Variable Persistence Sensing Schemes for Opportunistic Spectrum Access

Cognition technologies can significantly enhance spectrum utilization through secondary usage. Secondary users （SUs） search for spectrum holes which are not used by Primary users （PUs） and communicate through them. The design and analysis of simple opportunistic spectrum access schemes for maximizing utilization efficiency and minimizing delay of SUs under limited sensing bandwidth and energy is still at infancy. In this work, we consider PU＇s frequency occupancy patterns and propose three simple variable persistence sensing schemes for maximum exploitation of idle spectrum chunks by SUs, and perform their theoretical analysis. Numerical case studies are presented to verify the theoretical analysis and illustrate the performance of the schemes proposed.     

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.     

一种新的联合频谱能量检测方法

认知无线电技术被认为是解决未来频谱资源紧张的一种有效方法。而频谱感知技术是认知无线电中的一种关键技术,只有通过有效的频谱感知才能合理利用空间的频谱空洞。在传统的时域能量检测的基础上给出了一种结合时域与频域的新的频谱感知方法。该方法充分利用了时域与频域能量检测的优点,理论分析与仿真结果表明,该方法具有感知准确率高,功耗小的优点。适合于未来认知无线电中的应用。     

基于CSS的认知无线电网络PUEA检测和缓解

针对认知无线电网络中的主用户仿真攻击问题,提出了一种基于能量检测的协作频谱感知方法.首先,假设存在一个智能攻击者,它能知道自己所处环境并可以选择不同的传输策略.然后,调整CSS规则和相关参数为能量探测器确定合适且有效的阈值.最后,利用Neyman-Pearson准则进行能量检测,从而检测出恶意攻击.仿真结果表明,相比传统的基于能量检测的频谱感知方法,该方法可以更好地减轻PUEA在频谱感知中的破坏作用.     

Optimal Cooperative Inactive Secondary Sensing Order Using Users in Cognitive Radio Networks

A novel cooperative spectrum sensing or- der which utilizes inactive Secondary users （SUs） efficiently based on maximum throughput has been proposed in Cog- nitive radio networks （CRNs）. In order to predict the states of Primary users （PUs）, we build the PU＇s traffic pattern as a Continuous-time Markov chain （CTMC） pro- cess. CRNs obtain the maximum throughput while SUs sense the licensed channels with the optimal order. The numerical simulation results show that the proposed or- der based on spectrum sensing scheme can achieve larger channel utilization and lower sensing overhead as compared with the spectrum sensing scheme without using the opti- mal order sensing. After considering the report overhead of SUs, the optimal number of inactive SUs for the maxi- mum throughput can be found.     

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.     

基于压缩采样和DWPT的认知无线电频谱感知方案

认知无线电频谱感知技术要求能够快速、准确地对高达上千兆赫兹的带宽进行频谱感知,它需要很高速率的模数转换器（A/D）,这对传统的工作在Nyquist采样率下的频谱估计方法提出了很大的挑战。利用实际信号频谱在开放式频谱接入环境中的稀巯性,提出了基于压缩采样和小波包分解的宽带频谱感知方案。通过SIMULINK仿真表明,该方法对宽带信号能以远远低于Nyquist采样率的速率采样,并且能够精确地估计出可用信道列表。     

Maximizing the lifetime of energy‐constrained cooperative spectrum sensing sensor network

Adding the cognitive capability to wireless sensor networks allows the sensors to monitor the spectrum and identify the spectrum holes to operate in different frequencies according to the radio environment which result in better spectrum utilization. Spectrum sensing is a main component in any cognitive radio network. In this paper, we propose a new cooperative sensing scheme based on energy detection for cognitive sensor networks which is constrained by the energy limitation of the wireless sensor elements. The proposed scheme minimizes the sensing energy for individual sensor and carefully selects the suitable participant sensors in each cooperative sensing process. This results in maximizing the lifetime of energy‐constrained wireless sensor networks. The proposed scheme also takes into consideration the constraints on the detection accuracy. The simulation results show that the proposed scheme prolonged the lifetime of the cognitive network, makes efficient usage of available spectrum by secondary users, and satisfy the target detection performance.     

认知网络中基于簇结构的拓扑形成算法

在多跳的认知无线网络中,网络的拓扑发现算法都必须极其灵活以适应于环境内频谱空洞的改变。研究了认知无线电网络的基本特点,结合Ad-hoc网络簇的概念,在得到认知设备的感知结果的基础上,提出了一种适合于认知无线电网络的有效的拓扑形成算法。在提出的算法中,将可用的频谱空洞信息和节点的能量信息综合考虑,是一个组合的优化算法。     

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。     

滤波器组的多滤波器联合能量频谱感知算法

针对认知无线电系统,设计了一种基于滤波器组的多滤波器联合能量频谱感知算法.分析了算法的基本原理,给出了算法的流程,并以信噪比为参数说明了算法的优越性.仿真验证算法的检测概率与漏失概率,并对经典滤波器组的能量频谱感知算法的检测概率与漏失概率进行了比较,结果表明:本算法性能优于经典滤波器组能量频谱感知算法的性能.     

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.     

认知无线电的频谱感知技术研究

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。论文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感知频谱环境的功能,详细地对比分析了三种频谱检测的方法:匹配滤波器法、能量检测法和循环平稳特性检测法;最后介绍了“协同机制”用于改善极弱信号的接收问题。     

A New Cooperative Spectrum Sensing Scheme for Cognitive Ad-Hoc Networks

As the radio spectrum is becoming more and more crowded, cognitive radio has recently become a hot research topic to improve the spectrum utilization efficiency. It is well known that the success of cognitive radio depends heavily on fast and efficient spectrum sensing that is very difficult in practice. Toward this end, this paper introduces a new guard-resident cooperative spectrum sensing scheme for a cognitive ad-hoc network. In particular, we classify cognitive nodes as either resident or guard based on the spectrum neighbor decision and distributed boundary search. The guard nodes sense the spectrum and then inform the resident nodes that are greatly relieved from spectrum sensing about the radio environmental changes. The analysis and simulation results show that the proposed algorithm can significantly reduce the total spectrum sensing load without sacrificing the sensing accuracy.     

Energy Detection Approach for Spectrum Sensing in Cognitive Radio Systems with the Use of Random Sampling

In radiomobile contexte, radio frequency spectrum is a ressource that needs to be used with appropriate efficiency. This can be achieved by the mean of spectrum sensing operation. This function consists to analyze the occupancy of the radio frequency spectrum in order to detect which bands are unused. This concept is largely appreciated in cognitive radio where more flexibility is required to adapt to the communication environment. Different techniques are presented in the literature. In this paper, we are interested by the application of the energy detector method for spectrum sensing. This application is performed in cognitive radio systems with the use of random sampling. The performance of this approach is evaluated in term of its receiver operating characteristic curve and compared to the uniform sampling case.     

Optimal energy efficiency capacity under joint design of sensing and transmission duration for cognitive radio system

Due to its opportunistic spectrum sharing capability, cognitive radio （CR） has been proposed as a fundamental solution to alleviate the contradiction between spectrum scarcity and inefficient utilization of licensed spectrum. In CR system （CRS）, to efficiently utilize the spectrum resource, one important issue is to allocate the sensing and transmission duration reasonably. In this paper, the evaluation metric of energy efficiency, which represented the total number of bits that were delivered with per joule of energy consumed, is adopted to evaluate the proposed scheme. We study a joint design of energy efficient sensing and transmission durations to maximize energy efficiency capacity （EEC） of CRS. The tradeoff between EEC and sensing and transmission durations are formulized as an optimization problem under constraints on target detection probability of secondary users （SUs） and toleration interference threshold of primary users （PUs）. To obtain the optimal solution, optimizing sensing duration and transmission duration will be first performed separately. Then, a joint optimization iterative algorithm is proposed to search the optimal pair of sensing and transmission durations. Analytical and simulation results show that there exists a unique duration pair where the EEC is maximized, and that the EEC of the proposed joint optimization algorithm outperforms that of existed algorithms. Furthermore, the simulation results also reveal that the performance of the proposed low complexity iterative algorithm is comparable with that of the exhaustive search scheme.     

Artificial neural network design for improved spectrum sensing in cognitive radio

Dynamic Spectrum Access/Cognitive Radio systems access the channel in an opportunistic, non-interfering manner with the primary network. These systems utilize spectrum sensing techniques to sense the occupancy of the primary user. In this paper, an artificial neural network based hybrid spectrum sensing technique is proposed, which considers sensing as a binary classification problem to detect whether the primary user is idle or busy. The proposed scheme utilizes energy detection and likelihood ratio test statistic as features to train the neural network. Moreover, we demonstrate the impact of hyperparameter tuning and carry out the detailed study of it, yielding a combination of best-suited hyperparameters. The performance of the proposed sensing scheme is validated on primary signals of various real world radio technologies acquired with an empirical testbed setup. We conclude that the best performing configuration results in an increase of approximately 63% in detection performance compared to classical energy detection and improved energy detection sensing schemes when averaged over all the radio technologies considered in this work.

     

Utility driven cooperative spectrum sensing scheduling for heterogeneous multi-channel cognitive radio networks

Owing to the spectrum scarcity and energy constrained devices in wireless networks arises the demand for an efficient spectrum sensing technique which improves both sensing performance and energy efficiency for cognitive radio networks. This paper proposes a cooperative spectrum sensing scheduling (CSSS) scheme for heterogeneous multi-channel cognitive radio networks with the objective of finding an efficient sensing schedule to enhance network utility while keeping the energy depletion at a lower level. We start with formulating the CSSS problem as an optimization problem, which captures both the energy-performance and performance opportunity trade-offs. We prove that the formulated CSSS problem is non-deterministic polynomial hard (NP-hard). To tackle the higher computational complexity of the formulated problem, we propose a greedy-based heuristic solution, which produces a sub-optimal result in polynomial time. To reduce energy consumption during spectrum sensing, we make secondary users to adaptively decide on the sensing duration based on the received signal-to-noise ratio (SNR), where higher SNR leads to lower sensing duration and vice-versa. For enhancing network throughput, SUs sense multiple channels in the order of their suitability for data transmission to explore as many numbers of channels as possible within the permitted maximum sensing time. We consider erroneous nature of reporting channel to make the cooperative decision robust against errors during reporting. Simulation based results show the effectiveness of the proposed scheme in terms of utility, energy overhead, and the number of channels explored compared to similar schemes from literature.

     

Performance Analysis of Composite Fading Channel Based on Point Estimate Method

Existing spectrum sensing methods for cognitive radio do not consider the secondary network’s characteristics to obtain the frequency of spectrum sensing, i.e., the sensing period would be identical for secondary networks that have different traffic characteristics. In this paper, a hybrid sensing algorithm is proposed that finds the optimal sensing period based on both primary and secondary networks’ properties. A continuous-time Markov chain system is used to accurately model the spectrum occupancy, and a novel method is proposed that adaptively varies its parameters to avoid unnecessary sensing tasks, while guaranteeing the priority of the primary network. We conduct simulation work to evaluate the performance of the proposed method. It is shown that the proposed technique outperforms the non-hybrid approach with respect to sensing efficiency and energy consumption. A cognitive sensor network is also considered based on IEEE 802.15.4/ZigBee radios, and it is shown that significant energy savings can be achieved by the proposed method.