Energy-Based Collaborative Spectrum Sensing for Cognitive UWB Impulse Radio

This paper focuses on the issue of collaborative spectrum sensing in cognitive ultra wideband (CUWB) impulse radio. We employ energy-based signal detection method and apply the Neyman-Pearson (NP) deci...     

认知无线电网络中基于最大似然准则的协同频谱检测算法研究

提出一种集中式频谱协同检测算法。各认知节点采用能量检测算法,然后使用最大似然准则进行本地判决,且把似然比作为本地判决可靠性的度量;中心节点基于可信度对接收到的认知节点本地检测数据进行数据融合。仿真结果显示,文中提出的认知节点协同频谱检测方案能够减少误检概率。特别是当信道处于深衰落时,少量节点参与协同就能获得较好的检测性能。     

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。     

基于最大最小特征值之差的频谱感知技术研究

频谱感知技术是认知无线电系统的关键技术之一,该文基于阵列天线理论,利用并分析接收信号相关矩阵,并以其最大与最小特征值之差作为检验统计量,进而判断主用户是否存在,实现频谱感知。理论分析和仿真结果均表明,此方法的感知性能明显优于能量检测算法,并且有效地解决了噪声不确定度对检测性能的影响。     

认知无线电的频谱感知算法研究

频谱感知技术、频谱共享技术、频谱管理技术是当前认知无线电的三大核心技术,而频谱感知技术是实现后续的频谱共享和频谱管理的前提。因此主要对当前认知无线电频谱感知技术的基础理论、算法模型以及频谱感知的具体算法做了详尽的分类,并利用Matlab仿真平台对主要的频谱感知算法进行了仿真,并根据仿真结果,比较了各个算法的性能优劣性,可为认知无线电用户的算法选择提供参考,同时也为认知无线电后续的频谱共享和频谱管理工作奠定一定基础。     

一种新的认知无线电频谱感知方法

认知无线电是一种用于提高无线电通信频谱利用率的新的智能技术。首先简述了认知无线电的背景和概念,然后针对认知无线电频谱感知的能力,对比分析了现有的三种频谱检测方法：匹配滤波器法、能量检查法和循环平稳特征检测法,在对其进行研究的基础上,提出一种将能量检测法和循环平稳特征检测法相结合的双门限检测法,通过仿真验证了该方法的有效性和优越性。     

认知无线电的频谱感知技术研究

认知无线电技术接入灵活,能够大大提高频谱的利用率,被认为是当前解决频带使用拥挤的最佳方案。认知无线电是一种可以感知周围通信环境来改变发射机在特定的参数上实时改变的智能通信系统。它采用动态频谱管理来提高频谱利用率,高可靠性的频谱感知是实现频谱共享的关键技术,对频谱感知技术的匹配滤波器检测、能量检测、静态循环特征检测和合作检测等进行了讨论,并分析了各种方法的特点和性能。     

Spectrum Sensing Framework for Cognitive Radio Networks

Spectrum sensing feature of cognitive radio devices represents a cornerstone characteristic facilitating real-time and accurate spectrum occupancy measurements in cognitive radio networks. It practically enables the cognitive radio devices to detect vacant spectrum holes and use them for their communication purposes. There are numerous spectrum sensing methods proposed in the literature ranging from local based ones to cooperative strategies among several devices increasing the confidence level of the detected spectrum. This paper gives a general spectrum sensing framework for cognitive radio networks, classifies and explores different spectrum sensing techniques and approaches and shows practical examples, from authors’ own experience, of realized spectrum sensing engines and strategies along with some obtained results.     

一种新的认知无线电频谱感知算法

频谱感知是认知无线电的关键技术之一,通过检测授权用户信号的有无来发现频谱空穴,以提高频谱利用率。基于接收信号的统计协方差理论,提出一种新的认知无线电频谱感知算法,无需信号的先验信息,且计算复杂度较低。仿真分析了该算法,结果表明其在低信噪比环境下,较传统能量检测法有更好的感知性能。     

An Adaptive Multitaper-SVD Spectrum Sensing Method for OFDM-Based Cognitive Radio Systems

Cognitive radio (CR) has been proposed and widely investigated as an approach for increasing the spectrum efficiency. CR devices exploit so called white spaces in the spectrum allocated to the primary users (PU) by a process commonly referred to as the spectrum sensing. In this work, we use the singular value decomposition (SVD) for spectrum sensing in orthogonal frequency division multiplexing-based (OFDM) CR systems. A single input multiple output channel is assumed between the PU and the secondary user equipped with multiple antennas. At the CR side, the multitaper method (MTM) is used for the spectrum sensing in each antenna. As a first contribution, we aim at reducing the time necessary to perform spectrum sensing. To this end, we propose an adaptive MTM–SVD spectrum sensing method that decreases the sensing time. As a second contribution, we formulate a three dimensional SVD (referred to as 3-D SVD) scheme that efficiently processes signals and quantities related to multiple antenna traffic, OFDM multiple blocks and different tapers, simultaneously. Simulation results indicate that the proposed adaptive MTM–SVD decreases the sensing time by about 61–69 % for various proposed adaptive algorithms, compared to the conventional MTM–SVD method. Besides, performance improvement in probability of detection is achieved from 2–13 % for a predefined probability of false alarm by using adaptive MTM–SVD. In addition to further reduction of the sensing time, the proposed 3D-MTM–SVD outperforms conventional methods for the low probability of the false alarm.     

感知无线电中的高效频谱侦测方案

感知无线电是一种能有效提高频谱利用率的先进技术,他能在主用户没有使用频谱的时候发现并使用"频谱空洞".近来有研究结果表明,在衰落和阴影效应影响下频谱侦测的结果会有大幅度下降.为了削弱衰落和阴影效应的影响联合侦测诞生了,传统的联合侦测方式在每个用户所受衰落不同的情况下不能有效的工作.文章提出一种加权联合侦测来提高联合侦测性能.由大量分析和仿真结果可以表明加权联合侦测能大幅度提高侦测性能.     

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。     

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.     

Blind Spectrum Sensing Algorithms for Cognitive Radio Networks

In a cognitive radio network, the spectrum that is allocated to primary users can be used by secondary users if the spectrum is not being used by the primary user at the current time and location. The only consideration is that the secondary users have to vacate the channel within a certain amount of time whenever the primary user becomes active. Thus, the cognitive radio faces the difficult challenge of detecting (sensing) the presence of the primary user, particularly in a low signal-to-noise ratio region, since the signal of the primary user might be severely attenuated due to multipath and shadowing before reaching the secondary user. In this paper, a blind sensing algorithm is derived, which is based on oversampling the received signal or by employing multiple receive antennas. The proposed method combines linear prediction and QR decomposition of the received signal matrix. Then, two signal statistics are computed from the oversampled received signal. The ratio of these two statistics is an indicator of the presence/absence of the primary signal in the received signal. Our algorithm does not require the knowledge of the signal or of the noise power. Moreover, the proposed detection algorithm in this paper is blind in the sense that it does not require information about the multipath channel distortions the primary signal has undergone on its way to reaching the secondary user. Simulations have shown that our algorithm performs much better than the commonly used energy detector, which usually suffers from the noise uncertainty problem.      

Collaborative Cyclostationary Spectrum Sensing for Cognitive Radio Systems

This paper proposes an energy efficient collaborative cyclostationary spectrum sensing approach for cognitive radio systems. An existing statistical hypothesis test for the presence of cyclostationarity is extended to multiple cyclic frequencies and its asymptotic distributions are established. Collaborative test statistics are proposed for the fusion of local test statistics of the secondary users, and a censoring technique in which only informative test statistics are transmitted to the fusion center (FC) during the collaborative detection is further proposed for improving energy efficiency in mobile applications. Moreover, a technique for numerical approximation of the asymptotic distribution of the censored FC test statistic is proposed. The proposed tests are nonparametric in the sense that no assumptions on data or noise distributions are required. In addition, the tests allow dichotomizing between the desired signal and interference. Simulation experiments are provided that show the benefits of the proposed cyclostationary approach compared to energy detection, the importance of collaboration among spatially displaced secondary users for overcoming shadowing and fading effects, as well as the reliable performance of the proposed algorithms even in very low signal-to-noise ratio (SNR) regimes and under strict communication rate constraints for collaboration overhead.     

基于频谱感知结果的认知无线电用户分簇方法

认知无线电用户的分簇由于不同位置频谱环境的巨大差异而成为亟待解决的问题之一。该文分析了认知无线电用户频谱感知结果之间相关性,基于分析结论提出一种基于频谱感知结果相关性的分簇算法,并考察了频谱感知结果量化对该算法的性能影响,得出增加频段数量的方式能够部分补偿量化带来的性能损失的结论。仿真显示,该文的分簇算法能够有效地对认知无线电用户进行分簇,在频谱感知结果量化的条件下,也能达到较好的分簇性能。从可靠性、准确性和自适应能力等方面,相较传统基于地理位置的分簇方法,该文所提出的分簇算法能综合考虑频谱环境的特点,更加具实用价值。     

Nonparametric Multicycle Spectrum Sensing Method by Segmented Data Processing for Cognitive Radio

This paper proposes a novel multicycle spectrum sensing method. The method first divides the received data samples into several segments, and then gets cyclic autocorrelation estimates from these segments, constructs a test statistic from these estimates, and finally makes an F-test to determine whether there is a primary signal or not. The method can use multiple cyclic frequencies simultaneously; hence, it is a multicycle detector. The method is also nonparametric, and thus it is robust to noise uncertainty. Compared with existing cyclic spectrum sensing methods, the proposed method can reduce computational complexity significantly at the cost of a little performance loss. Simulation results are given to show the validity and the superiority of the proposed method.     

感知无线电中的频谱检测技术

感知无线电可以自动感知外界的无线环境并能调整系统参数以适应环境的变化。频谱检测是感知无线电的关键技术之一，要求感知用户可靠、快速地检测频谱空洞。文中按照主用户发射端检测和主用户接收端检测，对当前的主要频谱检测算法进行了分类，并对频谱检测算法进行了讨论和对比研究，给出了在IEEE802．22和频谱池结构中的频谱检测的具体应用。     

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.