Collaborative Spectrum Sensing for Illegal Drone Detection: A Deep Learning-Based Image Classification Perspective

Drones,also known as mini-unmanned aerial vehicles(UAVs),are enjoying great popularity in recent years due to their advantages of low cost,easy to pilot and small size,which also makes them hard to detect.They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security.In this article,we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty.First,we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem.Then,we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image.Furthermore,to exploit more information and improve the detection performance,we develop a trajectory classification algorithm which converts theflight process of the drones in consecutive multiple sensing slots into trajectory images.In addition,simulations are provided to verify the proposed methods’performance under various parameter configurations.

A COLLABORATIVE SPECTRUM SENSING SCHEME USING FUZZY COMPREHENSIVE EVALUATION IN COGNITIVE RADIO

Cognitive Radio(CR) is a promising technology to solve the challenging spectrum scarcity problem.However, to implement CR, spectrum sensing is the groundwork and the precondition.In this paper, a collaborative spectrum sensing scheme using fuzzy comprehensive evaluation is proposed.The final sensing decision of the proposed scheme is based on the combination of distributed sensing results of different Secondary Users(SUs).To improve the reliability of the sensing decision, the combination procedure takes into account the credibility of each SU, which is evaluated using fuzzy comprehensive evaluation.The effect of the presence of malicious SUs and malfunctioning SUs on the performance of the proposed scheme is also investigated.The efficiency of the scheme is validated through analysis and simulation.

A SPARSITY AND COMPRESSION RATIO JOINT ADJUSTMENT METHOD FOR COLLABORATIVE SPECTRUM SENSING

Spectrum sensing is the fundamental task for Cognitive Radio (CR). To overcome the challenge of high sampling rate in traditional spectral estimation methods, Compressed Sensing (CS) theory is developed. A sparsity and compression ratio joint adjustment algorithm for compressed spectrum sensing in CR network is investigated, with the hypothesis that the sparsity level is unknown as priori knowledge at CR terminals. As perfect spectrum reconstruction is not necessarily required during spectrum detection process, the proposed algorithm only performs a rough estimate of sparsity level. Meanwhile, in order to further reduce the sensing measurement, different compression ratios for CR terminals with varying Signal-to-Noise Ratio (SNR) are considered. The proposed algorithm, which optimizes the compression ratio as well as the estimated sparsity level, can greatly reduce the sensing measurement without degrading the detection performance. It also requires less steps of iteration for convergence. Corroborating simulation results are presented to testify the effectiveness of the proposed algorithm for collaborative spectrum sensing.

A Diffusion-Based Distributed Collaborative Energy Detection Algorithm for Spectrum Sensing in Cognitive Radio

Spectrum sensing is one of the most important steps in cognitive radio. In this paper, a new fully-distributed collaborative energy detection algorithm based on diffusion cooperation scheme and consensus filtering theory is proposed, which doesn’t need the center node to fuse the detection results of all users. The secondary users only exchange information with their neighbors to obtain the detection data, and then make the corresponding decisions independently according to the pre-defined threshold. Simulations show that the proposed algorithm is more superior to the existing centralized collaborative energy detection algorithm in terms of the detecting performance and robustness in the insecurity situation.

Collaborative Spectrum Sensing for Cognitive Radio: Diversity Combining Approach

In this paper it is shown that cyclostationary spectrum sensing for Cognitive Radio networks, applying multiple cyclic frequencies for single user detection can be interpreted (with some assumptions) in terms of optimal incoherent diversity addition for “virtual diversity branches” or SIMO radar. This approach allows proposing, by analogy to diversity combining, suboptimal algorithms which can provide near optimal characteristics for the Neyman-Pearson Test (NPT) for single user detection. The analysis is based on the Generalized Gaussian (Klovsky-Middleton) Channel Model, which allows obtaining the NPT noise immunity characteristics: probability of misdetection error (PM) and probability of false alarm (Pfa) or Receiver Operational Characteristics (ROC) in the most general way. Some quasi-optimum algorithms such as energetic receiver and selection addition algorithm are analyzed and their comparison with the noise immunity properties (ROC) of the optimum approach is provided as well. Finally, the diversity combining approach is applied for the collaborative spectrum sensing and censoring. It is shown how the diversity addition principles are applied for distributed detection algorithms, called hereafter as SIMO radar or distributed SIMO radar, implementing Majority Addition (MA) approach and Weighted Majority Addition (WMA) principle.

OPTIMIZATION OF MULTIPLE-CHANNEL COOPERATIVE SPECTRUM SENSING WITH DATA FUSION RULE IN COGNITIVE RADIO NETWORKS

This paper focuses on multi-channel Cooperative Spectrum Sensing (CSS) where Secondary Users (SUs) are assigned to cooperatively sense multiple channels simultaneously. A multi-channel CSS optimization problem of joint spectrum sensing and SU assignment based on data fusion rule is formulated, which maximizes the total throughput of the Cognitive Radio Network (CRN) subject to the constraints of probabilities of detection and false alarm. To address the optimization problem, a Branch and Bound (BnB) algorithm and a greedy algorithm are proposed to obtain the optimal solutions. Simulation results are presented to demonstrate the effectiveness of our proposed algorithms and show that the throughput improvement is achieved through the joint design. It is also shown that the greedy algorithm with a low complexity achieves the comparable performance to the exhaustive algorithm.

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Wireless Communication is a system for communicating information from one point to other,without utilizing any connections like wire,cable,or other physical medium.Cognitive Radio(CR)based systems and networks are a revolutionary new perception in wireless communications.Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum.Centralized Cooperative Spectrum Sensing(CSS)is a kind of spectrum sensing.Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix(SCM)of the received signal.Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector(PRIDe),Hadamard Ratio(HR)detector,Gini Index Detector(GID),etc.This paper presents the simulation and comparative perfor-mance analysis of PRIDe with various other detectors like GID,HR,Arithmetic to Geometric Mean(AGM),Volume-based Detector number 1(VD1),Maximum-to-Minimum Eigenvalue Detection(MMED),and Generalized Likelihood Ratio Test(GLRT)using the MATLAB software.The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

Reputation-Based Collaborative Spectrum Sensing Scheme in Cognitive Radio Networks

Collaborative spectrum sensing is proposed to improve the detection performance in cognitive radio (CR) networks. However, most of the current collaborative sensing schemes are vulnerable to the interference of the malicious secondary users (SUs). In this paper we propose a reputation-based collaborative spectrum sensing scheme to improve the security of cooperative sensing by mitigating the impacts of misbehaviors. The fusion center calculates the reputation rating of each SU according to their history reports to weight their sensing results in the proposed scheme. We analyze and evaluate the performance of the proposed scheme and its advantages over previous schemes in expansibility and integrity. Simulation results show that the proposed scheme can minimize the harmful influence from malicious SUs.

基于能量检测的用户选择协作频谱感知

协作频谱感知(collaborative spectrum sensing,CSS)检测结果的上报尽管能够改善检测性能,但是也可能导致认知用户(secondary users,SU)产生较高的开销,同时引起对授权用户(primary users,PU)的干扰。基于双门限能量检测的用户选择CSS策略,称为双门限最佳选择上报(double threshold best selection reporting,DTBSR)策略,意在减少SU的平均检测时间和提高频谱感知的性能。单门限能量检测在受到噪声干扰后性能会严重下降,采用了基于最大比合并(maximum ratio combining,MRC)的双门限能量检测算法,通过考虑信息上报过程对PU产生的干扰,设计了在瑞利衰落信道下DTBSR策略与传统策略的虚警概率和检测概率表达式。仿真结果表明,与传统策略和最优选择上报策略相比,改进的策略能够提高检测性能并减少平均检测时间,并且还可以通过调节感知时间获得最小化漏检概率。

一种认知无线电的联合频谱检测算法

为了改善信噪比不同的认知无线电(cognitive radio)用户的联合频谱检测性能,提出了一种基于能量检测的两个用户合作频谱检测算法。在联合虚警概率一定的条件下,根据接收信号能量对各个用户的判决门限进行调整,增大高信噪比用户对联合检测的影响,同时减小低信噪比用户的影响,来提高联合检测概率;判决结果只占用少量比特的信息,减少了通信开销。实验结果表明,所提算法很好地逼近了搜索解,提高了联合检测性能。

针对认知无线网络大规模入侵的双重防御方案

协作频谱感知(cooperative spectrum sensing,CSS)可有效提高频谱检测的准确性,然而,当次用户中恶意用户占比较大且同时发动大规模攻击时,将严重影响CSS的准确性。文中将用户选择和置信传播有机结合,提出一种针对大规模入侵的双重防御方案。基于本地感知结果通过用户选择机制挑选可靠次用户,挑选出的可靠次用户包括诚实用户和伪装成诚实用户的恶意用户。然后以可信度为权重计算置信值均值并将其和预设门限比较,进一步滤除伪装成诚实用户的恶意用户。仿真结果表明,与已有的简单的基于信誉的方案、基于均值的方案、基于克隆节点的攻击检测算法相比,所提方案具有更好的检测性能,且置信值收敛更快。

基于最大比合并的循环平稳频谱协作检测

多天线空间分集和多用户协作检测是提高认知无线电频谱检测性能的2种关键技术,由此提出了基于循环平稳特征的多天线多用户频谱协作检测方法。首先接收器在频域按照最大比合并的方法合并各天线接收信号,对合并信号进行初步频谱判决。然后在多用户中按照K/N融合准则进一步做频谱协作检测。仿真分析表明,综合利用信号循环平稳特征和协作检测技术可有效改善认知无线电系统检测性能。

频谱感知协作检测方法仿真研究

在认知无线电系统中,认知无线电用户需要快速准确地检测出主用户及其闲置的频带。本文就一种利用两个认知用户协作感知频谱的检测方法进行仿真研究,结果表明,它可以减少检测时间和增加检测的灵敏度。

一种基于跨层设计的双次协同频谱感知技术

频谱感知的性能不仅依赖于物理层的感知方法,也取决于媒体接入层的协作调度.针对无线电磁环境非常恶劣的多用户认知网络,提出双次协同频谱感知模型和适应此新模型动态变化特性的动态可变时分多址调度机制以进行跨层协作,推导出了平均感知时间的闭式表达式,给出了实现吞吐量最大化的优化方法,分析了该协作可实现的条件.仿真结果表明:通过对各层特性参数的联合优化设置,相比于单次协同感知,所提协同感知方法及其优化算法可有效提高感知的准确性、时效性及网络吞吐量.

报告信道传输错误环境下协作感知最优用户数分析

认知无线电网络中多个用户相互协作进行频谱感知能有效提高系统的感知性能,然而当各用户将本地感知信息经无线信道发送至中心控制器时,若因信道衰减等因素出现传输错误又会降低系统性能。该文针对这种网络环境,探索了参与协作的用户数目与系统感知性能的关系,并从授权/认知用户两个角度分别讨论协作感知最优用户数的计算方法。理论分析与仿真结果表明:网络中所有用户均参与协作不一定能获取最优性能;当选取具有较高感知信噪比条件的一定数目的用户进行协作反倒能实现最优,并且该最优用户数与网络的规模成正比,与报告信道传输错误概率成反比。

能量和协方差检验的联合频谱感知算法研究

快速准确的频谱感知是认知无线电系统有效通信的前提。现有的能量检测算法易受噪声波动影响,为提高频谱感知的性能,采用了联合频谱感知算法。方法利用双门限的能量检测法进行粗检,并使用协方差检测算法对中间混淆区域进行二次判决。在噪声不确定的情况下,对联合频谱感知算法性能进行了仿真分析。仿真结果表明,在噪声波动较大时,算法有效地提高频谱检测的性能,而且计算复杂度得到降低,并优于能量检测法和协方差检测法。

基于次用户功率控制辅助的合作频谱感知

传统的合作频谱感知一般将感知环境建模为单级信道,且次用户一般都以相同的发射功率向数据融合中心报告感知数据,难以体现并利用不同次用户感知数据之间的空间分集差异。为解决此问题并有效地设置次用户在感知数据上报阶段的发射功率,该文提出了3种最优功率控制方案,以获得相应设计准则下参与合作感知的次用户最优发射功率。在融合中心理想具备感知信道和报告信道的统计特性时,通过理论推导获得了基于信道统计特性的功率控制闭式解方案;当信道统计特性难以现实具备时,分别获得了基于联合信道统计特性估计的最大特征功率矢量及盲加权多特征功率矢量方案。理论分析和仿真实验表明,在不同的先验信息条件下,3种方案的性能皆远优于缺少功率控制的合作感知方案。

基于信噪比权值的累量循环平稳检测协作感知

累量循环平稳检测算法可以克服传统能量检测算法对噪声估计误差的敏感性,协作感知可以克服单用户检测中的阴影效应等问题,不同的判决融合方法对协作感知系统的性能有较大影响。提出在协作频谱感知中采用累量循环平稳算法,并利用Chair-Varshney准则进行判决融合。仿真实验表明,与硬判决融合方法相比,该方案可以在不增加融合中心数据量的情况下有效地调高系统的频谱检测性能,性能的提高程度与协作用户的信噪比分布情况有关。

重加权分布式多目标决策融合算法

在认知无线电网络中,单一子频段检测信息融合效率低,且融合过程中权值系数为固定值不能实现最优化。为解决该问题,提出一种重加权分布式多目标决策融合算法。该算法并行检测多个子频段,将自适应的稀疏权值矩阵运用在分布式决策融合算法中,利用最速下降法对优化问题进行求解,并结合用户与信道信息选取最佳的合作用户及数量。实验结果表明,该算法在低信噪比环境下的检测概率和稳定性能都得到较大提高。

无线认知传感器网络数据伪造攻击博弈研究

博弈论是无线认知传感器网络的重要分析工具。数据伪造攻击是无线认知传感器网络协同频谱感知面临的一种安全威胁,即恶意用户通过向其他用户发送伪造的频谱感知数据,导致汇聚节点做出错误的频谱分配决策。文章列举了无线认知传感器网络数据伪造攻击可能存在的几种场景,给出了协同频谱感知中存在两个次用户的博弈模型,定义了数据伪造攻击中的博弈元素,分析了数据伪造攻击博弈中的贝叶斯均衡,最后给出了数值结果和最优策略。