基于认知无线电的煤矿井下频谱感知技术的研究

对认知无线电的频谱感知技术进行分析,同时结合煤矿巷道环境特点提出煤矿巷道认知无线电感知模型.提出的单节点采用能量检测和循环平稳特征检测联合算法,既弥补了能量检测在低信噪比下检测概率低的缺点,又改善了循环平稳特征检测需要较长时间的问题.     

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。     

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D-S证据理论的循环平稳频谱协作感知

为了避免干扰授权用户的正常通信,认知无线电系统需要较好的频谱检测性能。提出了基于循环平稳特征和D-S证据理论的多天线多用户频谱协作感知方法。分析了多天线接收信号的谱相关函数,在频域按照最大比合并的方法合并各天线接收信号,得到合并信号的谱相关函数。依据谱相关函数的能量,在多用户中采用D-S证据理论做协作频谱检测。仿真分析表明,基于循环平稳特征和D-S证据理论的多天线多用户频谱协作感知方法可有效改善认知无线电系统检测性能。     

一种基于能量检测的协作感知技术的改进

认知无线电技术是解决无线资源紧缺的有效方法.基于非协作感知的不确定性,协作频谱感知方法具有更高的准确度,因此被人们所关注.文中提出了一种利用协作式能量检测的方法,通过利用近似计算将检测时间缩短,提高检测效率.经仿真结果证实,该方法能有效提高检测结果的准确性.     

基于循环平稳特征的频谱感知算法概述

传统的静态频谱资源分配分配政策导致频谱利用率低下,为解决这一问题,人们提出利用认知无线电技术实现动态频谱接入.频谱感知是认知无线电的关键技术,循环平稳特征检测算法是3种常见频谱检测算法之一,但是该算法存在各种不足.首先简要介绍认知无线电的背景和概念,然后详细介绍了循环平稳特征检测算法,以及目前提出的各种基于循环平稳特征检测的增强算法,分析了各自的原理及其优缺点.     

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.     

频谱感知技术在短波通信中的应用

为了提高短波通信的频谱利用率,需要对短波频谱进行实时准确的频谱感知.针对短波信道特点,对几种频谱感知方法能否适用于短波通信系统进行了分析.结合国内外军用认知无线电的发展,并充分利用能量检测速度快、循环谱特征检测精度高的优点,提出了一种能量和循环谱特征联合检测的方法,并给出了基于感知技术的短波电台的基本架构,为缓解短波信道资源紧张提供了一条途径.     

频谱空穴检测及其仿真

感知无线电接收机为了感知其周围的无线电环境,首要的工作就是对其周围存在的信号进行检测。本文针对感知无线电系统中最为关键的频谱空穴检测技术进行了研究。通过对3种信号检测技术的比较,选择循环平稳特征检测法作为感知无线频谱的方法并对该方法进行了仿真。此外．对控制实体根据频谱感知的结果、从频谱中检测出频谱空穴区域的功能、以及感知无线电接收机与感知无线电发射机进行协调的功能也进行了仿真。     

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。     

认知无线电中双阶段频谱检测算法研究

在认知无线电网络中,为了实现更精确的检测,并避免对授权用户的干扰,一种双阶段频谱检测算法被提出.该算法由粗检测和细检测两种方式组成.粗检测阶段采用应用广泛的能量检测技术.由于能量检测技术的性能在衰落环境中容易受到影响,当粗检测阶段时感知结果判定为信道空闲,在细检测阶段将采用基于一阶周期平稳特征检测算法.对于这种双阶段频谱检测算法,推导分析了错误检测概率和吞吐量的性能指标.仿真结果表明,频谱检测性能显著优于常规的一阶段频谱检测算法.     

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.     

Performance analysis of spectrum sensing techniques over TWDP fading channels for CR based IoTs

In this paper, the performance of spectrum sensing techniques over two wave diffused power (TWDP) fading channels has been investigated for CR based IoT devices. The ubiquitous objects based on IoTs with cognitive capabilities are the future that would enable intelligent decisions to achieve any time, any place, interference-free and on-demand services. In this work, new closed-form expressions in terms of Marcum-Q function and Whittaker function for the spectrum detection probability over TWDP fading channels have been derived for different sensing techniques. The expressions are then used to optimize the decision threshold for IoT sensor nodes describing the optimal behavior of sensing over TWDP channels. The error probabability and Receiver Operating Characteristic (ROC) curves have been plotted and the operating point and limiting values of threshold for optimal performance have been identified. The performance loss of traditional energy detection (ED), cyclostationary based detection (CSD) and matched filtering detector (MFD) has been analyzed. The analytical results thus obtained are validated through monte-carlo simulations.     

Cyclostationarity-based cooperative spectrum sensing over imperfect reporting channels

Reliable detection of weak primary user signals is a crucial problem for cognitive radio networks. To address the above issue, cooperative spectrum sensing (CSS) methods based on cyclostationary detection (CD) have been introduced in the literature. In this paper, a soft decision-based CSS method based on the second-order CD at secondary users (SUs) is proposed. The proposed scheme aims to maximize the deflection criterion at the fusion center (FC), while the reporting channels are characterized by Rayleigh fading. To this end, a fusion rule which does not require to know the noise variances of sensing channels is developed. Since the fusion rule assumes the perfect knowledge of channel state information (CSI) of reporting links, it has theoretical significance and provides an upper bound for the performance of cyclostationarity-based CSS. We have also proposed a more practical suboptimum fusion rule and studied its detection performance in the presence of uncertainties in noise variance and channel power gain estimations. Furthermore, in order to be able to evaluate the performance of the CSS, an analytic threshold estimation method has been proposed. Extensive simulation results have been illustrated the robustness of the proposed method compared to the existing cyclostationary detectors.     

低信噪比下周期平稳信号的稳健检测算法

本文提出了一种新的对周期平稳信号进行检测以及对二阶周期循环频率进行估计的算法。该算法利用信号的递归性质构造高阶自相关矩阵,并通过利用周期平稳信号与自相关矩阵特征值和特征向量的关系,对其进行检测以及对循环频率进行估计。传统检测周期平稳信号的算法是通过计算其循环自相关函数或循环谱实现,相比传统算法而言,本算法由于利用到了信号更多的先验信息,因而在较低信噪比以及较低快拍数下对周期平稳信号均能有较好的检测性能。文中仿真实验表明,本文所提算法估计出的伪循环谱相比传统方法估计出的循环谱更为平滑,在相同快拍和信噪比条件下,检测概率均高于传统方法,特别在低信噪比下对检测概率的改善更为明显。      

DS-CDMA中扩频信号的循环平稳特性分析

介绍了循环平稳信号的基本概念，对DS—CDMA系统中直接序列扩频信号的循环平稳特性进行了详细的理论分析，通过计算机仿真实验验证了分析结果；仿真同时说明了利用信号的循环平稳特性可以进行有效地信号提取和干扰抑制，具有极大的应用前景。     

Detecting anomalous spectrum usage in dynamic spectrum access networks

Dynamic spectrum access has been proposed as a means to share scarce radio resources, and requires devices to follow protocols that access spectrum resources in a proper, disciplined manner. For a cognitive radio network to achieve this goal, spectrum policies and the ability to enforce them are necessary. Detection of an unauthorized (anomalous) usage is one of the critical issues in spectrum etiquette enforcement. In this paper, we present a network structure for dynamic spectrum access and formulate the anomalous usage detection problem using statistical significance testing. The detection problem investigated considers two cases, namely, the authorized (primary) transmitter is (i) mobile and (ii) fixed. We propose a detection scheme for each case by exploiting the spatial pattern of received signal energy across a network of sensors. Analytical models are formulated when the distribution of the energy measurements is given and, due to the intractability of the general problem, we present an algorithm using machine learning techniques to solve the general case when the statistics of the energy measurements are unknown. Our simulation results show that our approaches can effectively detect unauthorized spectrum usage with a detection probability above 0.9 while keeping the false alarm rate less than 0.1 when only one unauthorized radio is present, and the detection probability is even higher for more unauthorized radios.