coefficient is used to weight each CR user’s sensing result, a linear weighted fusion process is performed at the fusion center (FC) to combine received sensing results. For a fault-tolerant cooperative spectrum sensing scheme, the most important issue is to distinguish whether the CR user is reliable or not. In this paper, a reputation-based cooperative mechanism is presented to alleviate the influence of the unreliable sensing results from CR users suffering shadowing and the false sensing data from malicious CR users on the detection result at the FC. In proposed fault-tolerant cooperative scheme, each cooperative CR user has a reputation degree which is initialized and adjusted by the FC and used to weight the sensing result from the corresponding user in the fusion process at the FC. And then, two reputation degree adjusting methods are presented to manage the reputation degree of each CR user. Simulation results show that the proposed scheme can not only weaken the harmful influence caused by malicious CR users, but also alleviate the corrupted detection problem resulting from destructive channel condition between the primary transmitter and the CR user. Moreover, the detection performance of the fault-tolerant cooperative scheme, which has a feasible computational complexity and needs no instantaneous SNRs, is close to that of the optimal scheme.     

Reputation-Based Hierarchically Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.     

认知无线电多时隙联合频谱感知方法及优化

为了降低认知无线电中次用户对主用户的干扰,提出了次用户通过多个时隙的能量感知联合检测主用户的频谱感知方法。每个传输帧被分成若干个时隙,次用户在每个时隙的开始进行能量感知,通过合并多个时隙的感知结果,提高次用户对主用户的检测性能。为了最大化次用户的频谱效率,将主用户状态建模为二维马尔科夫随机过程,并根据该过程优化单时隙频谱感知时间。仿真结果表明：相比Liang的“先听后传”频谱感知方法,提出方法仅牺牲14％的频谱效率,却使干扰概率降低了28％;随着信噪比增加,提出方法的最大频谱效率逐渐接近“先听后传”频谱感知方法。     

一种认知无线网络中的协作感知优化方法

认知无线电技术能够显著提高频谱资源的利用率,因此越来越受到学术界的关注。频谱感知是认知无线电的基础,一方面它要求能够快速找到可用频谱,另一方面当授权用户返回时又要求能够快速释放信道避免干扰。提出了一种认知无线网络中多用户协作感知分级方法,并通过数学推导给出最优的分级策略。仿真结果表明,采用所提出的协作频谱感知方法,能够提高认知无线网络的吞吐量。     

认知无线电中基于加权硬判决的协作频谱感知算法

Weighted one bit hard combination for cooperative spectrum sensing is proposed in this paper. Two thresholds are adopted to divide the possible energy value into three weighted regions.If the energy value falls into the corresponding region, it will be judged as “1”, no information or “0”. When the probability of false alarm is constrained to be constant, the objective is to maximize the probability of detection. The optimization problem is simplified by separating the weight of the middle region into several intervals. Simulation results show that the sensing performance of the proposed scheme is much better than that of the traditional one bit hard combination scheme and almost the same as that of the equal gain combination (EGC) scheme. Moreover, compared with the traditional one bit hard combination, fewer average sensing bits are required to transmit to the data fusion center with the proposed method.     

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。      

Soft Combination Schemes for Cooperative Spectrum Sensing in Cognitive Radio Networks

This paper investigates linear soft combination schemes for cooperative spectrum sensing in cognitive radio networks. We propose two weight-setting strategies under different basic optimality criteria to improve the overall sensing performance in the network. The corresponding optimal weights are derived, which are determined by the noise power levels and the received primary user signal energies of multiple cooperative secondary users in the network. However, to obtain the instantaneous measurement of these noise power levels and primary user signal energies with high accuracy is extremely challenging. It can even be infeasible in practical implementations under a low signal-to-noise ratio regime. We therefore propose reference data matrices to scavenge the indispensable information of primary user signal energies and noise power levels for setting the proposed combining weights adaptively by keeping records of the most recent spectrum observations. Analyses and simulation results demonstrate that the proposed linear soft combination schemes outperform the conventional maximal ratio combination and equal gain combination schemes and yield significant performance improvements in spectrum sensing.     

Optimization of Cluster-Based Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks with Soft Data Fusion

This paper investigates cluster-based cooperative spectrum sensing issues in two-layer hierarchical cognitive radio networks with soft data fusion. We first define a two-phase reporting protocol in the paper. In the first phase, secondary users forward their soft sensing information to cluster heads (CHs) over large-scale fading. In the second phase, all CHs transmit the aggregated soft energy information to the fusion center (FC) with different weights. Thus we derive the network false alarm (FA) and the detection probabilities as functions of the FC decision threshold, the clustering algorithm and different weights. Given a target on the detection probability, minimizing the FA probability is then formulated as a constraint optimization problem within two scenarios including additive white Gaussian noise environment and Rayleigh fading environment. A close-form upper bound of the FA probability is derived and a novel clustering scheme is also proposed for each scenario. Numerical results show that the proposed schemes achieve a satisfying performance.     

Joint Optimization of Cooperative Spectrum Sensing and Resource Allocation in Multi-channel Cognitive Radio Sensor Networks

Cooperative spectrum sensing (CSS) that utilizes multi-user diversity to mitigate channel instability and noise uncertainty is a promising technique in cognitive radio networks (CRNs). However, the spectrum-sensing parameters which affect the channel-access opportunities of secondary users (SUs) are conventionally regarded as static and treated independently from the resource-allocation strategies. In this paper, joint optimization of CSS, channel access and resource allocation is investigated in an overlay CRN in which each SU carries multi-channel spectrum sensing and transmits the detected energy to a fusion centre in the imperfect reporting channel. An access factor is introduced to describe the channel-access strategies in both cooperative and non-cooperative schemes. Based on the aggregate interference and the transmit power constraints, an optimization problem of multi-channel CSS is formulated to obtain the optimal transmit powers, allocation-access strategies, and sensing threshold of CR system for maximization of the opportunistic throughput. To solve the non-convex problems in both the single and multiple CR systems, the efficient iterative algorithms are developed by exploiting the hidden convexity of the optimization problems. Numerical results show that the performance of our approaches yields a significant enhancement compared with the equal channel-access and equal power-allocation strategy.     

认知无线网络中硬合并协同频谱感知的融合准则优化

频谱感知是认知无线电一项基础的任务。在认知无线网络中,多个次级用户可以协同工作,对主用户进行可靠感知。如何融合多个次级用户的感知信息是实施协同感知的关键。本文围绕集中式认知无线网络中的硬合并协同感知技术展开研究,讨论了常用的k-out-of-m融合准则。与OR准则（k=1）、Half-voting准则（k=m/2）和AND准则（k=m）等特例不同,本文考虑参数k任意取值的场景,从最小化贝叶斯代价的角度,推导出了最优k值的闭合表达式。仿真结果验证了该闭合公式的有效性,并显示最优k值随判决门限、频谱非空闲与空闲先验概率比、漏检与虚警影响因子比的增大而减小,且在信噪比场景中更具应用价值。      

协作频谱感知在认知网络中的应用

认知无线电能够灵敏感知周围环境的变化,通过频谱感知功能发现频谱空洞,使得认知无线电能够与周围通信环境相适应。在认知无线电网络中,认知用户（次用户）必须连续地监控频谱,检测主用户是否存在。如果主用户存在,次用户必须尽快地退出该频段,从而不影响主用户。在文中采用一种协作机制,即两用户进行协作频谱感知,从而提高主用户的检测率,减少了检测时间,并且得到捷变增益。我们用Matlab给出仿真结果。     

Cooperative Spectrum Sensing Techniques in Cognitive Radio

Cognitive radio has become an effective theory to solve the inefficiency of the spectrum usage. One of the main requirements of cognitive radio systems is the ability to reliably detect the spectrum hole. Previous works on the problem of detection for cognitive radio have suggested the necessity of user cooperation to enable the detection at the low signal-to-noise ratios experienced in practical situations. This paper introduces energy sensing and the cooperative sensing techniques: AND model, OR model, counting model, double threshold model, likelihood ratio model, linear cooperation model and DWCS model. It is proved that significant cooperative gain can be achieved by the proposed models in reducing interference and improving spectrum usage.     

Review of Robust Cooperative Spectrum Sensing Techniques for Cognitive Radio Networks

Cognitive radio networks sense spectrum occupancy and manage themselves to operate in unused bands without disturbing licensed users. The detection capability of a radio system can be enhanced if the sensing process is performed jointly by a group of nodes so that the effects of wireless fading and shadowing can be minimized. However, taking a collaborative approach poses new security threats to the system as nodes can report false sensing data to reach a wrong decision. This paper makes a review of secure cooperative spectrum sensing in cognitive radio networks. The main objective of these protocols is to provide an accurate resolution about the availability of some spectrum channels, ensuring the contribution from incapable users as well as malicious ones is discarded. Issues, advantages and disadvantages of such protocols are investigated and summarized.     

认知无线电MAC层多用户协同感知机制优化

为了有效快速的寻找到频谱空洞并不对主用户造成干扰,频谱感知是认知无线电系统中具有重要意义的一环.而多用户协作感知可以进一步提高系统性能.本文关注认知无线电MAC层两种频谱感知机制主动感知和被动感知机制.在多用户协作的场景下,分析主用户活跃度对感知性能造成的影响.并提出了一种低复杂度的折衷接入延时和功率消耗的优化策略.通过理论分析和仿真结果可以看出,主动和被动感知机制下多用户协作感知可以有效的减小信道搜索时间,优化算法帮助动态地选取感知机制减小系统开销.     

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.