一种改进的认知无线电频谱接入策略

认知无线电（Cognitive Radio）网络中,次用户在不干扰主用户的情况下即时接入检测到的空闲频谱以提高频谱资源的利用率。基于部分可观测马尔科夫决策过程的最优频谱接入算法计算复杂度较高,而基于贪婪算法的次优接入策略具有自私性,都不能有效的提高频谱利用率。本文提出一种新的频谱接入算法,该算法对贪婪算法的自私性进行了改进。由于贪婪算法只关注信道当前时隙的瞬时奖励值,导致最大奖励值相同的信道可能会有多个,因此给瞬时奖励值加上这些信道下一时隙的奖励值后重新选择,直至次用户选择出奖励值最大的一个信道。仿真结果表明,相比于传统的贪婪算法,改进算法虽然增加了一些计算复杂度,但有效提高了系统的吞吐量。     

基于人工神经网络的认知无线电频谱感知研究

认知无线电是一种新的智能无线通信技术,通过感知周围环境特征,并采用构建理解的方法进行学习。频谱感知技术是认知无线电系统设计的重要组成部分,本文提出了基于人工神经网络（ANN）算法的认知无线电感知技术。由于单用户检测存在检测精度不高的问题,本文通过采用ANN的自主学习能力设计频谱检测分类器,对以往分类器所收集的环境信息进行学习和储存,从而对以后感知新的环境时起到先验信息的作用,能快速准确地判决出主用户的存在与否。通过与能量检测和循环平稳检测对不同调制方式信号进行信号识别仿真实验可以看出,相比原有的单用户频谱检测技术,ANN具有更高的有效性和可靠性。     

Sensor node-assisted asynchronous cooperative spectrum sensing for cognitive radio network

In order to take advantage of the asynchronous sensing information, alleviate the sensing overhead of secondary users （SUs） and improve the detection performance, a sensor node-assisted asynchronous cooperative spectrum sensing （SN-ACSS） scheme for cognitive radio （CR） network （CRN） was proposed. In SN-ACSS, each SU is surrounded by sensor nodes （SNs）, which asynchronously make hard decisions and soft decisions based on the Bayesian fusion rule instead of the SU. The SU combines these soft decisions and makes the local soft decision. Finally, the fusion center （FC） fuses the local soft decisions transmitted from SUs with different weight coefficients to attain the final soft decision. Besides, the impact of the statistics of licensed band occupancy on detection performance and the fact that different SNs have different sensing contributions are also considered in SN-ACSS scheme. Numerical results show that compared with the conventional synchronous cooperative spectrum sensing （SCSS） and the existing ACSS schemes, SN-ACSS algorithm achieves a better detection performance and lower cost with the same number of SNs.     

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.     

Binary decision-based collaborative spectrum sensing in infrastructure-less cognitive radio network

Collaborative spectrum sensing (CSS) plays a vital role in achieving accurate spectrum sensing results in a cognitive radio network (CRN). However, CSS in infrastructure-less CRNs is a challenging task as there is no central authority which aggregates individual sensing reports to take a final decision. Hence, it becomes the responsibility of each node to aggregate sensing reports of other nodes to take the final decision. Towards this end, we present two collaborative spectrum sensing techniques for infrastructure-less CRN based on message passing. The first technique is distributed binary decision-based CSS (DBCSS), in which a node broadcasts its sensing report and uses the binary decisions of its h-hop neighbors for making its final sensing decision. The second one is distributed cluster-based CSS (DCCSS) in which a cluster head consolidates the sensing reports in its cluster into a message and broadcast this message in the network. We support the validity of the proposed schemes through extensive simulation results.     

Detection efficiency of cooperative spectrum sensing in cognitive radio network

Sensing the spectrum in a reliable and efficient manner is crucial to cognitive radio. To combat the channel fading suffered by the single radio, cooperative spectrum sensing is employed, to associate the detection of multiple radios. In this article, the optimization problem of detection efficiency under the constraint of detection probability is investigated, and an algorithm to evaluate the required radio number and sensing time for maximal detection efficiency is presented. To show the effect of cooperation on the detection efficiency, the proposed algorithm is applied to cooperative sensing using the spectral correlation detector under the Rayleigh flat fading channel.     

基于改进群搜索优化算法的认知无线电协作频谱感知

提出了一种基于改进群搜索优化的认知无线电协作频谱感知方法。用改进的群搜索优化算法求解线性协作感知模型中的权重向量,并将本文方法与基于单节点感知、选择合并、等增益合并和MDC的频谱感知方法进行了比较,仿真结果表明基于改进群搜索优化算法的协作频谱感知较传统的群搜索优化算法具有更好的收敛性,可获得更高的检测概率,且检测性能随感知用户数的增加而提高、随噪声环境的恶化而降低。仿真结果验证了本文方法的优越性。     

认知无线电网络频谱感知与接入算法研究

在对认知无线电网络频谱感知和接入研究成果进行分析的基础上,文章首先讨论了认知无线电网络频谱感知的基本方法以及协作频谱感知、单智能体和多智能体强化学习算法,比较了各种算法的优缺点,并展望了这一领域未来研究的方向。     

Cyclo-energy detector for spectrum sensing in cognitive radio

In this paper, a cyclo-energy detector is proposed for the spectrum sensing problem based on the cyclostationary signal analysis. The proposed detector can determine the existence of primary user by estimating the primary user's received power according to the cyclostationarity features of the secondary user. By using this detector, the secondary user does not need to stop transmitting signals when sensing the frequency spectrum. Simulation results are presented to confirm the performance of the proposed detector.     

认知无线网络频谱感知与节点协同问题综述

认知无线电是被公认的解决移动无线网络频谱资源短缺和频谱资源动态分配问题的最有效和最有潜力的技术。基于认知无线电技术目前在移动无线网络中的实际应用情况,详细分析了认知无线网络目前所存在的无线频谱资源感知、探测和接入机制以及多用户节点协同等问题,并综述了频谱资源分配算法和认知无线网络容量问题的最新研究进展,最后提出用博弈论来研究解决认知无线网络多用户协同问题中的用户自私行为。     

Secure centralized spectrum sensing for cognitive radio networks

Spectrum utilization becomes more and more important while new communication techniques keep increasing and the spectrum bands remain finite. Cognitive radio is a revolutionary technology to make use of the spectrum more effectively. In order to avoid the interference to the primary user, spectrum sensing must be sensitive and reliable. Cooperative spectrum sensing (CSS) is one of the ways to increase the reliability of spectrum sensing. The information fusion technique is a key component of CSS. In this paper, we proposed a novel fusion scheme based on spatial correlation technique. We utilize geographical information with reputational weights to propose a two-level fusion scheme called secure centralized spectrum sensing (SCSS). The simulation results show that as the attackers present high density aggregation at some areas, the correct sensing ratio of SCSS is increasing as well even when the number of attackers is very large.     

最优线性认知无线电协作频谱感知技术研究

频谱感知是检测频谱空穴,实现动态频谱接入的前提和关键。单认知用户检测存在阴影效应和多径衰落等影响,检测效果较差。多用户协作频谱感知是目前频谱感知的主要手段。文中针对常用的协作频谱感知方法的不足,提出一种基于最优线性加权的协作频谱感知方法,对其检测模型与检测算法进行分析,并进行仿真。仿真结果表明,最优线性加权协作频谱感知在相同的条件下相比常用协作频谱感知具有更大的检测概率,更优良的检测性能。     

基于多层分簇优化的认知无线电网络协作频谱感知策略

针对认知无线电网络(CRN)中基于簇的频谱感知策略的检测性能和能耗问题,提出一种基于多层分簇优化的协作频谱感知策略。首先,将CRN分成多个簇,进而将簇分成多个组,再将组分成多个子组,构建三层分簇结构;然后,利用提出的优化算法获得最优的分簇参数和决策阈值;最后,通过投票机制和K-out-of-N规则对各级决策进行聚合,进行频谱感知。实验结果表明,该方案在获得较高主要用户(PU)频谱占用检测率的同时,能够最大限度地减少信道开销,提高了网络的吞吐量。     

基于 DS 证据理论的协作频谱感知改进方法

提出了一种新的协作频谱感知方法,该方法采用DS证据理论。首先,在本地感知通过投影近似法分解感知结果以减少次级用户上报至融合中心的感知数据,从而降低协作带宽开销。其次,在融合中心根据次级用户证据矢量之间的距离计算可信度,以可信度为权重对每个次级用户的基本概率分配进行加权平均来减小高度冲突数据对检测性能的影响。仿真结果表明,该方法减少了协作感知的带宽开销,改善了数据高度冲突时的检测性能。     

基于信任度与信噪比的认知无线电协同频谱检测

无线网络中存在信噪比较高的恶意认知用户的情况,为了有效利用可靠的认知无线电(CR)技术用户的本地感知结果,提出了一种基于信任度的信噪比比较协同频谱感知算法,可有效剔除认知网络中存在的信噪比较高的恶意认知用户。仿真实验表明,在存在恶意认知用户的认知无线电网络中,该算法检测性能优于传统的或准则(OR)数据融合的协同频谱感知算法以及基于信噪比比较协同频谱感知算法。     

多输入多输出系统基于特征函数频谱盲检测

提出多输入多输出（Multiple-Input Multiple-Output,MIMO）认知无线电系统中基于多元特征函数的频谱盲检测方法.所提方法不需要知道授权用户传输信号的先验知识,也不需要知道信道噪声的方差.通过计算次用户多天线系统接收到的信号样本向量的经验特征函数,MIMO系统中的频谱检测就转换为多元特征函数检验问题.通过度量经验特征函数与已知特征函数的距离, 实现频谱检测.在MIMO系统中,基于特征函数的频谱检测算法比现存的频谱检测方法,具有更好的性能,特别是在低信噪比、小样本条件下.     

基于空间谱的多天线盲频谱感知算法

现有的频谱感知算法主要在时间、频率以及地理空间维度进行检测,对角度维的利用尚不成熟。将多天线技术中的到达角（AOA, angle of arrival）估计算法应用到频谱感知领域,提出了2种基于空间谱的盲频谱感知算法,分别为最大—最小延迟相加谱值比检测和平均—最小延迟相加谱值比检测。利用空域匹配滤波的优势,新算法在低信噪比下得到了较高的检测概率,同时为角度维的频谱接入提供了AOA信息,从而提高了频谱利用率。此外,运用随机矩阵理论,推导了检测阈值和检测概率的理论值。仿真结果表明,在Nakagami-m信道下,提出的算法具有比现有盲感知算法更优的检测性能。     

Secure spectrum sensing in relay-based cognitive radio networks

Wireless Networks - A novel cooperative spectrum sensing algorithm intended for robust sensing in the presence of Byzantine attacks was formulated for relay-based cognitive radio networks, with the...     

Compressive slow-varying wideband power spectrum sensing for cognitive radio

Wideband spectrum sensing is a critical component of a functioning cognitive radio system. Its major challenge is the too high sampling rate requirement. Compressive sensing (CS) promises to be able to deal with it. Nearly all the current CS-based compressive wideband spectrum sensing methods exploit only the frequency sparsity to perform. This paper sets up a new signal model which is sparse in both temporal and frequency domain. Motivated by the achievement of a fast and robust detection of the wideband spectrum change, total variation minimization is incorporated to exploit the temporal and frequency structure information to enhance the sparsity level. As a sparser vector is obtained, the spectrum sensing period would be shortened and sensing accuracy would be enhanced. Both theoretical analysis and numerical experiments demonstrate the performance improvement.     

Pre-reservation based spectrum allocation for cognitive radio network

Studies on the current usage of the radio spectrum by several agencies have already revealed that a large fraction of the radio spectrum is inadequately utilized. This basic finding has led to numerous research initiatives. Cognitive radio technology is one of the key candidate technologies to solve the problems of spectrum scarcity and low spectrum utilization. However, random behavior of the primary user (PU) appears to be an enormous challenge. In this paper, a Pre-reservation based spectrum allocation method for cognitive radio network is proposed to apply a PU behavior aware joint spectrum band (SB) selection and allocation scheme. In the first step, the SB is observed in terms of PU usage statistics whereas in the second phase, a network operator (NO) using a spectrum allocation scheme is employed to allocate SBs among secondary users (SUs). We also introduce the concept of reservation and exchange functionality under the priority serving strategy in a time-varying framing process. Simulation results show that the proposed scheme outperforms existing schemes in terms of the spectrum utilization and network revenue. In addition, it helps NO to manage the spectrum on a planned basis with a systematical spectrum reservation management where the NO has the status of time slots. Moreover, SUs have an opportunity to reserve or instantly request a SB that maximizes the SUs satisfaction in terms of quality of experience.