单天线认知无线电中的协作频谱感知和无线能量传输

在单天线认知无线电中,可以通过天线协作频谱检测来提高检测衰落信道中主用户(Primary User,PU)存在的传感性能。然而,由于协作频谱检测,认知无线电(Cognitive Radio,CR)可能消耗更多的存储能量,从而降低其传输性能。为了保证其传输性能,提出了一种同步协作频谱检测和无线功率传输的方案,该方案可以收集PU信号的射频(Radio Frequency,RF)能量,以提供频谱检测消耗的能量。提出了时间分流模型、实现了协作频谱检测,能量收集和数据传输的同步。制定了适用于该方案的优化问题,以最大化同步协作频谱检测和无线功率传输模型中认知无线电的频谱效率,分别受限于检测的概率和收集的能量。最后通过计算得到的最佳分配因子,以实现了频谱效率的最大值,并通过仿真得出了结论。     

Proposed Approaches for Cooperative Cognitive Radio

This paper in this topic concentrates on an important part is spectrum sensing (SS). It can detect the idle hole in spectrum by detection methods. This paper uses the sensing technique is called energy detector(ED). The ED depends on only the energy of the signal without other needs such as the modulation of signal or pre-knowledge about the signal and this is considered as advantage. This research proposed new two techniques are the additive wavelet transform (AWT) with Homomorphic Way (HW) and Haar Discrete Wavelet Transform (HDWT) approach. We apply these techniques are applied in wide band wireless signal by using the Cognitive Radio (CR) network. Each technique reduces the noise of signal before enter to the detection method ED. The HW is considered new technique in the wireless communication. This study will have these techniques as hybrid with the ED to increase the throughput for the cognitive user with a sufficient protection to the PU transmission. Also, it improves the probability of detection and reduces the probability of false alarm and the probability of error. The cooperative CR is used in this work which more than the non-cooperative cognitive user to detect the holes. The final decision for detection built on four fusion rules are the logic OR, logic AND, MAJORITY and K-Out-Of-M fusion rule. The two proposed are applied techniques on four fusion rule at constant sensing time. Then; study the four metric detection performances for each fusion rule by using the Additive White Gaussian Noise (AWGN) channel. At the end, comparison between two these proposed techniques with each fusion rule. Simulation results prove that the proposed scenario increases the probability of detection in the range of SNR of the PU from ?20 to ?5 dB using the theses proposed approaches.

     

Soft decision cooperative spectrum sensing with noise uncertainty reduction

Cognitive radio (CR) is a potential strategy for opportunistic access of idle resources to solve the conflicts between spectrum scarcity and underutilization. Spectrum sensing (SS) constitutes the most critical part in CR systems since the CR needs to detect the presence of primary signals reliably and quickly. Energy detection (ED) based SS is considered as the most preferable SS technique due to its simplicity and applicability. However, it is influenced by the effect of noise uncertainty which highly degrades its sensing performance. Cooperative spectrum sensing (CSS) is also introduced to mitigate some sensing problems such as multipath fading, shadowing, and hidden node problems. In this paper, we propose an enhanced fusion center (FC) rule for soft decision CSS using ED, which highly alleviates the noise uncertainty effect and enhances the sensing performance of cognitive radio networks (CRNs). In the proposed fusion rule, to increase the probability of detection and decrease the probability of false alarm, two dynamic thresholds are utilized by the FC. These thresholds are toggled based on predicting the current activity of the primary user (PU), and their values are dynamically changed based on estimating the noise uncertainty factor of the collected energy measurements from the CRs. To effectively predict (estimate) the current PU activity (the noise uncertainty factor), simple successive averaging processes over the collected energy measurements (the estimated noise variances) are performed by the FC, respectively. Theoretical analysis is performed on the proposed fusion rule for soft decision CSS to evaluate its enhanced false alarm and detection probabilities using different data combining schemes. Performance evaluations are also investigated to confirm the theoretical claims and to prove the effectiveness of the proposed scheme over the conventional ED based soft decision CSS.     

认知无线电系统多接入用户信息新鲜度研究

为了解决物联网信道资源有限的问题以及提高物联网系统的信息时效性,考虑了包括一个主用户(primary user, PU)和两个次用户(secondary user, SU)节点的多接入认知无线电(CR)物联网系统模型。在PU工作状态和SU数据队列稳定的约束下,分别分析了第一个SU节点在先来先服务(first come first served, FCFS)、后来先服务(last come last served, LCLS)以及包丢弃队列下的平均信息年龄(age of information, AoI),推导了在阈值策略下第二个SU节点的平均AoI。然后,提出了使第一个SU平均AoI最小化,并且第二个SU的平均AoI低于给定阈值的优化问题。优化问题的约束条件是凸的,但所得到的目标函数是非凸的,故引入了一种次优技术,利用双层凸优化算法得到最优解。仿真结果给出了所考虑优化算法在不同系统参数下的性能,该算法在不同系统参数和多天线影响下的性能表现良好。后续工作可以考虑扩展到两个以上次用户的CR物联网系统。     

基于M&S模型的分布式认知无线电网络时间同步机制

针对分布式认知无线电网络(DCRN)中节点时间同步的问题,提出一种基于M&S同步模型的节点协作式时间同步机制。首先,选定的主用户(PU)通过控制信道将其空闲频谱列表广播到邻居节点,以确定节点对之间的可用公共信道;然后,邻居节点以该PU节点为参考,在公共信道上发送同步信息,并通过M&S同步模型调整内部计时器,从而实现时钟同步;经过多次迭代,最终实现全网时间同步。实验结果表明,该方案能够实现DCRN的时间同步,同时具有较快的收敛速度和较低的开销。     

多用户MIMO系统低复杂度收发联合波束成形方案

基于多用户多输入多输出(MIMO)有限反馈系统提出了一种收发联合波束成形方法。该方法在发送端采用每个用户酉速率控制(PU2RC)波束成形技术,在接收端采用基于信干噪比(SINR)最大化的天线合并算法。天线合并算法利用用户SINR的广义Rayleigh熵特性寻找最佳的天线合并矢量。仿真结果表明,该方法能充分利用信道信息,不仅改善了传统PU2RC系统的和速率性能,相对于已有的迫零(ZF)联合波束成形方法,对信道状态信息的量化误差的敏感性更低,用户选择算法的复杂度更低,且低速率反馈条件下和速率性能更优。     

Cooperative spectrum sensing based on stochastic resonance in cognitive radio networks

A new censoring cooperative spectrum sensing scheme based on stochastic resonance(SR)technique in cognitive radio(CR)network is proposed in this paper.The observations of the cooperative secondary users(SUs)whose statistics fall into the censoring interval are processed by SR system in the proposed scheme.The hard fusion and the soft fusion for the censoring cooperative spectrum sensing scheme are analyzed respectively.Theoretical analyses and simulation results show that the proposed censoring cooperative spectrum sensing scheme has the same detection performance as and lower computational complexity than the method that each cooperative SU performs spectrum sensing using SR-based energy detection,and its detection performance is superior to that of the conventional method that all the cooperative SUs perform spectrum sensing using energy detection in hard fusion.In soft fusion,the proposed censoring cooperative spectrum sensing based on equal gain combination can achieve the optimal sensing performance approximately.     

LACAV: an energy-efficient channel assignment mechanism for vehicular ad hoc networks

Designing an efficient channel assignment system for Vehicular Ad hoc Networks (VANETs), which conserves energy, is a challenging task, primarily because of the high degrees of mobility of nodes in these networks. As the high mobility of nodes in vehicular networks leads to frequent handoffs, channel assignment in VANETs becomes a tedious task. In this paper, we propose a channel assignment mechanism using the concepts of learning automata (LA) and reusability. LA is used to optimize the performance of the proposed system by selecting suitable number of reserved channels for the handoff calls and reusability allows the channel to be reused by the different base stations (BSs) based on the reuse distance. The proposed system is designed to reduce the dropping probability. The proposed system is suitable for network architectures in which it is possible to arrange the BSs with different groups of channels sequentially in a particular order that helps in conserving energy. Our experiments clearly indicate that the system reduces the dropping probability and allows a continuous communication throughout the duration of the call. The performance of proposed algorithm is compared with the Vehicular Fast Handover Scheme (VFHS), and the Cooperative scheme for service channel reservation (CRaSCH) scheme in terms of handoff latency, and it is shown that the proposed algorithm performs better than VFHS and CRaSCH.     

On detecting primary user emulation attack using channel impulse response in the cognitive radio network

Cognitive radio is an effective technology to alleviate the spectrum resource scarcity problem by opportunistically allocating the spare spectrum to unauthorized users. However, a serious denial-of-service (DoS) attack, named the ‘primary user emulation attack (PUEA)’, exists in the network to deteriorate the system performance. In this paper, we propose a PUEA detection method that exploits the radio channel information to detect the PUEA in the cognitive radio network. In the proposed method, the uniqueness of the channel impulse response (CIR) between the secondary user (SU) and the signal source is used to determine whether the received signal is transmitted by the primary user (PU) or the primary user emulator (PUE). The closed-form expressions for the false-alarm probability and the detection probability of the proposed PUEA detection method are derived. In addition, a modified subspace-based blind channel estimation method is presented to estimate the CIR, in order for the proposed PUEA detection method to work in the scenario where the SU has no prior knowledge about the structure and content of the PU signal. Numerical results show that the proposed PUEA detection method performs well although the difference in channel characteristics between the PU and PUE is small.     

一种面向工控系统的PU学习入侵检测方法

工业控制系统与物理环境联系紧密,受到攻击会直接造成经济损失,人员伤亡等后果,工业控制系统入侵检测可以提供有效的安全防护。工业控制系统中将入侵检测作为一个异常检测问题,本文围绕PU learning （Positive-unlabeled learning,PU学习）进行工业控制系统入侵检测进行研究。首先针对工业控制系统中数据维度高的特点,提出了一种特征重要度计算方法,通过正例数据集和无标签数据集的分布差异度量特征重要度,用于PU学习的特征选择;其次提出了一种基于OCSVM （One-Class SVM）的类先验估计算法,该算法可以稳定且准确的估计出类先验概率,为PU学习提供必要的先验知识;最后采用了三个公开数据集进行实验,在仅有一类标签数据的条件下,通过PU学习发现待检测数据中的异常样本,并与一些现有的模型进行对比,验证了PU学习的有效性。     

一种基于子空间分解的认知MIMO传输机制

针对传统认知无线电中检测-避免机制无法保证认知用户通信质量的问题,利用多输入多输出(MIMO)在空域处理上的优势,提出一种基于子空间分解的认知MIMO传输机制。在信道检测阶段,通过对协方差矩阵进行子空间分解得到噪声空间,在数据传输阶段,结合预编码将发送信号投影到噪声空间上,并采用功率注水以优化系统性能。仿真结果表明,该算法可以实现认知用户与主用户的空间复用,有效地提高系统的频谱利用率。     

Search: A routing protocol for mobile cognitive radio ad-hoc networks

Recent research in the emerging field of cognitive radio (CR) has mainly focussed on spectrum sensing and sharing, that allow an opportunistic use of the vacant portions of the licensed frequency bands by the CR users. Efficiently leveraging this node level channel information in order to provide timely end-to-end delivery over the network is a key concern for CR based routing protocols. In addition, the primary users (PUs) of the licensed band affect the channels to varying extents, depending on the proportion of the transmission power that gets leaked into the adjacent channels. This also affects the geographical region, in which, the channel is rendered unusable for the CR users. In this paper, a geographic forwarding based SpEctrum Aware Routing protocol for Cognitive ad-Hoc networks (SEARCH), is proposed that (i) jointly undertakes path and channel selection to avoid regions of PU activity during route formation, (ii) adapts to the newly discovered and lost spectrum opportunity during route operation, and (iii) considers various cases of node mobility in a distributed environment by predictive Kalman filtering. Specifically, the optimal paths found by geographic forwarding on each channel are combined at the destination with an aim to minimize the hop count. By binding the route to regions found free of PU activity, rather than particular CR users, the effect of the PU activity is mitigated. To the best of our knowledge, SEARCH takes the first steps towards a completely decentralized, CR routing protocol for mobile ad-hoc networks and our approach is thoroughly evaluated through analytical formulations and simulation study.     

Cognitive radio inspired NOMA with SWIPT for green multicasting in next generation wireless networks

This paper investigates an application of simultaneous wireless information and power transfer (SWIPT) in a cognitive radio (CR) inspired non-orthogonal multiple access (NOMA) framework with an aim to support simultaneous multicast services of delay sensitive traffic to a high priority multicast group and a low priority multicast group, modeled as primary user (PU) and secondary user (SU) respectively. A cooperative NOMA protocol is proposed where the nodes in the SU multicast group (SU-MG), located in between a common base station (BS) and the PU multicast group (PU-MG), act as relays. As an incentive, a provision is made such that the BS could support exclusive downlink transmission to the SU-MG. In view of the challenge of supporting sustainable network operation with their limited battery power, the SU relays are equipped with requisite harvesting hardware to harvest energy from the NOMA signal transmitted by the BS, using the principle of SWIPT. Closed form expressions of outage probabilities of PU-MG and SU-MG and their approximations under high SNR regime are derived to characterize the performance of the proposed protocol. Furthermore, allocation of exclusive time interval to SU-MG and power sharing of NOMA signal are optimized jointly with an aim to minimize the outage probability of SU-MG under the outage constraint of PU-MG. Simulation results are presented to validate the accuracy of the analytical expressions. Results highlight that proposed scheme performs much better in terms of energy efficiency of PU-MG as compared to that of traditional orthogonal multiple access and non-cooperative NOMA, both without SWIPT.     

混合模式下认知无线电网络的能效优化算法

认知无线电网络中,协作频谱感知利用多个节点同时感知可提高频谱感知检测性能。然而随着感知的次用户(SU)个数增加,导致能耗增高、能效(EE)降低。为解决这一问题,本文结合机会频谱接入和衬垫式频谱共享2种共享模式,构造基于混合频谱共享模式的能效模型,同时考虑3种不同的融合规则、主用户(PU)的再占据概率和报告信道误差,以最大化SU系统的EE为目标,使用拉格朗日乘子法与次梯度下降算法对感知时间、参与感知个数、次用户发射功率进行迭代优化求解。仿真结果表明,在最低服务质量要求(QoS)和发射功率的约束下,该能效优化算法能够实现更高的吞吐量和更高的能量效率。     

基于辅助节点的安全协作频谱感知

在认知无线电网络中,恶意用户会占用授权频段,模仿主用户的信号特征发送信号,导致其他认知用户误认为主用户存在。针对该问题,提出一种基于辅助节点的安全感知机制,在网络中部署若干辅助节点,利用链路特征和加密机制对主用户的活动情况进行识别。设计适用于该机制的融合判决算法,不对授权网络做任何形式的改动即能有效识别恶意用户,并在融合时采用信任节点的感知数据,从而提高融合结果的鲁棒性。仿真结果表明,与K秩、And和Or算法相比,在恶意环境下该算法具有较好的检测性能。     

EMCOS: Energy-efficient Mechanism for Multimedia Streaming over Cognitive Radio Sensor Networks

One of the major challenges for multimedia transmission over multimedia WSN (MWSN) in urban environment is the scarcity of spectrum combined with high radio interference. Such environment makes it difficult to ensure high bandwidth, low delay and low packet losses required for real time multimedia streaming applications. We target a scenario of video surveillance in urban environment which not only requires efficiency of spectrum utilization, but also requires energy efficient mechanisms for the battery operated MWSN nodes. In this paper, we propose a new solution for multimedia transmission over WSNs which uses cognitive radio technology for spectrum efficiency and clustering mechanism for energy efficiency. A video streaming solution is proposed that is called “EMCOS: Energy-efficient Mechanism for Multimedia Streaming over Cognitive Radio Sensor Networks”. EMCOS ensures high quality real time multimedia transmission from one or more sources to a given sink, under different spectrum availability conditions, while efficiently using the energy of the MWSN nodes. First, EMCOS clusters the MWSN nodes into different clusters in order to ensure low energy consumption. Additionally for clustering, EMCOS not only takes into consideration the geographic positions, but it also takes into account the actual and the forecast of the channel availability in order to ensure stable clusters. Once the clusters are built, a cluster head is elected for each cluster in a way which preserves the cluster energy by considering the energy utilization of all cluster members. Further, to ensure the content delivery from the source to the sink, a routing/channel selection mechanism is proposed. The channel selection is based on PU activity forecasts to prevent frequent channel switching. Simulations show that our proposal EMCOS outperforms the two existing pioneering mechanisms called SEARCH and SCEEM. EMCOS outperforms them in terms of providing higher video quality, lower end-to-end transmission delay and lower frame loss ratio under varied spectrum conditions.     

Handwritten Data Clustering Using Agents Competition in Networks

In this paper, we study a new type of competitive learning scheme realized on large-scale networks. The model consists of several agents walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder agents. In the end of the process, each agent dominates a community (a strongly connected subnetwork). Here, the model is described by a stochastic dynamical system. In this paper, a mathematical analysis for uncovering the system’s properties is presented. In addition, the model is applied to solve handwritten digits and letters clustering problems. An interesting feature is that the model is able to group the same digits or letters even with considerable distortions into the same cluster. Computer simulations reveal that the proposed technique presents high precision of cluster detections, as well as low computational complexity.     

基于门限动态调整的最优协作检测方法

为提高协作频谱感知的检测效率、减少系统的反馈开销,提出一种基于门限动态调整的最优协作检测方法。在单节点检测错误概率最小的情况下,导出能量检测中门限值与信噪比的关系表达式,利用二分法求得不同信噪比下的动态门限值。根据系统恒虚警率和恒检测率计算最优协作用户数,并与动态门限值相结合得到系统检测错误概率。仿真结果表明,该方法协作检测概率较高,检测错误概率较低。     

Robust acoustic bird recognition for habitat monitoring with wireless sensor networks

The key solution to study birds in their natural habitat is the continuous survey using wireless sensors networks (WSN). The final objective of this study is to conceive a system for monitoring threatened bird species using audio sensor nodes. The principal feature for their recognition is their sound. The main limitations encountered with this process are environmental noise and energy consumption in sensor nodes. Over the years, a variety of birdsong classification methods has been introduced, but very few have focused to find an adequate one for WSN. In this paper, a tonal region detector (TRD) using sigmoid function is proposed. This approach for noise power estimation offers flexibility, since the slope and the mean of the sigmoid function can be adapted autonomously for a better trade-off between noise overvaluation and undervaluation. Once the tonal regions in the noisy bird sound are detected, the features gammatone teager energy cepstral coefficients (GTECC) post-processed by quantile-based cepstral normalization were extracted from the above signals for classification using deep neural network classifier. Experimental results for the identification of 36 bird species from Tonga lake (northeast of Algeria) demonstrate that the proposed TRD–GTECC feature is highly effective and performs satisfactorily compared to popular front-ends considered in this study. Moreover, recognition performance, noise immunity and energy consumption are considerably improved after tonal region detection, indicating that it is a very suitable approach for the acoustic bird recognition in complex environments with wireless sensor nodes.