基于智能分析的分布式频谱共享技术研究

次用户以跳频序列的方式共享动态的频谱是分布式认知无线电网络频谱共享技术的解决方法之一。在此类方法中,次用户跟随特定的跳频序列接入到它的所有可用信道中,并在此过程中实现与其他次用户的交会。根据分布式认知网络的组网特性,提出了一种针对交会操作的MAC层超帧结构,使得整个认知网络在此结构支撑下可以取得更好的系统性能。根据次用户的频谱感知和使用信道情况的积累与分析,建立次用户的智能频谱库,为次用户的信道接入提供策略支持。仿真结果表明,提出的MAC协议能在高网络负载情况下能获得较好的综合性能。     

基于协作频谱预测的认知网络吞吐率分析

频谱预测是将预测结果传递给次级用户（Secondary User,SU）,使SU有选择性地实施频谱感知,提高频谱感知的有效性。但是存在预测结果不准确的情况,影响整个网络的吞吐率。在基于遗传算法优化的神经网络预测模型基础上,提出了SU进行协作的频谱预测方法,提高了SU预测空闲信道的准确率。讨论了协作频谱预测条件下,在通信强度、协作用户数量、信道数量不同时的系统吞吐率。仿真结果表明协作频谱预测比传统非协作频谱预测系统吞吐率有较大提升。     

On optimal fuzzy c-means clustering for energy efficient cooperative spectrum sensing in cognitive radio networks

This work explores the scope of Fuzzy C-Means (FCM) clustering on energy detection based cooperative spectrum sensing (CSS) in single primary user (PU) cognitive radio network (CRN). PU signal energy sensed at secondary users (SUs) is forwarded to the fusion center (FC). Two different combining schemes, namely selection combining (SC) and optimal gain combining are performed at FC to address the sensing reliability problem on two different optimization frameworks. In the first work, optimal cluster center points are searched for using differential evolution (DE) algorithm to maximize the probability of detection under the constraint of meeting the probability of false alarm below a predefined threshold. Simulation results highlight the improved sensing reliability compared to the existing works. In the second one, the problem is extended to the energy efficient design of CRN. The SUs act here as amplify-and-forward (AF) relays and PU energy content is measured at the FC over the combined signal from all the SUs. The objective is to minimize the average energy consumption of all SUs while maintaining the predefined sensing constraints. Optimal FCM clustering using DE determines the optimal SU amplifying gain and the optimal number of PU samples. Simulation results shed a light on the performance gain of the proposed approach compared to the existing energy efficient CSS schemes.     

ICSSSS: An intelligent channel selection scheme for cognitive radio ad hoc networks using a self organized map followed by simple segregation

In Cognitive Radio Ad Hoc Networks (CRAHNS), several spectrum bands with different channel characteristics may be available over a large frequency range. It is essential to identify the most appropriate spectrum band correctly which allow the Secondary Users (SUs) to exploit the band without disturbing the Primary Users (PUs). Many channel selection solutions, based on cooperative spectrum sensing, have been employed for this purpose depending on their prediction models for primary users’ activities. In practice, cooperative spectrum sensing cannot completely solve the sensing problems which are false alarm and miss detection, especially in heavily shadowed or fading environment. This paper presents, ICSSSS, as an Intelligent Channel Selection Scheme for cognitive radio ad hoc network using Self organized map followed by simple Segregation. The contribution of the proposed scheme is twofold: using an unsupervised learnable Self Organizing Map (SOM) method to efficiently minimize the probability of the sensing errors (false alarm and miss detection), in addition to segregated channel selection strategy to speed up the search for the available best channel. Simulation results based on NS2 simulations show that the proposed scheme can be used with the advantage of better performance than other existing channel selection strategies.     

Application of reinforcement learning for security enhancement in cognitive radio networks

Cognitive radio network (CRN) enables unlicensed users (or secondary users, SUs) to sense for and opportunistically operate in underutilized licensed channels, which are owned by the licensed users (or primary users, PUs). Cognitive radio network (CRN) has been regarded as the next-generation wireless network centered on the application of artificial intelligence, which helps the SUs to learn about, as well as to adaptively and dynamically reconfigure its operating parameters, including the sensing and transmission channels, for network performance enhancement. This motivates the use of artificial intelligence to enhance security schemes for CRNs. Provisioning security in CRNs is challenging since existing techniques, such as entity authentication, are not feasible in the dynamic environment that CRN presents since they require pre-registration. In addition these techniques cannot prevent an authenticated node from acting maliciously. In this article, we advocate the use of reinforcement learning (RL) to achieve optimal or near-optimal solutions for security enhancement through the detection of various malicious nodes and their attacks in CRNs. RL, which is an artificial intelligence technique, has the ability to learn new attacks and to detect previously learned ones. RL has been perceived as a promising approach to enhance the overall security aspect of CRNs. RL, which has been applied to address the dynamic aspect of security schemes in other wireless networks, such as wireless sensor networks and wireless mesh networks can be leveraged to design security schemes in CRNs. We believe that these RL solutions will complement and enhance existing security solutions applied to CRN To the best of our knowledge, this is the first survey article that focuses on the use of RL-based techniques for security enhancement in CRNs.     

FPGA implementation and performance study of spectrum sensing based on entropy estimation using cyclic features

This work presents a spectrum sensing technique based on the entropy of frequency domain autocorrelation of receiving signal at different cyclic frequencies. The performance of the proposed sensing technique is compared with other sensing techniques such as energy detection using Bayesian and Neyman–Pearson criteria, entropy estimation under frequency domain, cyclostationary feature detection. The performance of sensing algorithms is also analyzed for single node and multinode/cooperative environment under most probable channel effects such as fading, shadowing, receiver’s uncertainty and free space path loss using Monte-Carlo methods. Simulation results reveal that the proposed sensing technique is able to detect signals of signal-to-noise ratio up to −24 dB with five nodes in cooperation while maintaining a false alarm probability of 0.1 and a detection probability of 0.9. The proposed sensing algorithm is also implemented in Virtex-4 Field Programmable Gate Arrays.     

基于信道预测的认知无线电混合频谱切换算法

针对认知无线电网络,提出了一种将被动式频谱切换与主动式频谱切换相结合的混合频谱切换算法。该算法基于主用户信道的连续时间马尔可夫链模型,预测出信道的未来状态信息,根据该预测结果周期性地对正在通信的认知用户执行主动式频谱切换。该算法对于由于碰撞而退出信道的认知用户执行被动式频谱切换。仿真结果表明,相对于被动频谱切换算法,混合频谱切换算法在保持认知用户阻塞概率和中断概率不变的前提下可显著减少认知用户和主用户间的碰撞次数,能够提高认知无线电网络的频谱利用率。     

Opportunistic medium access control for maximizing packet delivery rate in dynamic access networks

One of the key challenges to enabling efficient cognitive radio (CR) communications is how to perform opportunistic medium access control (MAC) that maximizes spectrum efficiency. Several CRN MAC protocols have been designed assuming relatively static primary radio (PR) channels with average idle durations largely exceed CR transmission times. For such CR environment, typical multichannel MAC protocols, which select the best quality channel, perform reasonably well. However, when such mechanism is employed in a CRN that coexists with highly dynamic licensed PR networks (PRNs), where PR channel idle durations are comparable to CR transmission times, the forced-termination rate for CR transmission can significantly increase, leading to a reduction in network throughput. To reduce the forced-termination rate, many MAC protocols have been proposed to account for the dynamic time-varying nature of PR channels by requiring communicating CR users to consistently perform channel switching according to PR activities. However, such channel-switching strategy introduces significant overhead and latency, which negatively affect network throughput. Hence, in this paper, we propose a probabilistic channel quality- and availability-aware CRN MAC. Our protocol uses a novel channel assignment mechanism that attempts at maximizing the packet success probability of each transmission and hence avoids the significant overhead and latency of channel switching. Simulation results show that by being quality- and availability-aware, our protocol provides better spectrum utilization by decreasing the forced-termination rate and improving network throughput.     

Securing Privacy Using Optimization and Statistical Models in Cognitive Radio Networks

Cognitive Radio Networks (CRN) are the possible and ideal solution for meeting the spectrum needs of next-generation communication systems. CRN is a promising alternative approach that allows spectrum sharing in many applications. The licensed users considered Primary Users (PU) and unlicensed users as Secondary Users (SU). Time and power consumption on security issues are considered degrading factors in performance for improving the Quality of Service (QoS). Irrespective of using different optimization techniques, the same methodology is to be updated for the task. So that, learning and optimization go hand in hand. It ensures the security in CRN, risk factors in spectrum sharing to SU for secure communication. The objective of the proposed work is to preserve the location of the SU from attackers and attain the clustering of SU to utilize the resource. Ant Colony Optimization (ACO) is implemented to increase the overall efficiency and utilization of the CRN. ACO is used to form clusters of SUs in the co-operative spectrum sensing technique. This paper deals with threat detection and classifying threats using parameters such as unlikability, context privacy, anonymity, conditional traceability, and trade-off. In this privacy-preserving model, overall accuracy is 97.4%, and it is 9% higher than the conventional models without Privacy-Preserving Architecture (PPA).     

CDCSS: cluster-based distributed cooperative spectrum sensing model against primary user emulation (PUE) cyber attacks

In cognitive radio network, the secondary users (SUs) use the spectrum of primary users for communication which arises the security issues. The status of SUs as legitimate users is compulsory for the stability of the system. This paper addresses the issue of delay caused by a band-selection decision process that directly affects the security and performance. The model cluster-based distributed cooperative spectrum sensing is proposed. In this model, cluster heads (CHs) exchange control information with other CHs and ordinary nodes. This model significantly reduced the delay, sensing, convergence, routing, in band-selection process. This also reduces the energy consumption while sensing the spectrum which seriously leads to performance upgradation. The simulated results show the improved performance of cognitive radio networks in terms of delay, packet loss ratio and bandwidth usage as compared to cluster-based cooperative spectrum sensing model. The opportunity for primary user emulation attacker is minimized as the overall delay is reduced.     

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.     

针对时变OFDM信道的一种盲估计改进算法

OFDM盲信道估计技术可以在不需要导频的情况下,估计出信道的状态信息,能有效节约带宽,提高频谱利用率。分析了一种基于子空间SVD方法的盲信道估计技术,在此算法基础上提出了针对时变信道的估计方法,并就现有方法估计精度较低的问题,提出了一种利用信道相关特性进行λ加权的优化算法。对算法进行了蒙特卡罗仿真,仿真结果表明该算法能够有效改善信道估计性能,并且具有较低的复杂度。     

A game-theoretic and stochastic survivability mechanism against induced attacks in Cognitive Radio Networks

Cognitive Radio Networks (CRNs) are envisioned to provide a solution to the scarcity of the available frequency spectrum. It allows unlicensed secondary users (SUs) to use spectrum bands that are not occupied by licensed primary users (PUs) in an opportunistic manner. This dynamic manner of spectrum access gives rise to vulnerabilities that are unique to CRNs. In the battle over the available spectrum, SUs do not have any means of identifying whether disruption sensed on a band is intentional or unintentional. This problem is further intensified in the case of heterogeneous spectrum, where different bands provide different utilities. A smart malicious agent can use this vulnerability to temporarily disrupt transmissions on certain bands and induce their unavailability on SUs. The motivation for such disruption-induced attacks can be either monopolism, i.e. to capture as much spectrum as possible and make other SUs starve, or denial of service by intentional disruption of other SUs’ communications. This paper proposes an adaptive strategy for robust dynamic spectrum access in the event of induced attacks. Assuming rational players, and considering the notion of channel utility, the optimal strategy is established by modeling such scenarios as zero-sum games that lead to Nash equilibrium. Thereafter, the case of non-stationary channel utilities is investigated, where utilities are subject to abrupt changes due to fluctuations in channel characteristics, as well as arrival and departure of PUs. Through concurrent estimation, learning, and optimal play, it is shown that the proposed mechanism performs robustly even in such dynamic environments. Comparison of the proposed mechanism to other reasonable benchmark strategies in simulation confirms that this mechanism significantly enhances the performance of CRNs.     

Analysis of Cognitive Radio for LTE and 5G Waveforms

Spectrum sensing is one of the major concerns in reaching an efficient Quality of service (QOS) in the advanced mobile communication system. The advanced engineering sciences such as 5G, device 2 device communications (D2D), Internet of things (IoT), MIMO require a large spectrum for better service. Orthogonal frequency division multiplexing (OFDM) is not a choice in advanced radio due to the Cyclic Prefix (CP), wastage of the spectrum, and so on. Hence, it is important to explore the spectral efficient advanced waveform techniques and combine a cognitive radio (CR) with the 5G waveform to sense the idle spectrum, which overcomes the spectrum issue. The demand for spectrum is ever increasing; however, spectrum is limited and is an acutely scarce resource. To alleviate the issue, techniques like Cognitive Radios (CR) have been devised. However, such techniques are non-standardized, and many variations of CR algorithms have been tried and tested. This paper details the several spectrum sensing methods tailored for CR. We explain the benefits, uniqueness, and drawbacks of the various techniques to provide a comprehensive review of the scene, including all recent and novel techniques of CR. Finally, we provided experimental results for the performance of the CR for key 5G and beyond modulation techniques to elaborate the dependency of the CR techniques for CR applications and provide a competitive review of their performance. Experiments show that the CR integrated with NOMA shows better performance as compared with existing techniques.     

一种改进的MIMO-OFDM系统频域信道估计和跟踪方案

深入研究了MIMO-OFDM系统中的频域信道估计和跟踪算法．在分析比较了现有频域信道估计算法的基础上,提出了一种有效的改进频域信道估计和跟踪方案．该方案利用基于正交训练符号的算法求得初始信道估计值,利用信道估计和信号检测的联合迭代算法来跟踪随时间变化的信道参数．在典型的室内传输环境下对三种频域信道估计和跟踪方案进行了仿真比较,结果表明,本文所提方案在较低的迭代次数下就可以带来明显的性能改善．     

System model and performance estimation of dynamic spectrum allocation strategy with multi-channel and imperfect sensing

The concept of cognitive radio networks (CRNs) is a promising candidate for enhancing the utilization of existing radio spectrum. In CRNs, secondary users (SUs) are allowed to use the spectrum unused by primary users (PUs). In order to mathematically estimate the system performance of dynamic spectrum allocation strategy with multi-channel and imperfect sensing, we propose a novel preemptive priority queueing model. We establish a discrete-time Markov chain in line with the stochastic behaviour of SU and PU packets. Then, we derive some performance measures, such as the interference rate of PU packets, the normal throughput and the average delay of SU packets. Moreover, we provide theoretical and simulation experiments to investigate the system performance. Numerical experiments show that there is a tradeoff between different performance measures when imperfect sensing is considered. Finally, we present an optimal design for setting the number of the channels in a spectrum.     

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.

     

CRN中基于自适应阈值和OR-决策规则的频谱感知策略

针对传统认知无线电网络(CRN)的频谱感知策略没有考虑噪声不确定性问题,提出一种基于噪声功率估计自适应阈值和OR-决策规则的频谱感知策略。首先,将各接收器数据构建成一个数据矩阵,并计算矩阵的协方差矩阵。然后,计算协方差矩阵的特征值,并根据特征值的均值来获得噪声的最大似然估计。接着,根据估计的噪声和能量信号的检验统计量来确定决策阈值。最后,各节点根据决策阈值作出局部决策并上传融合中心(FC),FC利用OR-决策规则作出最终决策。实验结果表明,该方案的决策阈值能够随噪声自适应调整,有效提高了检测率,对噪声不确定性具有很好的鲁棒性。     

基于隐马尔可夫模型的短波认知频率选择方法

针对短波频谱利用率低下及频率选择不够智能的局限性,提出一种基于隐马尔可夫模型(HMM)的短波认知频率选择方法。应用认知无线电原理,将短波传统用户作为主用户,将采用认知无线电技术的短波电台作为认知用户。首先,建立隐马尔可夫模型,结合频谱感知历史数据预测主用户信道状态;其次,在预测空闲的基础上估计信道参数;最后,根据估计的信道参数选择最优频率。仿真结果表明,所提方法能够准确预测传统短波用户信道状态,快速估计信道参数。在设定的仿真条件下,所提方法的成功传输率分别较HMM预测和能量感知随机信道选择方法有5.54%和10.56%的提升,能够选择最优信道。     

基于DFT寻径的压缩感知信道估计改进算法

正交频分复用系统中的信号在传输过程中受到无线信道环境衰落和延时的影响,容易产生符号间干扰（ISI）,对信道状态信息进行准确估计是降低ISI、提高信号传输准确率的有效方法。针对贪婪迭代类压缩感知信道估计算法存在的估计径错误及漏选问题,提出一种基于离散傅里叶变换（DFT）寻径的压缩感知信道估计算法DFT-OMP。通过DFT寻径的方式抑制由噪声引起的不理想原子,从而对OMP算法重构过程中的原子进行筛选,解决传统方法选取相关因子最大的原子作为重构原子而导致的依赖信号稀疏度问题。在原子预选后的贪婪迭代类压缩感知算法信道估计中引入残差精度控制,以提高信道估计的自适应性与鲁棒性。仿真结果表明,相对OMP算法,该算法能取得4 dB的信道估计性能增益,其适用于较大导频下的无线通信系统。