基于空间谱的频谱感知算法及性能分析

现有的基于特征值或谱密度的频谱感知算法,多分别使用近似高斯分布和Tracy-Widom分布来分别分析求解检验统计量在信号是否存在时的分布,未能给出统一的解析表达式。该文提出均匀线阵(ULA)条件下基于空间谱密度比的频谱感知算法,并且基于顺序统计量的最新研究成果,给出检验统计量统一的闭合表达式。该算法基于离散空间谱密度最大最小值的比建立检验统计量。仿真结果表明,对于8阵元的ULA,在采样点数为1000、检测概率为0.9时,所提算法比最大最小特征值(MME)比算法有约1.7 dB的性能优势,同时也有效验证了检验统计量理论分布的准确性。     

一种基于随机共振增强的协方差矩阵频谱感知算法

为了在避免对主用户系统产生有害干扰的同时 提高频谱利用效率,要求认知无线电系统的频谱感知算法能在极低的信噪比下快速检测出主用户信号。由于可以避免能量检测面临的噪声不确定性问题,基于协方差矩阵的检测算法是一种有效的盲频谱感知算法。为了进一步提高极低信噪比下的性能,本文提出了一种基于随机共振的协方差矩阵频谱感知算法。该算法通过在接收信号中加入优化的特定信号,利用随机共振原理,增大有无主用户信号下的检测统计量概率分布函数的分离度,提高频谱感知的性能。仿真结果表明,相对于现有的协方差矩阵频谱感知算法 ,在相同的虚警概率下,所提算法可以显著提高极低信噪比下的检测概率,同时大幅度缩减检测时间。      

利用功率谱最大最小平均比的频谱感知算法

由于载波频偏未知和噪声不确定性影响,信号功率谱的最大值和最小值不能根据单个频点来准确估计。该文提出利用功率谱最大最小平均比的频谱感知算法。利用基频附近一段功率谱的平均值作为功率谱最大值估计,利用功率谱中点频率附近一段的平均值作为功率谱最小值估计,将此二者之比作为检测统计量。推导了算法的虚警概率,得到了判决门限。加性高斯白噪声信道和瑞利衰落信道下的仿真结果表明:该算法性能优于基于功率谱分段对消频谱感知算法（PSC）和基于功率谱的平均比值算法（PSRA）,降低了载波频偏未知和噪声不确定性对频谱感知算法性能的影响。      

一种基于能量检测的协作式频谱感知算法

为提高认知无线电系统中频谱检测的可靠性,提出了一种基于能量检测的协作式频谱感知算法。利用授权用户的状态在相邻感知帧之间变化的概率小这一特性,通过将当前感知帧的能量值与相邻值相结合来判断授权用户状态,这样当授权用户使用授权频段时,所提算法能有效减小采样信号能量值骤减时发生误判的概率。另外给出了所提算法检测概率和虚警概率的闭式表达式。理论分析和仿真结果表明,所提算法比传统的协作式频谱检测算法检测性能好。     

基于功率谱分段对消频谱感知算法研究及性能分析

在实用的认知无线电系统中,频谱感知技术必须具备在噪声电平高动态变化和无线信道严重衰落电磁背景下,进行实时盲频谱感知的能力,这为经典的频谱感知算法带来巨大的挑战。该文提出的功率谱分段对消频谱感知算法,依据傅里叶变换的渐进正态性和相互独立性,计算出功率谱的统计特性,利用监测频带内部分谱线强度和与全部谱线强度和的比值作为检验统计量进行信号存在性的判断。该文推导了算法的虚警概率和不同信道模型下正确检测概率的数学表达式,并依据Neyman-Pearson准则得到判决门限的闭式表达式。理论分析和仿真结果均表明:功率谱分段对消频谱感知算法对噪声不确定度具有鲁棒性;固定信噪比,算法的频谱感知性能不受噪声电平改变的影响;应用于高斯白噪声和平坦慢衰落信道中,可在较宽的信噪比范围内获得较优越的频谱感知性能;算法计算复杂度低,可在微秒级时长内完成频谱感知。     

基于采样协方差矩阵的混合核SVM高效频谱感知

近年来随着盲检测算法的提出,越来越多的基于采样协方差矩阵的盲检测算法应用于频谱感知。针对其检测门限是近似值,检测性能会受到影响等问题,提出了基于采样协方差矩阵的混合核函数的支持向量机（support vector machine,SVM）高效频谱感知,通过感知信号采样协方差矩阵的最大最小特征值（maximum minimum eigenvalue,MME）和协方差绝对值（covariance absolute value,CAV）提取的统计量作为SVM的特征向量并训练其生成频谱感知的分类器,无需计算检测门限并且特征提取减少了样本集的大小。利用遗传算法（genetic algorithm,GA）优化混合核函数的SVM的参数。实验结果表明,该方法比MME算法和CAV算法的检测概率有所提高,并且比SVM减少了感知时间,具有良好的实用性。     

利用功率谱最大最小值的频谱感知算法

为了提高频谱感知性能,首先利用功率谱函数特性和瑞利熵概念,从理论上分析了基于功率谱的频谱感知算法原理,提出一种以功率谱的最大最小值之差作为检测统计量的频谱感知新算法;然后推导给出了检测门限和检测概率表达式;最后给出了仿真结果。理论分析和仿真结果表明,在AWGN信道和Rayleigh衰落信道中本文算法都具有良好的检测性能,性能优于已有的利用功率谱的频谱感知算法,该算法不需要主用户信息,不用进行复杂的特征值分解,当虚警概率确定时,检测门限能准确给出。      

基于循环谱能量的自适应频谱检测算法

根据信号循环平稳谱的特征,研究在低信噪比环境下的频谱检测问题,提出一种基于循环谱能量的自适应判决门限频谱检测算法。该算法融合能量检测与循环平稳特征检测的机理,以信号的循环谱能量为检测统计量,加权合并虚警率与检测率,准确估计循环谱特征值,构建了具有噪声自适应能力的频谱检测判决门限。仿真结果表明,该算法可以在低信噪比环境下有效地完成频谱检测,克服了噪声波动对频谱检测性能的影响,对不同调制主信号的感知具有稳健性。与最大—最小特征值算法和盲检测算法相比,该算法分别改善了信噪比4dB和8dB。     

基于循环谱对称性的频谱感知算法

针对现有基于循环谱的频谱感知算法的不足,利用改进SSCA算法计算接收信号的循环谱,减少算法的计算量;利用循环谱的对称性,选择非零循环频率处的循环谱抵抗干扰和噪声,结合对称性搜索策略进行频谱感知.分析并仿真了循环谱的参数对频谱感知算法的影响,仿真结果证明了所提出算法克服了传统算法的不足,提高了低信噪比下的正确检测性能.     

基于压缩感知的通信信号频谱检测方法

频谱感知是无线通信网络中提高频谱利用率的关键。针对现有通信信号频谱检测方法抗噪性低的问题,文中提出一种基于压缩感知的频谱检测方法。该方法首先利用压缩感知理论对通信信号的宽频带进行稀疏采样,其次采用改进的平滑范数重构算法对信号循环谱进行重构,提高了信号循环谱的重构性能,最后在循环谱域实现频谱检测。仿真实验结果表明,改进的平滑范数重构算法对信号的重构精度优于正交匹配追踪算法,压缩感知信号频谱检测算法的抗噪性优于传统能量检测算法。     

Cooperative blind spectrum sensing using autocorrelation matrix

To improve the spectrum sensing performance in cognitive radios, a scheme of cooperative blind spectrum sensing based on autocorrelation matrix is proposed. The test statistic is extracted from the autocorrelation matrix of the received signal samples and a bi-threshold hybrid decision scheme is designed for local spectrum sensing. The cognitive radio base station makes a credibility fusion based on the local soft decisions and then takes global fusion combining with the local hard decisions. The proposed method is blind in the sense since it requires no apriori knowledge of the signal and the noise power. Theoretical analysis and computer simulation results show that the proposed method can enhance the spectrum sensing capability.     

Effective autocorrelation-based spectrum sensing technique for cognitive radio network applications

Spectrum sensing based on detection techniques enables cognitive radio networks to detect vacant frequency bands. The spectrum sensing gives the opportunity to increase the radio spectrum channels re-utilization. However, the main challenge in spectrum sensing is the simplicity of the considered detection approach and the amount of prior information needed to make an accurate decision. This paper proposes a novel sensing technique based on the autocorrelation function. This novel approach is based on the speed of convergence to zero of all autocorrelation coefficients. This technique shows the highest probability of detection for the same probability of false alarm target at low signal-to-noise ratio (SNR) compared with many standard detection techniques. The proposed method has been implemented using GNU Radio software and SDR (software-defined radio) platforms. The experimental results show the effectiveness of the proposed method under real scenarios.     

Robust spectrum sensing algorithm based on free probability theory

In low signal‐to‐noise ratio (SNR) cases, the performance of spectrum sensing algorithms cannot meet the practical needs, which is a major problem faced by spectrum sensing technology in current cognitive radio field. Now, existing algorithms based on random matrix theory (RMT) have high sensing performance, but they require a large number of samples, which are very difficult to satisfy in practice. Free probability theory (FPT) is a main branch of RMT. It describes the asymptotic behavior of large random matrices and portrays a strong link between two matrices and their sum or product matrices. FPT can also be utilized to the digital communication system that can be modeled by random matrices and has been applied to spectrum sensing in simplified ideal channels, for example, additive white Gaussian noise channel. The most pivotal issue and difficulty of the FPT‐based methods is to set up and solve the asymptotic freeness equation corresponding to a specific communication model. In this paper, FPT‐based spectrum sensing schemes are proposed for some typical wireless communication systems, such as multiple‐input multiple‐output system, Rayleigh multipath fading system, and orthogonal frequency division multiplexing system. It is shown that the asymptotic freeness behavior of random matrices and the property of Wishart distribution can be used to assist spectrum sensing for these typical systems with low SNR and very limited samples. Simulation results demonstrate that compared with the existing RMT‐based spectrum detection methods, for example, the maximum and minimum eigenvalue detectors, the proposed FPT‐based schemes offer superior detection performance and are more robust to low SNR cases, especially for a small sample of observations. Copyright © 2015 John Wiley & Sons, Ltd.     

基于部分频谱小波边缘检测的压缩宽带频谱感知

压缩宽带频谱感知是一种采用压缩感知技术去感知相对较宽的频谱的方法.针对压缩宽带频谱感知中信号重构引起的计算资源消耗比较大以及能量检测在低信噪比下感知性能差的问题,提出了基于部分频谱小波边缘检测的压缩宽带频谱感知方法.首先使用基于最大熵准则的部分傅里叶矩阵获得部分频谱,然后根据部分频谱进行小波边缘检测,最后判别.仿真结果证明了该方案的有效性和优异性.     

Different sensing durations‐based cooperative spectrum sensing in cognitive radio systems

In order to provide more accurate detection of the primary user's activity in cognitive radio (CR) systems, cooperative spectrum sensing is proposed. The transmit diversity can also be employed by cooperative spectrum sensing to improve the performance of decision reporting. Hence, in the reporting channels between the cognitive users and the base station (BS), space time block code (STBC) scheme is considered in each cluster with time division multiple access (TMDA) method. In this paper, to improve the time efficiency in the case that one cluster makes sensing report, whereas the others do nothing but wait for their orders, we set each cluster with different sensing durations and the clusters will not stop the spectrum sensing until their results are reported. Furthermore, we also adopt the flexible sensing durations to decrease unnecessary energy consumption based on the clusters’ sensing sensitivities. Simulation results and analysis show the better detection performance and time efficiency of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

基于随机共振的双门限协作频谱感知算法

针对现有频谱感知算法在低信噪比（SNR）环境中性能检测不佳的问题以及传统随机共振（SR）检测弱信号的方法在实际应用中存在的局限性,通过设置最优门限,计算出最优的协作用户数量,提出了一种基于随机共振的双门限协作频谱感知算法,并对提出的算法进行了性能分析。DCSSR算法通过将位于双门限不确定区域的统计数据经过随机共振系统,进一步提高频谱感知算法在低信噪比下的检测性能。仿真结果表明,在不同信噪比和虚警概率下,DCSSR算法相较于传统单门限能量协作算法、双门限能量协作算法以及单门限随机共振协作算法,检测性能都得到了提升。在信噪比为-20 dB时,提出的DCSSR算法相较于传统单门限能量检测协作算法,检测概率提高了80%。     

A sparsity and compression ratio joint adjustment method for collaborative spectrum sensing

Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.     

一种采用压缩协方差的无线电宽带频谱感知方法

为有效降低宽带频谱感知的观测时间和计算复杂度,提出了一种基于压缩协方差的无线电宽带频谱感知方法。首先,通过循环稀疏规则测量不同标记的距离,运用多陪集采样组代替奈奎斯特模数转换器,形成基于多陪集采样库的欠奈奎斯特采样结构;其次,构建压缩协方差频谱感知模型,运用频谱决策模块对输入样本进行处理,完成频谱分析;最后,通过Matlab生成测试信号数据,对所提频谱感知算法进行建模与性能分析。实验结果表明,所提方法能够将检测误差控制在有效范围内,且与传统频谱检测方法相比,所提方法在不同信噪比环境下具有更高的频谱检测水平度。     

Exploiting temporal and spatial diversities for spectrum sensing and access in cognitive vehicular networks

In cognitive vehicular networks (CVNs), spectrum sensing and access are introduced as the promising technologies to fully exploit the underutilized licensed spectrum. Because the sensing ability of a single secondary vehicular user (SVU) is affected by high mobility, dynamic topology, and unreliable wireless environment, collaborative sensing is developed to increase the sensing accuracy and efficiency. Generally, the synchronization is required in the collaborative sensing in CVN. However, it is difficult to keep all SVUs synchronized with others for sensing under the high dynamic network topology, and the sensing overhead of the synchronous cooperative action may be significant. In this paper, we first propose an asynchronous cooperative sensing scheme in which each SVU provides an energy information (EI) that is tagged with location and time information. The sensing decision will be made on account of the EI. Considering the temporal and spatial diversities of each SVU, we assign different weights to each EI and formulate the probabilities of detection and false alarm as the optimization problems to find the optimal weight of each EI. Then, based on the asynchronous sensing, the specifications of the opportunistic spectrum access mechanism are elaborated in both centralized and decentralized CVNs for the sake of practical implementation. We analyze the system performance in terms of achievable throughput and transmission delay. Numerical results show that the proposed scheme is able to achieve substantially higher throughput and lower delay, as compared with existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.