BLSBA‐ED‐SSO: Bivariate Lévy‐stable bat algorithm‐based energy detection for spectrum sensing optimization in cognitive radio networks (CRNs)

The vacant licensed spectrum can be employed by the secondary users in cognitive radio networks. Nevertheless, this process of identification is frequently agreed with shadowing, multipath fading and receiver uncertainty problems. The primary contributions of the spectrum sensing techniques adopted in this work mainly concentrate on energy detection for spectrum sensing as well as to recognize the optimal spectral estimation according to the multitaper spectral estimation. The algorithm proposed in this work is based on bivariate Lévy‐stable bat algorithm (BLSBA), energy detector (ED), and with the help of K out of M fusion rule; it is analysed for the single user as well as cooperative multiple users. In the BLSBA algorithm, a modified search equation with more helpful information from the search experiences is brought‐in to create an optimal energy solution and bivariate Lévy‐stable random walk is associated with BLSBA to remove the trapping process into local optima. The energy detection is defined numerically from this optimal detection. At last, a multi‐taper spectral estimator (MSE) is proposed to cognitive radio detection for a huge network. The simulation results in both cases are computed and checked with the help of a BLSBA optimizer. For an individual secondary user scenario, the advancement of MSE to the ED is broadly described. Experimental result indicates that the objective false alarm likelihood is minimized and the demanded signal‐to‐noise ratio is accomplished to the extent.     

一种基于噪声估计的能量检测自适应门限新算法

频谱感知是认知无线电研究的关键技术之一,能量检测法是典型的频谱感知方法,但传统能量检测法的性能很容易受噪声功率变化影响。本文提出一种基于噪声方差估计的能量检测自适应门限算法。该算法基于噪声统计模型实时估计噪声方差,自适应地设置判决门限,达到更充分利用频谱资源的目的。仿真结果证明,该算法能准确估计噪声方差,有效地提高频谱检测性能。     

Non-parametric Blind Spectrum Sensing Based on Censored Observations for Cognitive radio

In cognitive radio, spectrum sensing is a challenging task. In this paper, a spectrum sensing method based on censored observations is proposed. We call it as Censored Anderson Darling (CAD) sensing. We present the performance of the CAD sensing method with receiver operating characteristics (ROC) in fading channels using simulations. It is observed that the proposed method outperforms the conventional energy detection (ED) at lower signal to noise ratio. It also provides better detection performance compared to Ordered Statistics (OS) based sensing method. We also use the CAD sensing method assuming noise uncertainty. It means noise variance at secondary user(SU) is unknown. We call it as Blind CAD sensing (B-CAD). We also show that the B-CAD outperforms the energy detection.     

Development of analytical model for energy detectors with SC and MRC diversity over Inverse‐Gamma fading

An ever‐escalating demand for wireless applications has caused great concern for the proper exploitation of the accessible radio spectrum. Cognitive radio materializes as an auspicious remedy to the present‐day crisis of spectral congestion, by detecting the licensed primary user (PU). This is accomplished with the assistance of the spectrum sensing technique, which provides an indication of the presence of PU over the spectrum. Energy detection is one of the prevailing spectrum sensing techniques due to its low implementation complexity. In the present work, the performance of an energy detector (ED) over Inverse‐Gamma (I‐Gamma) fading distribution is examined. Initially, a closed‐form expression of the probability density function for I‐Gamma distribution with maximal ratio combining diversity reception is derived. Following it, an investigation of an ED‐based cognitive radio device is carried out in the form of the average probability of detection (PD) and average area under the receiver operating characteristic curve (AUC). In addition, we also present a performance analysis of an ED with selection combining diversity. Optimization of the detection threshold is also executed alongside the low signal‐to‐noise ratio analysis. In the end, the resulting expression of the PD is exploited to examine the functioning of cooperative spectrum sensing within the erroneous environment. The validation of derived mathematical forms has been confirmed by comparing it with the Monte‐Carlo simulation and exact numerical results.     

Reducing the effects of the noise uncertainty in energy detectors for cognitive radio networks

Because of its ease of implementation and minimum requirements about the primary signals' information, energy detection is broadly considered for signal detection in spectrum sensing algorithms. However, the noise uncertainty phenomenon, caused by the random variations in the noise power, degrades the performance of an energy detector, particularly when the signal‐to‐noise ratio (SNR) is low. In this work, we propose to reduce the negative effects of the noise uncertainty in the performance of an energy detector by dynamically adapting its detection threshold to the noise conditions experienced at each sensing epoch. The noise power is estimated from the received signal samples using an algorithm based on a high‐pass filters bank and median filtering. With our proposal, it is possible to maintain a constant and low false alarm rate in the presence of noise uncertainty, without increasing the probability of misdetection, even in the low SNR regime, and without increasing the number of samples considered for spectrum sensing. Copyright © 2014 John Wiley & Sons, Ltd.     

基于能量检测与Sevcik分形维数的协作频谱感知算法

在认知无线网络中,针对单节点频谱感知易受到噪声不确定性的影响和传统的能量检测法在高噪声功率场景中检测性能较差等问题,根据Sevcik分形维数(Sevcik fractal dimension, SFD)对噪声不敏感、能够区分信号与噪声波形的特点,提出一种将自适应门限的能量检测法与SFD相结合的协作频谱感知方法. 通过能量检测法对接收信号进行检测判决,然后由SFD对判定为主用户不存在的信号进行复检,并将所有检测结果进行K秩融合,根据融合结果得出最终判决. 仿真结果表明,本文提出的频谱感知方法对噪声不敏感,在低信噪比下的检测性能得到显著提高.     

Smart antenna‐assisted spectrum sensing for robust detection in cognitive radio networks

In this paper, we consider the problem of multiband spectrum sensing by employing smart antenna arrays at the cognitive receiver. Although energy detection is widely used for spectrum sensing in cognitive radio networks because of its simplicity and accuracy, it is severely deteriorated by the noise uncertainty. This paper introduces robust spectrum sensing techniques to circumvent this difficulty, which operate simultaneously over the total frequency channels rather than a single channel each time. To enhance the detection performance, the proposed schemes jointly utilize the information of eigenvalues and eigenvectors, signal and noise subspace components in conjunction with the likelihood functions and Gerschgorin radii. Neither subjective decision threshold setting nor the estimation of noise power is required in our schemes, making them robust to noise uncertainty. Simulations are presented to validate the performance of the proposed schemes, and the results show that our schemes can outperform other existing spectrum sensing methods. Copyright © 2014 John Wiley & Sons, Ltd.     

Robust spectrum sensing under noise uncertainty for spectrum sharing

Spectrum sensing plays an important role in spectrum sharing. Energy detection is generally used because it does not require a priori knowledge of primary user, (PU) signals; however, it is sensitive to noise uncertainty. An order statistics (OS) detector provides inherent protection against nonhomogeneous background signals. However, no analysis has been conducted yet to apply OS detection to spectrum sensing in a wireless channel to solve noise uncertainty. In this paper, we propose a robust spectrum sensing scheme based on generalized order statistics (GOS) and analyze the exact false alarm and detection probabilities under noise uncertainty. From the equation of the exact false alarm probability, the threshold value is calculated to maintain a constant false alarm rate. The detection probability is obtained from the calculated threshold under noise uncertainty. As a fusion rule for cooperative spectrum sensing, we adopt an OR rule, that is, a 1‐out‐of‐N rule, and we call the proposed scheme GOS‐OR. The analytical results show that the GOS‐OR scheme can achieve optimum performance and maintain the desired false alarm rates if the coefficients of the GOS‐OR detector can be correctly selected.     

利用样本特征的盲频谱感知算法

为克服噪声不确定度及噪声方差的影响,利用样本特征构造了新的检验统计量,推导了频谱空闲、频谱占用时检验统计量的概率密度函数,提出基于F分布的盲频谱感知算法,但其判决门限受采样点数影响;为此,利用Anderson-Darling准则提出基于F分布的拟合度检验算法。在高斯信道下对两种算法进行了仿真,并与能量检测算法、GOF算法仿真结果比较可知,所提两种算法性能优于噪声方差已知的能量检测算法,并克服能量检测算法、GOF算法受噪声不确定度以及噪声方差影响这一缺陷。      

利用功率谱极值和几何平均的频谱感知算法

为了提高基于功率谱的频谱感知算法抗噪声不确定性、抗频偏及低信噪比下检测性能,本文利用功率谱的部分样本平均估计最大值,以降低信号频偏对频谱感知性能影响;利用功率谱的最大值与最小值之差与功率谱几何平均之比作为判决统计量,以尽可能消除噪声影响及保留主用户信号;推导得到了检测门限表达式,表明该算法对噪声不确定性不敏感。加性高斯白噪声信道和瑞利衰落信道下的仿真结果表明:该算法频谱感知性能优于已有的基于功率谱的频谱感知算法,降低了未知载波频偏和噪声不确定性对频谱感知算法性能的影响,该算法能够有效检测实际信号。      

基于随机共振和非中心F分布的频谱感知算法

为解决频谱感知算法在低信噪比（SNR）时检测概率较低且检测所需采样点数较多的问题,提出了基于随机共振和非中心F分布（SRNF）的频谱感知算法。通过引入直流随机共振噪声,建立了SRNF的系统模型,推导了服从非中心F分布的检验统计量表达式、虚警概率与检测概率以及判决门限表达式,并采用数值法求解最佳的随机共振噪声参数。仿真结果表明,在低信噪比时,所提基于SRNF算法的检测性能优于能量检测（ED）算法和基于F分布的盲频谱感知（BSF）算法,当虚警概率为5%、信噪比为–12 d B、采样点数为200时,所提算法的检测概率是95%,分别比BSF算法和ED算法高34%和67%;当信噪比为–12 dB、检测概率达到95%时,所提算法所需的采样点数是210,比BSF算法节省了340个采样点。此外,噪声不确定度对所提算法的影响小于ED算法。     

实高斯信号下的能量检测技术

认知无线电被认为是目前提高频谱资源利用率的最有效手段。次用户要使用主用户的空闲频段,必须能够准确的检测到主用户是否出现。能量检测技术由于无需信号的任何先念知识,应用最为广泛。在实高斯信号下,对基于能量的独立检测和协作检测方式进行了仿真研究。仿真结果表明：信号样本数和参与协作的用户数的增加可以有效地改善系统检测性能,但当用户数达到一定数目后,错误率基本无变化,反而还会增加系统开销。     

利用LMS的频谱感知算法

针对低信噪比下的频谱感知问题,提出一种基于最小均方算法(LMS)的不受噪声不确定度影响的频谱感知算法。本文利用LMS算法对原始发送信号的幅度进行实时估计,并以其估计值作为检验统计量,判断主用户是否存在,实现频谱感知。理论和仿真结果均表明,此方法对微弱信号的检测能力较强,且性能明显优于能量检测算法,通过对噪声方差的实时估计,可以有效克服噪声不确定度的影响。      

低信噪比条件下基于随机共振的感知方法与性能分析

针对认知网络实际环境中常呈现出噪声高动态变化、低信噪比特征,无法快速准确进行频谱感知的问题,本文将物理学非线性领域中的随机共振理论引入到频谱感知中,提出了一种基于广义随机共振的能量检测算法.该算法引入匹配噪声,通过匹配非线性系统、噪声和信号三者的关系,从而改变能量检测统计量的分布,有效地检测信号的存在性.本文从理论上推导了最佳匹配噪声的表达式,并得到了检测性能、受噪声不确定度的影响、感知时间等方面的重要理论结论.仿真结果验证了理论推导的正确性,表明所提算法能够在信噪比为-20dB等低信噪比条件下较现有能量检测算法提高3dB以上,且具有感知速度快、受噪声不确定度影响小等特点.     

An Improved Spectrum Sensing Method: Energy-Autocorrelation-Based Detection Technology

Due to the environmental noise, variance is uncertain and unknown. The energy-based detection (ED) technology has many shortcomings in cognitive radio. In the paper, an energy-autocorrelation detection (EAD) algorithm is proposed to overcome these challenges, taking advantage of the different characteristics of Gauss white noise and signal. Two statistics are structured based on energy and autocorrelation of samples. This spectrum sensing algorithm can lead to stable and accurate detection performance without any prior information on noise and signal. It is testified in the simulation that the energy-autocorrelation-based detection is much better than energy-based detection; moreover, the impact of some parameters of the algorithm is also simulated and discussed.     

基于小波包变换的能量检测技术研究

频谱感知技术是认知无线电实现的关键,对解决频谱资源匮乏的问题起着举足轻重的作用,因此受到业界的广泛关注。在分析比较了三种常见的频谱检测技术后,提出了一种在未知噪声下的基于小波包变换的能量检测算法,通过小波包变换对噪声和信号功率进行估计从而得到较为准确的判决门限。仿真结果显示,在未知噪声情况下的该算法具有较好的鲁棒性,有望成为适于认知无线电应用的频谱感知技术。     

Cognitive radio network with coordinated multipoint joint transmission

Cognitive radio (CR) is considered to be a promising technology for future wireless networks to make opportunistic utilization of the unused or underused licensed spectrum. Meanwhile, coordinated multipoint joint transmission (CoMP JT) is another promising technique to improve the performance of cellular networks. In this paper, we propose a CR system with CoMP JT technique. We develop an analytical model of the received signal‐to‐noise ratio at a CR to determine the energy detection threshold and the minimum number of required samples for energy detection–based spectrum sensing in a CR network (CRN) with CoMP JT technique. The performance of energy detection–based spectrum sensing under the developed analytical model is evaluated by simulation and found to be reliable. We formulate an optimization problem for a CRN with CoMP JT technique to configure the channel allocation and user scheduling for maximizing the minimum throughput of the users. The problem is found to be a complex mixed integer linear programming. We solve the problem using an optimization tool for several CRN instances by limiting the number of slots in frames. Further, we propose a heuristic‐based simple channel allocation and user scheduling algorithm to maximize the minimum throughput of the users in CRNs with CoMP JT technique. The proposed algorithm is evaluated via simulation and found to be very efficient.     

利用秩和检验的多天线协作频谱感知

高效稳定的频谱感知是认知无线电系统的关键环节.传统的能量检测算法受噪声不确定性影响,而协方差矩阵类算法在天线相关性低时性能较差.针对上述缺陷,利用秩来衡量由信道衰落导致的同一感知时刻不同天线上的信号功率差异,提出通过构建秩和统计量来实现频谱感知的算法.另外,推导了所提算法判决门限的理论表达式,结果显示其不受采样点数影响,因此当采样点数变化时无需重新设置门限.理论分析和仿真表明所提算法不受噪声不确定度的影响,并且在低天线相关性时可以保持良好的性能.     

Adaptive Spectrum Sensing Algorithm in Cognitive Ultra-wideband Systems

Energy detection is a simple spectrum sensing technique that compares the energy in the received signal with a threshold to determine whether a primary user signal is present or not. Setting the threshold is very important to the performance of the spectrum sensing. This paper proposes an adaptive spectrum sensing algorithm where an optimal decision threshold of energy detection is derived based on minimizing the weighted sum of probabilities of detection and false alarm. Since the optimal decision threshold is dependent on the noise power and signal power, a simple, practical frequency domain approach is devised to estimate both. The algorithm can be used for the detection of various kinds of signals without any prior knowledge of the signal, channel or noise power, and is able to adapt to noise fluctuation. Simulations for detecting narrow-band and wideband signals (phase shift keying signal, frequency shift keying signal, orthogonal frequency division multiplexing signal) and ultra-wideband (UWB) signals (direct sequence spread spectrum signals) in an IEEE 802.15.3a UWB band are presented. The results show that the proposed algorithm has excellent robustness to noise uncertainty and outperforms the existing spectrum sensing algorithms in the literature.