子空间投影的频谱感知算法研究

为了发现空间中的“频谱空洞”而加以利用以使频谱利用率最大化,频谱感知技术得到了广泛关注。已有基于特征矢量的频谱感知算法因涉及大量特征值分解运算导致算法运算量大,不适应实时检测。本文提出的频谱感知算法利用信号子空间和噪声子空间之间的正交性,将次用户接收信号分别投影到上述子空间,根据投影值的差异实现快速频谱感知。理论分析和仿真结果表明本文提出的算法与已有算法相比有效降低了运算量,检测性能不受噪声不确定度影响、不需要预知主用户先验知识和噪声方差,且低信噪比、小采样情况下有更优越的检测性能。      

Combined energy detection and one-order cyclostationary feature detection techniques in cognitive radio systems

One of the main requirements of cognitive radio systems is the ability to detect the presence of the primary user with fast speed and precise accuracy.To achieve that,a possible two-stage spectrum sensing scheme is suggested in this paper.More specifically,a fast spectrum sensing algorithm based on the energy detection is introduced focusing on the coarse detection.A complementary fine spectrum sensing algorithm adopts one-order cyclostationary properties of primary user's signals in time domain.Since the one-order feature detection is performed in time domain,the real-time operation and low-computational complexity can be achieved.Also,it drastically reduces hardware burdens and power consumption as opposed to two-order feature detection.The sensing performance of the proposed method is studied and the analytical performance results are given.The results indicate that better performance can be achieved in proposed two-stage sensing detection compared to the conventional energy detector.     

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.     

基于快速变极化的认知无线电频谱感知算法

为充分利用授权信号的极化状态矢量信息进行频谱感知,该文提出一种基于K臂赌博机的快速变极化算法,通过将双自由度搜索问题转化为K个1维问题,能够实现满足实时频谱感知要求的授权信号极化状态识别。给出了算法收敛时预期收益的上下确界。最后分析了基于该算法进行极化匹配接收的频谱感知性能。仿真结果表明,算法能够快速收敛,并高精度识别授权信号极化状态,对频谱感知性能提升显著。     

Modified Bayesian algorithm‐based compressive sampling for wideband spectrum sensing in cognitive radio network using wavelet transform

This paper presents the implementation of a modified version of Bayesian relevance vector machine (RVM)‐based compressive sensing method on cognitive radio network with wavelet transform for spectrum hole detection. Bayesian compressive sensing is used in this work to deal with the complexity and uncertainty of the process. The dependency of the Bayesian compressive sensing on the knowledge of noise levels in the measurement has been relaxed through the proposed Bayesian RVM‐based compressive sensing algorithm. This technique recovers the wideband signals even with fewer measurements maintaining considerably good accuracy and speed. Wavelet transform is used in this paper to enable the detection of primary user (PU) even in the low regulated transmission from unlicensed user. The advantage of this approach lies in the fact that it enables the evaluation of all possible hypotheses simultaneously in the global optimization framework. Simulation study is performed to evaluate the efficacy of the proposed technique over the cognitive radio environment. The performance of the proposed technique is compared with the conventional Bayesian approach on the basis of recovery error, recovery time and covariance to verify its superiority.     

Optimal Energy Harvesting-based Weighed Cooperative Spectrum Sensing in Cognitive Radio Network

In cognitive radio (CR) network, to improve spectrum sensing performance to primary user (PU) and decrease energy wastage of secondary user (SU) in cooperative spectrum sensing, an energy harvesting-based weighed cooperative spectrum sensing is proposed in this paper. The SU harvests the radio frequency (RF) energy of the PU signal and then converts the RF energy into the electric energy to supply the power used for energy detection and cooperation. The time switching model and power splitting model are developed to realize the notion. In the time switching model, the SU performs either spectrum sensing or energy harvesting at any time, while in the power splitting model, the received PU signal is split into two signal streams, one for spectrum sensing and the other one for energy harvesting. A joint optimization problem is formulated to maximize the spectrum access probability of the SU by jointly optimizing sensing time, number of cooperative SUs and splitting factor. The simulation results have shown that compared to the traditional cooperative spectrum sensing, the proposed energy harvesting-based weighed cooperative spectrum sensing can decrease the energy wastage obviously while guaranteeing the maximum spectrum access probability.     

A reliable cooperative spectrum detection scheme in cognitive radio networks

Reliable spectrum detection of the primary user (PU) performs an important role in the cognitive radio network since it’s the foundation of other operations. Spectrum sensing and cognitive signal recognition are two key tasks in the development of cognitive radio (CR) technology in both commercial and military applications. However, when the CR terminals receiving signals have little knowledge about the channel or signal types, these two tasks will become much more difficult. In this paper, we propose a reliable cooperative spectrum detection scheme, which combines the cooperative spectrum sensing with distributed cognitive signal recognition. A novel improved cooperative sensing algorithm is achieved by using a credibility weight factor and the “tug-of-war” rule, which is based on the double threshold detection and Dempster–Shafer theory, to determine whether the PU signals exist. In this scheme, cognitive signal recognition can be used to identify the signal type when the PU signal is present. During the cognitive signal recognition processing, the CR terminals make local classification of the received signals by using Daubechies5 wavelet transform and Fractional Fourier Transform, and send their recognition results to the globe decision making center. A distributed processing uses these cognitive terminals’ local results to make final decisions under the Maximum Likelihood estimation algorithm. Simulation results show that the proposed method can achieve good sensing probability and recognition accuracy under the Additive White Gaussian Noise channel.     

基于预测的差分信号压缩感知算法

基于压缩感知的频谱感知方法可以较低的采样速率快速获取信号,并利用获得的稀疏数据样本来判断信道的占用情况。然而,压缩感知技术中信号重构算法的复杂度很高,难以满足无线通信中的实时性要求。本文提出一种基于预测的差分信号压缩感知算法,该算法利用信道占用时间上的相关性,建立了一种信道占用情况的预测模型,依此模型预测出信道占用的变化情况;基于预测结果,在重构信号时可减少频点的搜索范围,两次降低重构算法的运算量。仿真结果表明,在保证感知性能的前提下,新算法可大幅降低迭代次数,减少算法复杂度。      

采用离散小波变换的认知无线电频谱能量检测

在认知无线电系统中,频谱检测是搜索空闲信道,避免对授权用户产生有害干扰的关键环节。本文提出了一种离散小波变换与时域能量检测相结合的频谱检测方法,对SU共享的宽带信道中的窄带PU信号进行预检测。首先对接收信号进行离散小波变换,获得能够反映信道频谱变化的细节小波系数,然后以该系数作为统计量,对其进行时域能量统计计算。该方法计算量较小,容易实现,可进行多分辨率分析,能够提高检测的灵敏度;不需要被检测信号的先验知识,适用于检测各种未知信号。仿真实验对无线麦克信号和地面无线数字电视信号进行了检测,验证了该方法的正确性。      

An overview of spectrum sensing techniques for cognitive LTE and LTE-A radio systems

Advanced communication systems, such as long term evolution (LTE) and LTE-advanced (LTE-A) systems, promise to increase the number of users with high-speed data exchange. However, it leads to spectrum scarcity because of the huge size of data exchange with limited spectrum resources. Cognitive radio (CR) technique is considered the best solution for this spectrum scarcity problem. Spectrum sensing (SS), one of the CR techniques is used to detect the spectrum hole of primary user (PU) without interference with PU. In this paper, several SS approaches for LTE and LTE-A systems are investigated in the CR system. These SS approaches are based on two techniques, namely energy detection and cyclostationary feature detection techniques. The first technique includes four approaches of auto-correlation based advanced energy, time domain detection, Welch periodogram and two-stage model algorithms, while the second technique contains two approaches, namely pilot induced cyclostationary and second order cyclostationary algorithms. According to the analysis, the two-stage model and the second order cyclostationary algorithms are better than the other algorithms because they produce accurate results at the expense of system complexity. Hence, in general a good SS algorithms would require some trade-off between complexity and accuracy.     

随机共振在认知无线电频谱感知中应用的最新研究进展

频谱感知是认知无线电网络的一项关键技术.低信噪比(SNR)环境下频谱检测的性能会大幅降低,而随机共振(SR)能有效提高信号信噪比,所以将其应用到频谱感知中,能增强认知用户对主用户(PU)的检测性能.首先介绍了随机共振在认知无线电频谱感知中应用的最新研究进展,包括随机共振在本地感知中(如能量检测、协方差矩阵频谱感知、循环平稳特征检测)及协作感知中的应用,然后指出了随机共振在认知无线电频谱感知中还有待解决的问题,并提出了下一步的研究方向.     

一种主用户随机到达情况下改进的循环平稳特征检测算法

在认知无线电(CR)网络中,针对检测频段突然被主用户(PU)占用导致次用户频谱检测性能较差的情况。该文提出一种基于反馈叠加原理的改进循环平稳特征检测算法,该算法通过将检测周期后半部分采样点的瞬时采样值累加到检测周期前半部分采样点的瞬时采样值上,在不延长检测时间的基础上,提高了整个检测周期的判决统计值,从而提高了系统检测性能。并且从理论上详细分析了该算法的检测概率,虚警概率与吞吐量。仿真结果表明,该算法的检测性能优于传统循环平稳特征检测算法和传统能量检测算法,并且保证了不错的用户数据吞吐量。     

L-CR系统中分布式压缩感知最小角回归信号重构

在低轨（LEO）微小卫星感知无线电（L-CR）系统中,多个LEO卫星节点具备一定的频谱感知功能,卫星节点通过分布式组网对地面信关站发射的信息进行感知、传输和处理,地面汇聚节点对LEO卫星节点转发信号进行重构。考虑LEO系统中授权频带的主用户（PU）对卫星认知用户（SU）的干扰,认知用户感知到的信号同时存在PU干扰和噪声,地面汇聚节点通过高效的重构算法进行含噪信号恢复是L-CR系统实现的重要问题。论文研究了L-CR系统中基于分布式压缩感知的信号重构方法。针对L-CR特点,分别分析了汇聚节点在低信噪比情况下采用凸松弛法中的基追踪去噪（BPDN）、同伦（Homotopy）法和最小角回归（Lars）的重构均方误差（MSE）与重构复杂度。研究表明,BPDN具有最小的重构MSE,但其重构复杂度最高。Lars可以有效折衷重构MSE与复杂度。在此基础上,提出了基于分布式压缩感知的最小角回归（DCS-Lars）信号重构方案。仿真结果表明,所提DCS-Lars方法可以在低信噪比情况下有效重构感知信号,并具有良好的频谱检测能力,同时重构复杂度大大降低。      

Sliding window energy detection for spectrum sensing under low SNR conditions

For spectrum sensing, energy detection has the advantages of low complexity, rapid analysis, and requires no knowledge of the transmission signal, which makes it suitable for a wide range of applications. However, under low signal‐to‐noise ratio conditions, the required window length (or the time‐bandwidth product) for energy detection to achieve a desired detection performance is large. In addition, conventional energy detection assumes that the detection tests are independent, that is, there is no overlap between individual detection tests. These properties significantly reduce the detection speed when energy detection is used for the continuous monitoring over a communication channel for the detection of signal transmission activities. In this paper, we propose a sliding window detection analysis with overlap among multiple tests. Algorithms for effective performance analysis of the proposed sliding window energy detection are proposed. The impact of window length on distribution of detection time is investigated. Simulation results on the proposed sliding window energy detection are also compared with the theoretically predicted and conventional energy detection performance estimates. Copyright © 2015 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.     

基于均值辅助的LSTM网络频谱感知算法

针对传统感知算法在低信噪比时检测性能低和深度学习感知算法网络训练量大、复杂度高等问题,本文提出一种在均值辅助下的长短时记忆网络（Long Short-Term Memory,LSTM）频谱感知算法。具体来讲,首先对接收信号序列做多点均值计算,然后利用所得的均值构造特征向量并作为LSTM网络的输入来训练网络,最后利用训练好的网络对新的接收序列进行感知。仿真结果表明:相比于传统算法,所提算法在检测性能上有较大提升;相对于利用原始接收序列直接训练的深度学习算法,所提算法的复杂度大幅下降。      

基于压缩感知的循环平稳特征检测方法

在认知无线电中,传统的循环平稳特征检测技术为了达到理想的感知效果,需要大量的数据采样点,导致其感知过程复杂度大,感知时间长.针对此问题,提出了一种基于压缩感知的改进循环平稳特征检测方法,该算法利用信号循环自相关函数(Cyclic Autocorrelation Function,CAF)的稀疏特性,基于分段平均的时变自相关函数估计值,通过压缩感知技术重构二维CAF矩阵,再根据重构结果实现循环平稳特征检测.该方法不仅可有效降低计算复杂度和检测时间,而且提高了二维CAF的估计精度.仿真结果表明该方法的检测性能优于基于经典CAF估计的循环平稳特征检测技术.     

Optimization algorithm of periodical cooperative spectrum sensing in cognitive radio

To decrease the interference to the primary user (PU) and improve the detected performance of cognitive radio (CR), a single‐band sensing scheme wherein the CR periodically senses the PU by cooperative spectrum sensing is proposed in this paper. In this scheme, CR first senses and then transmits during each period, and after the presence of the PU is detected, CR has to vacate to search another idle channel. The joint optimization algorithm based on the double optimization is proposed to optimize the periodical cooperative spectrum sensing scheme. The maximal throughput and minimal search time can be respectively obtained through the joint optimization of the local sensing time and the number of the cooperative CRs. We also extend this scheme to the periodical wideband cooperative spectrum sensing, and the joint optimization algorithm of the numbers of the sensing time slots and cooperative CRs is also proposed to obtain the maximal throughput of CR. The simulation shows that the proposed algorithm has lower computational quantity, and compared with the previous algorithms, when SNR = 5 dB, the throughput and search time of the proposed algorithm can respectively improve 0.3 kB and decrease 0.4 s. The simulation also indicates that the wideband cooperative spectrum sensing can achieve higher throughput than the single‐band cooperative spectrum sensing. Copyright © 2012 John Wiley & Sons, Ltd.     

Temporal and spatial outlier detection in wireless sensor networks

Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph‐based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state‐of‐the‐art methods in terms of accuracy and work well with various data types.     

一种基于特征值和级联聚类的协作频谱感知方法

为了提高低信噪比(Signal-to-Noise Ratio, SNR)下频谱感知的性能，使用模糊C均值(Fuzzy C-means, FCM)和高斯混合模型(Gaussian Mixture Model, GMM),提出了一种基于特征值和级联聚类的协作频谱感知方法。从接收信号的协方差矩阵中提取特征值构造特征向量，通过在三维空间中执行聚类得到信道是否可用的分类模型，此过程无需获得主用户(Primary User, PU)信号以及噪声功率的先验信息，避免了复杂的门限计算。FCM聚类用于优化GMM聚类的初始参数，有效解决了在低SNR下GMM容易陷入局部最小值的问题。仿真结果表明，该方法降低了GMM的收敛时间并提高了模型分类的准确性，与其他主流方法相比能够有效提升频谱感知的性能。