Double‐threshold cooperative detection for cognitive radio based on weighing

In cognitive radio (CR), the single‐threshold cooperative detection is sensitive to the noise power. In this paper, a double‐threshold cooperative detection scheme based on weighed combination is proposed to improve the global detected performance of the cooperative detection. The double thresholds that are decided by the restriction probability are adopted to divide the energy statistics observed by CRs into three subareas, and the detected performance of the CRs locating in the confusion area is fortified through the weighed data fusion by the coordinator. The CRs in the other two areas can make local binary decisions because of their exact sensing performance and then forward their 1‐bit decisions to the coordinator that will combine all the received decisions to give the final decision on the presence of the primary user. In this paper, both the periodic single‐band and wideband cooperative detections are investigated, and the probabilities of spectrum utilization by these two patterns are respectively analyzed. The simulation shows that the detection probability of the proposed scheme is better than that of the single‐threshold cooperative detection with decision fusion but slightly worse than that of the single‐threshold cooperative detection with data fusion. However, the average number of the bits sent to the coordinator by the proposed algorithm is less than that by the data fusion, and it may increase by the decreasing of the restriction probability. The simulation also shows that the periodic wideband sensing can obtain higher spectrum utilization than the periodic single‐band sensing. Copyright © 2012 John Wiley & Sons, Ltd.     

Throughput analysis in censoring-based cooperative cognitive radio network with energy harvesting

An energy harvesting (EH) and cooperative cognitive radio (CR) network (CRN) is studied in this paper where CR users transmit data through a primary user (PU) channel if the channel remains idle, else an optimal number CRs helps in transmission of PU. To achieve the optimum number of CRs (ONCR) involved in cooperation, a novel scheme based on a combination of channel censoring and total error is proposed. The performance of the proposed scheme is investigated under RF harvesting scenario. The EH is dependent on sensing decision and a CR source harvests energy from PU's RF signal. The harvested energy (HE) is split into two parts: One part is used by the CR network (CRN) for its own transmission, and the other part is used for supporting PU. The effect of the energy allocation factor on total throughput is also investigated. New expressions for optimal number of CRs and throughput are developed. The effect of network parameters such as sensing time, censoring threshold, and energy allocation parameter (EAP) on throughput is investigated. Impact of distance between nodes is also studied.     

Distributed Cooperative Wideband Spectrum Sensing Algorithm Based on Subspace Methods

We present a novel distributed cooperative spectrum sensing algorithm from compressive sampling in wideband cognitive radio (CR) networks. Each CR utilizes compressive sampling to reduce data acquisition costs. A subspace method is then adopted to directly detect occupied channels without reconstructing the sparse spectrum. To obtain the spatial diversity gain, global signal subspace is estimated by the distributed projection approximation subspace tracking (DPAST) algorithm in which the CRs exchange information locally and cooperate without the need for a fusion center. Then, the orthogonality property of the signal subspace and noise subspace can be exploited to find spectral support to complete the spectrum sensing. We study the convergence behavior of the DPAST algorithm and evaluate the performance of spectrum sensing. Simulation results indicate that the DPAST can effectively estimate the global signal subspace, and the proposed compressed wideband spectrum sensing scheme performs better than spectrum sensing at a single CR.     

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.     

Sensing‐throughput tradeoff for cooperative multiple‐input single‐output cognitive radio

In this paper, we propose a new cooperative multiple‐input single‐output (MISO) cognitive radio (CR) system, which can use some of the antennas to transmit its data and the others to help to transmit the data of the primary user (PU) by performing cooperative communication if the presence of the PU is detected through the cooperative spectrum sensing. A new cooperative sensing‐throughput tradeoff model is proposed, which maximizes the aggregate rate of the CR by jointly optimizing sensing time and spatial sub‐channel power, subject to the constraints of the aggregate rate of the PU, the false alarm and detection probabilities, the aggregate interference to the PU and the aggregate power of the CR. Simulation results show that compared with the conventional scheme, the proposed cooperative scheme can achieve the larger aggregate rate of the CR, while keeping the aggregate rate of the PU invariable with the increasing of the interference. Copyright © 2013 John Wiley & Sons, Ltd.     

A new sensing‐throughput tradeoff scheme in cooperative multiband cognitive radio network

A conventional cognitive radio network (CRN) uses the spectrum of the licensed primary network (PN) on the premise of detecting the absence of the PN by the spectrum sensing of the sensor node (SN). In this paper, a cooperative multiband CRN is considered, wherein the SNs are allowed to use some time of the transmission slot to relay PN data by cooperative communication, while using the remaining time of the transmission slot to forward its own data, over multiple sub‐bands during each frame, if the presence of PN is detected by cooperative spectrum sensing of the SNs in the sensing slot. A new sensing–throughput tradeoff scheme is formulated as a multi‐variable optimization problem, which maximizes the average aggregate throughput of the CRN over all the sub‐bands by jointly optimizing spectrum sensing time and sub‐band transmission power, subject to the constraints on the average aggregate throughput of the PN, the maximal aggregate power of each SN, and the false alarm and detection probabilities of each sub‐band. The bi‐level optimization method is adopted to obtain the optimal solution by dividing the multi‐variable optimization problem into two convex single‐variable sub‐optimization problems. The simulations show that there exists the optimal sensing time and sub‐band transmission power that maximize the average aggregate throughput of the CRN and, compared with the conventional scheme, the throughput obtained by the proposed scheme is outstanding. Copyright © 2014 John Wiley & Sons, Ltd.     

Study the effect of PAPR on wideband cognitive OFDM radio networks

Cognitive radio (CR) technology is viewed as a novel approach for maximizing the utilization of the radio electromagnetic spectrum. Spectrum sensing methods are often used for finding free channels to be used by CR. Recently, Orthogonal Frequency Division Multiplexing system (OFDM) has been suggested as a candidate technology for multicarrier-based CR systems. However, one problem that appears in OFDM systems is the high Peak to Average Power Ratio (PAPR). In this paper, the effect of PAPR reduction of the primary signal on the performance of the multiband joint detection for wideband spectrum sensing and the profit of the primary user will be investigated. Moreover, the optimal solutions for the multi-band joint detection for the non-cooperative and cooperative schemes will be analyzed by considering the primary user’s PAPR reduction. Also, the wideband cooperative spectrum sensing to improve the signal detection with high reduction in the PAPR will be suggested. Simulation results show that the PAPR reduction decreases the total price of the primary user and the aggregate opportunistic throughput of the secondary user. The cooperative scheme is effective in improving the performance in terms of the aggregate opportunistic throughput with PAPR reduction.     

A Low Complexity High Resolution Cooperative Spectrum-Sensing Scheme for Cognitive Radios

Conventional distributed cognitive radio (CR) communication aims to increase the sensing accuracy and reduce the detection time by allowing the CR users to operate in the same frequency band in a cooperative fashion and by forming efficient decision making. In this paper, we propose a spectrum-sensing scheme which provides accurate detection at low complexity, by assigning different sensing tasks to the CRs. If the number of cooperative CRs is N, our method offers an effective sensing resolution of N times higher than that of the conventional method. An inherently low complex progressive decimation filter bank, which provides variable sensing resolutions by software reconfiguration, is employed at each CR. The design example shows that the hardware complexity of our sensing scheme is 41.3% less than that of the conventional spectrum sensor. It is also shown that the proposed system can support multiple communication standards.     

基于监督学习的协作宽带压缩频谱检测方案

宽带压缩频谱检测存在信号稀疏度未知和次用户检测开销过大的问题.因此,本文提出一种高效的协作宽带压缩频谱检测方案.首先,推导了一种基于学习的稀疏度自适应预测模型.其次,设计了一种宽带频谱筛选算法.最后,提出一种协作宽带压缩频谱检测方案.仿真结果表明,自适应预测模型的拟合效果优于现有预测模型,并且所提检测方案也有效地降低了次用户采样率和频谱重构时延.     

基于自适应决策融合的合作频谱感知方法

频谱感知是认知无线电(CR)的关键技术之一。在该机制中,对主用户(PU)信号的可靠检测是实现CR的前提。提出一种基于自适应决策融合的合作频谱感知算法用于频谱感知,该算法通过估计PU的先验概率与各个CR用户(SU)的漏检及虚警概率,然后运用Chair-Varshney准则对局部判决进行决策融合以得到全局判决。仿真结果表明,采用该方案的全局虚警和漏检概率明显低于单个SU,可有效提高CR系统频谱感知的可靠性。     

基于加权平均一致算法的宽带压缩频谱感知

在认知无线电(CR)网络中进行频谱共享接入,首要的任务是进行频谱感知,并发现频谱空洞。基于认知无线网络中信号频域的固有稀疏性,本文结合了压缩感知(CS)技术与加权平均一致(weighted average consensus)算法,建立了分布式宽带压缩频谱感知模型。频谱感知分为两个阶段,在感知阶段,各个CR节点对接收到的主用户信号进行压缩采样以减少对宽带信号采样的开销和复杂度,并做出本地频谱估计;在信息融合阶段,各CR节点的本地频谱估计结果以分布式的方式进行信息融合,并得到最终的频谱估计结果,获得分集增益。仿真结果表明,结合压缩感知与加权平均一致算法增强了频谱感知的性能,比在相同的CR网络中使用平均一致算法时有了性能上的提升。      

Cooperative Spectrum Sensing Scheme with Hard Decision Based on Location Information in Cognitive Radio Networks

Without an efficient way to achieve the reliability of the decision, the implementation of weighted data fusion is limited in the hard decision combination for cooperative spectrum sensing. To address this problem, a new cooperative spectrum sensing scheme based on the location information of the primary user (PU) and cognitive radio (CR) is proposed. In the new scheme, depending on the location information, the channel condition between the PU and each CR is obtained at the fusion center (FC), with which the local sensing reliability is first achieved. Then we calculate the transmission reliability between the CR and FC. Based on both the local sensing reliability and the transmission reliability, the combining weighting factor is determined for optimal data fusion. On the basis of this proposed scheme, we study the global sensing false alarm and detection probabilities, derive the expressions to obtain the optimal local sensing threshold, and perform an error analysis that demonstrates the impact of imperfect channel knowledge. Using both analytical and simulation methods, we find that the proposed scheme achieves better performance compared with the conventional logical fusion rules in the hard decision combination for cooperative spectrum sensing.     

Analysis of Joint Multiband Sensing‐Time M‐QAM Signal Detection in Cognitive Radios

We analyze a wideband spectrum in a cognitive radio (CR) network by employing the optimal adaptive multiband sensing‐time joint detection framework. This framework detects a wideband M‐ary quadrature amplitude modulation (M‐QAM) primary signal over multiple nonoverlapping narrowband Gaussian channels, using the energy detection technique so as to maximize the throughput in CR networks while limiting interference with the primary network. The signal detection problem is formulated as an optimization problem to maximize the aggregate achievable secondary throughput capacity by jointly optimizing the sensing duration and individual detection thresholds under the overall interference imposed on the primary network. It is shown that the detection problems can be solved as convex optimization problems if certain practical constraints are applied. Simulation results show that the framework under consideration achieves much better performance for M‐QAM than for binary phase‐shift keying or any real modulation scheme.     

A Distance Based Reliable Cooperative Spectrum Sensing Algorithm in Cognitive Radio

Spectrum sensing is the most critical task in cognitive radio (CR) which needs to be performed very precisely in order to efficiently utilize the underutilized spectrum and to provide sufficient protection to the primary users (PUs). To improve the sensing performance under fading, shadowing and hidden terminal problems more than one CR users collaboratively perform the spectrum sensing called as cooperative spectrum sensing (CSS). In conventional CSS the decision of each CR is fused at fusion center with equal weights. But due to variable distance of each CR from the PU all decisions are not equally reliable and therefore should be assigned different weights according to their reliability. In this paper we propose a new weighting scheme for CSS under Rayleigh faded channel. In proposed weighting scheme, based on the distance of each CR from the PU reliability of CR nodes is determined and correspondingly appropriate weights are assigned to different users. The CSS algorithm using new weighting scheme gives better performance than conventional CSS algorithm.     

Cross‐layer optimization for full‐duplex cognitive radio network with cooperative spectrum sensing

In this paper, the cross‐layer optimal design of multihop ad hoc network employing full‐duplex cognitive radios (CRs) is investigated. Firstly, the analytical expressions of cooperative spectrum sensing performance for multihop CR networks over composite fading channels are derived. Then, the opportunistic throughput and transmit power of CRs are presented based on the derivation of false alarm and missed detection probability. Finally, a multiobjective optimization model is proposed to maximize the opportunistic throughputs and minimize the transmitting power. Simulation results indicate that Pareto optimal solution of sensing duration, decision threshold, and transmit power can be achieved by cross‐layer multiobjective optimization, it can balance the conflicts between different objective functions and reap the acceptable outcomes for multihop CR network.     

认知无人机网络中次级链路吞吐量优化研究

无人机(UAV)的便携性和高机动性使其与认知无线电(CR)结合的应用场景更加实用。在构建的无人机认知无线网络(CRN)模型中,该文提出UAV单弧度吞吐量优化方案,在确保检测概率的前提下优化感知弧度最大化UAV平均吞吐量。考虑在信道条件不理想情况下进一步改善感知性能,提出基于协作频谱感知(CSS)的多弧度吞吐量优化方案,利用交替迭代优化(AIO)算法对感知弧度和弧度数量进行联合优化以最大化吞吐量。仿真结果表明,该文提出的多弧度协作频谱感知方案在信道衰落严重时,对于主用户(PU)服务质量(QoS)和UAV吞吐量有明显提升。     

Joint spectrum sensing and resource allocation optimization using genetic algorithm for frequency hopping–based cognitive radio networks

In cognitive radio (CR) networks, secondary users should effectively use unused licensed spectrums, unless they cause any harmful interference to the primary users. Therefore, spectrum sensing and channel resource allocation are the 2 main functionalities of CR networks, which play important roles in the performance of a CR system. To maximize the CR system utility, we propose a joint out‐of‐band spectrum sensing and operating channel allocation scheme based on genetic algorithm for frequency hopping–based CR networks. In this paper, to effectively sense the primary signal on hopping channels at each hopping slot time, a set of member nodes sense the next hopping channel, which is called out‐of‐band sensing. To achieve collision‐free cooperative sensing reporting, the next channel detection notification mechanism is presented. Using genetic algorithm, the optimum sensing and data transmission schedules are derived. It selects a sensing node set that participate the spectrum sensing for the next expected hopping channel during the current channel hopping time and another set of nodes that take opportunity for transmitting data on the current hopping channel. The optimum channel allocation is performed in accordance with each node's individual traffic demand. Simulation results show that the proposed scheme can achieve reliable spectrum sensing and efficient channel allocation.     

Scheme of Cooperative Spectrum Sensing Based on Adaptive Decision Fusion Algorithm

Spectrum sensing is one of the core technologies for cognitive radios(CR),where reliable detection of the signals of primary users(PUs) is precondition for implementing the CR systems.A cooperative spectrum sensing scheme based on an adaptive decision fusion algorithm for spectrum sensing in CR is proposed in this paper.This scheme can estimate the PU prior probability and the miss detection and false alarm probabilities of various secondary users(SU),and make the local decision with the Chair-Varshney rule so that the decisions fusion can be done for the global decision.Simulation results show that the false alarm and miss detection probabilities resulted from the proposed algorithm are significantly lower than those of the single SU,and the performance of the scheme outperforms that of the cooperative detection by using the conventional decision fusion algorithms.     

Adaptively Controlled Cooperative Spectrum Sensing Using OR Fusion Rule for Throughput Maximization in Cognitive Radio

Utilization of wireless spectrum resources should be improved with the great increase of using wireless services. Cognitive radio (CR) has been proposed to overcome the problem of inefficient use of spectrum holes. Spectrum sensing is the most important functional block, which is used to estimate the spectrum holes in the CR system. Cooperative spectrum sensing improves the detection performance and minimizes miss-detection of the primary user. The objective of this paper is to achieve the maximum throughput for all values signal-to-noise ratio (SNR). Cooperative spectrum sensing with fixed sensing time does not provide efficient throughput performance for all values of SNR. In this paper, we propose an adaptively controlled cooperative spectrum sensing scheme using OR fusion rule for throughput maximization in CR system. The proposed scheme estimates the required optimal sensing time for each value of the SNR to achieve the maximum throughput. Simulation results of the proposed scheme demonstrate that it maximizes the achieved throughput for all values of SNR and minimizes the probability of false alarm.

     

基于授权用户状态迁移统计特性的频谱感知算法

通过动态监测各个频段使用状况并机会性地接入空闲频谱,认知无线电技术可显著提高已分配频段的频谱利用效率,继而有效地缓解频谱资源日趋枯竭的现状。文章首先采用一类具有记忆特性的状态迁移模型来刻画授权用户行为;综合考虑漏检概率与虚警概率的影响,建立了频谱检测的代价函数;在此基础上,采用最大后验概率准则实现检测算法,并对理论算法进行修正,使其满足实际场景下低复杂度、低检测时延要求。借助于有限状态机模型所隐含的编码增益,该算法能显著提高检测性能,数值仿真也进一步验证了算法有效性。