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.

High Capacity Spectrum Sensing Framework Based on Relay Cooperation

In order to reduce interference to primary users and provide better performance of detection probability and channel capacity in multi-user cooperative spectrum sensing networks, a high capacity spectrum sensing framework is proposed based on the analysis of amplification factors on the performance of detection probability and channel capacity. Thanks to the energy concentration property of chirp signals in the Fractional Fourier Transform(Fr FT) domain, sinusoidal signal and different chirp signals are utilized for primary user and cognitive users, respectively. Hence, spectrum sensing and signal transmission can be performed simultaneously in our proposed framework. Simulation results show that compared with the previous relay-based framework, the modified cooperative spectrum sensing framework can improve the detection probability significantly, and the channel capacity can also be improved. Moreover, the amplification factor can be used to realize the tradeoff between detection probability and channel capacity in our proposed framework.

Joint Optimal Energy-Efficient Cooperative Spectrum Sensing and Transmission in Cognitive Radio

In order to improve the energy efficiency(EE) in cognitive radio(CR), a joint optimal energy-efficient cooperative spectrum sensing(CSS) and transmission in multi-channel CR is proposed in this paper. EE is described as a tradeoff between the throughput and the entirely consumed power. A joint optimization problem is formulated to maximize EE by jointly optimizing local sensing time, number of cooperative sensing secondary users(SU), transmission bandwidth and power. A combined optimization algorithm of bi-level optimization, Polyblock optimization and Dinkelbach's optimization is proposed to solve the proposed non-convex optimization problem effectively. The simulation results show that, compared with throughput maximization model(TMM), the energy efficiency maximization model(EEMM) improves EE of the CR system and limits the excessive power consumption effectively.

A NOVEL COOPERATIVE SPECTRUM SENSING METHOD BASED ON COOPERATIVE GAME THEORY

A novel cooperative sensing method is proposed in this paper. The proposed scheme adopts sensing creditability degree to characterize the impact of the distance and the channel parameters on the sensing result,and considers that each user has different average SNR and different decision threshold,by using General Nash Bargaining Solution (GNBS) strategy in Cooperative Game Theory (CGT),the detection performance for two-user case are derived. For multi-user case,the sensing performance is obtained with Hungarian method. Compared with the traditional schemes such as Nash Bargaining Solution (NBS) and AND,the proposed scheme covers all the factors mentioned above,and enhances the sensing rationality and reliability. Simulation results show that the proposed scheme can further improve the sensing performance and creditability.

Reputation-Based Cooperative Spectrum Sensing Algorithm for Mobile Cognitive Radio Networks

Identifying malicious users accurately in cognitive radio networks(CRNs) is the guarantee for excellent detection performance. However, existing algorithms fail to take the mobility of secondary users into consideration. If applied directly in mobile CRNs, those conventional algorithms would overly punish reliable users at extremely bad or good locations, leading to an obvious decrease in detection performance. To overcome this problem, we divide the whole area of interest into several cells to consider the location diversity of the network. Each user's reputation score is updated after each sensing slot and is used for identifying whether it is malicious or not. If so, it would be removed away. And then our algorithm assigns users in cells with better channel conditions, i.e. larger signal-to-noise ratios(SNRs), with larger weighting coefficients, without requiring the prior information of SNR. Detailed analysis about the validity of our algorithm is presented. The simulation results show that in a CRN with 60 mobile secondary users, among which, 18 are malicious, our solution has an improvement of detection probability by 0.97-d B and 3.57-d B when false alarm probability is 0.1, compared with a conventional trust-value-based algorithm and a trusted collaborative spectrum sensing for mobile CRNs, respectively.

Cooperative Spectrum Sensing Techniques in Cognitive Radio

Cognitive radio has become an effective theory to solve the inefficiency of the spectrum usage. One of the main requirements of cognitive radio systems is the ability to reliably detect the spectrum hole. Previous works on the problem of detection for cognitive radio have suggested the necessity of user cooperation to enable the detection at the low signal-to-noise ratios experienced in practical situations. This paper introduces energy sensing and the cooperative sensing techniques: AND model, OR model, counting model, double threshold model, likelihood ratio model, linear cooperation model and DWCS model. It is proved that significant cooperative gain can be achieved by the proposed models in reducing interference and improving spectrum usage.

Enhanced spectrum sensing using a combination of energy detector,matched filter and cyclic prefix

In this paper,we propose a sensing scheme based on energy detection,matched filter and cyclic prefix.Both Equal Gain Combining(EGC)and optimal combination of the aforementioned detectors are investigated in cooperative and non-cooperative spectrum sensing scenarios.In packet transmission systems such as OFDM(Orthogonal Frequency Division Multiple access)systems,the proposed scheme takes advantage of utilizing more samples than individual detectors,i.e.,cyclic prefix,training or pilot samples,and data payload samples.The proposed combine-sensing scheme offers higher detection probability and lower false alarm probability,as compared with the performance of individual detectors over the same frame duration.Simulation results are congruent with the theoretical curves and confirm the validity of our derivations.

Cooperative Spectrum Sensing Based on Centralized Double Threshold in MCN

Cooperative spectrum sensing appears popular currently due to its ability to solve the issue of hidden terminal and improve detection performance in Cognitive Radio Networks. Meanwhile, double threshold based energy detector has attracted much attention for its low computational complexity and superior performance. Motivated by this, a cooperative spectrum sensing scheme is proposed in this paper based on centralized double threshold in the maritime communication networks(MCN), where the energy value of received signal in each cognitive node is forwarded to the fusion center for final decision based on double thresholds. Additionally, the proposed scheme is further optimized for the decisions that the energy is within the scope of maximum threshold and minimum threshold. Simulation experiments verify the performance of the proposed method.

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.

SNR WALL AND COOPERATIVE SPECTRUM SENSING IN COGNITIVE RADIO UNDER NOISE UNCERTAINTY

Cooperative energy spectrum sensing has been proved effective to detect the spectrum holes in Cognitive Radio(CR).However,its performance may suffer from the noise uncertainty,which is portrayed by the SNR wall in some literatures.In this paper we analyze the spectrum sensing per-formance under noise uncertainty and find an alternative approach to obtain the SNR wall.Then the average SNR wall is proposed to illustrate the statistically average impact of noise uncertainty.In addition,the cooperative sensing performance under noise uncertainty with AND rule is discussed.Analyses and numerical results show that cooperative sensing can significantly improve the sensing performance under the condition of noise uncertainty.

Cooperative Subcarrier Sensing Using Antenna Diversity Based Weighted Virtual Sub Clustering

The idea of cooperation and the clustering amongst cognitive radios(CRs) has recently been focus of attention of research community, owing to its potential to improve performance of spectrum sensing(SS) schemes. This focus has led to the paradigm of cluster based cooperative spectrum sensing(CBCSS). In perspective of high date rate 4th generation wireless systems, which are characterized by orthogonal frequency division multiplexing(OFDM) and spatial diversity, there is a need to devise effective SS strategies. A novel CBCSS scheme is proposed for OFDM subcarrier detection in order to enable the non-contiguous OFDM(NC-OFDM) at the physical layer of CRs for efficient utilization of spectrum holes. Proposed scheme is based on the energy detection in MIMO CR network, using equal gain combiner as diversity combining technique, hard combining(AND, OR and Majority) rule as data fusion technique and antenna diversity based weighted clustering as virtual sub clustering algorithm. Results of proposed CBCSS are compared with conventional CBCSS scheme for AND, OR and Majority data fusion rules. Moreover the effects of antenna diversity, cooperation and cooperating clusters are also discussed.

Analysis of secondary throughput and optimization in cooperative spectrum sensing

In cooperative spectrum sensing, more secondary user makes more opportunity for detecting the vacant spectrums, which resulting the spectrum utilization improved, however much bandwidth would be occupied for sending the local observation results, inducing the reduced secondary throughput. In this paper, an adaptive algorithm for selecting detection threshold was proposed, where the maximized secondary throughput can always be achieved while assuring sufficient protection to primary user, regardless of the number of sensing users in cognitive radio networks. Theoretical analysis and simulation results validate the proposed scheme.

SNR-based weighted cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing （CSS） is an approach to confront fading environment. However, in conventional cooperative spectrum sensing （CCSS）, the difference among the secondary users （SUs） is ignored when SUs suffer from different fading. In this article, a signal-to-noise ratio （SNR）-based weighted spectrum sensing scheme is proposed to improve the sensing performance. The sensing performance of the weighted spectrum sensing scheme is then derived. Considering the minor contribution of the SUs with small weighted factor, a selective CSS scheme is proposed, where SUs with low SNR are not selected into cooperative spectrum sensing. The simulation results conftrm the analytical results. And the performance of weighted scheme is better than that of conventional schemes. In the case where the SNR of SUs are randomly distributed, the performance of selective scheme is almost the same as the weighted scheme while the number of cooperative SUs is reduced to save the consumption of system resource in cooperation with little additional complexity.

认知无线电中基于加权硬判决的协作频谱感知算法(英文)

Weighted one bit hard combination for cooperative spectrum sensing is proposed in this paper. Two thresholds are adopted to divide the possible energy value into three weighted regions. If the energy value falls into the corresponding region,it will be judged as "1",no information or "0". When the probability of false alarm is constrained to be constant,the objective is to maximize the probability of detection. The optimization problem is simplified by separating the weight of the middle region into several intervals. Simulation results show that the sensing performance of the proposed scheme is much better than that of the traditional one bit hard combination scheme and almost the same as that of the equal gain combination(EGC) scheme. Moreover,compared with the traditional one bit hard combination,fewer average sensing bits are required to transmit to the data fusion center with the proposed method.

Sensor node-assisted asynchronous cooperative spectrum sensing for cognitive radio network

In order to take advantage of the asynchronous sensing information, alleviate the sensing overhead of secondary users （SUs） and improve the detection performance, a sensor node-assisted asynchronous cooperative spectrum sensing （SN-ACSS） scheme for cognitive radio （CR） network （CRN） was proposed. In SN-ACSS, each SU is surrounded by sensor nodes （SNs）, which asynchronously make hard decisions and soft decisions based on the Bayesian fusion rule instead of the SU. The SU combines these soft decisions and makes the local soft decision. Finally, the fusion center （FC） fuses the local soft decisions transmitted from SUs with different weight coefficients to attain the final soft decision. Besides, the impact of the statistics of licensed band occupancy on detection performance and the fact that different SNs have different sensing contributions are also considered in SN-ACSS scheme. Numerical results show that compared with the conventional synchronous cooperative spectrum sensing （SCSS） and the existing ACSS schemes, SN-ACSS algorithm achieves a better detection performance and lower cost with the same number of SNs.

Optimal fusion rule for cooperative spectrum sensing in cognitive radio networks

In this paper, we analyze performance of cooperative spectrum sensing under counting rules when exponential model is utilized to characterize the burst nature of primary user （PU） link. Our objective is to minimize the average error probability （AEP） so that the link utilization in the considered link achieves its maximum. We derive a closed-form expression of AEP as well as the probability of interference （PoI） by classifying cognitive transmission into six events. Then, we consider the minimization of AEP over counting rules under the constraint of interference. As the solution, we develop an efficient algorithm to evaluate the optimal fusion rule. Finally, we verify our analysis in numerical results.

A Selection Scheme for Optimum Number of Cooperative Secondary Users in Spectrum Sensing

An optimization scheme for choosing the optimum number of secondary users in cooperative spectrum sensing based on the cyclostationary feature detection with Neyman-Pearson criterion is proposed in this paper.The optimal soft combination test statistic for the cooperative spectrum sensing based on cyclostationary feature detection is derived according to the generalized likelihood ratio test and its corresponding detection performance is deduced.A target function,considering two important parameters as the resource use efficiency and the number of samples employed by each cooperative secondary user in the system design,is constructed to obtain the optimum number of cooperative secondary users.It can be found that the selection scheme is to make a tradeoff between the system complexity of the cognitive radio network and the global sensing performance of the cooperative spectrum sensing.

Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of Things 5G Wireless Communication

Recently,Cooperative Spectrum Sensing(CSS)for Cognitive Radio Networks(CRN)plays a significant role in efficient 5G wireless communication.Spectrum sensing is a significant technology in CRN to identify underutilized spectrums.The CSS technique is highly applicable due to its fast and efficient performance.5G wireless communication is widely employed for the continuous development of efficient and accurate Internet of Things(IoT)networks.5G wireless communication will potentially lead the way for next generation IoT communication.CSS has established significant consideration as a feasible resource to improve identification performance by developing spatial diversity in receiving signal strength in IoT.In this paper,an optimal CSS for CRN is performed using Offset Quadrature Amplitude Modulation Universal Filtered Multi-Carrier Non-Orthogonal Multiple Access(OQAM/UFMC/NOMA)methodologies.Availability of spectrum and bandwidth utilization is a key challenge in CRN for IoT 5G wireless communication.The optimal solution for CRN in IoT-based 5G communication should be able to provide optimal bandwidth and CSS,low latency,Signal Noise Ratio(SNR)improvement,maximum capacity,offset synchronization,and Peak Average Power Ratio(PAPR)reduction.The Energy Efficient All-Pass Filter(EEAPF)algorithm is used to eliminate PAPR.The deployment approach improves Quality of Service(QoS)in terms of system reliability,throughput,and energy efficiency.Our in-depth experimental results show that the proposed methodology provides an optimal solution when directly compares against current existing methodologies.

Weight sequential log-likelihood ratio detect algorithm with malicious users removing

Due to the openness of the cognitive radio network, spectrum sensing data falsification （SSDF） can attack the spectrum sensing easily, while there is no effective algorithm proposed in current research work, so this paper introduces the malicious users removing to the weight sequential probability radio test （WSPRT）. The terminals＇ weight is weighted by the accuracy of their spectrum sensing information, which can also be used to detect the malicious user. If one terminal owns a low weight, it can be treated as malicious user, and should be removed from the aggregation center. Simulation results show that the improved WSPRT can achieve higher performance compared with the other two conventional sequential detection methods under different number of malicious users.