Cooperative Spectrum Sensing Scheme Research Articles

A Strong Machine Learning Classifier and Decision Stumps Based Hybrid AdaBoost Classification Algorithm for Cognitive Radios.

Machine learning (ML) based classification methods have been viewed as one kind of alternative solution for cooperative spectrum sensing (CSS) in recent years. In this paper, ML techniques based CSS algorithms are investigated for cognitive radio networks (CRN). Specifically, a strong machine learning classifier (MLC) and decision stumps (DS) based adaptive boosting (AdaBoost) classification mechanism is proposed for pattern classification of the primary user’s behavior in the network. The conventional AdaBoost algorithm only combines multiple sub-classifiers and produces a strong weight based on their weights in classification. Taking into account the fact that the strong MLC and the weak DS serve as different sub-classifiers in classification, we propose employing a strong MLC as the first-stage classifier and DS as the second-stage classifiers, to eventually determine the class that the spectrum energy vector belongs to. We verify in simulations that the proposed hybrid AdaBoost algorithms are capable of achieving a higher detection probability than the conventional ML based spectrum sensing algorithms and the conventional hard fusion based CSS schemes.

Sensing Coverage-Based Cooperative Spectrum Detection in Cognitive Radio Networks

With the advancement of the Internet of Things (IoT), conventional principles of spectrum allocation cannot mitigate spectrum depletion so that a cognitive radio technology is proposed as a solution. The hidden primary user (PU) problem, however, is a critical issue in cognitive radio networks, because spectrum sensing nodes (SNs) can misclassify spectrum occupancy. To cope with this, machine learning-based cooperative spectrum sensing schemes (CSSs) have been proposed. The CSSs without considering the node placement, however, are still faced with the hidden PU problem. In this paper, we present how to place SNs to guarantee the performance of machine learning-based CSSs. We verify that the hidden PU problem makes the overlap of data distribution, which deteriorates the spectrum sensing ability. Based on the Kullback–Leibler divergence, analytical expressions for the spectrum sensing coverage of a single SN are derived. Then, we propose a strategy on how to place a few SNs to cover the whole area of the PU and prove the feasibility of our proposal by the experiment results.

Bhattacharyya distance criterion based multibit quantizer design for cooperative spectrum sensing in cognitive radio networks

Cooperative spectrum sensing (CSS) is crucial for dynamic spectrum access in cognitive radio networks. This paper considers a CSS scheme by using a multilevel quantizer in each sensing node (SN) to quantize the local energy detector’s observation. A log-likelihood ratio test detector by using quantized data received from each SN is proposed to determine the presence or absence of the primary user signal. The Bhattacharyya distance (BD) of the cooperative sensing system is derived. Then, a quantizer design scheme by maximizing the BD that as the criterion function in an optimization problem with respect to the quantization thresholds is proposed. As nonlinear and high-dimensional of the objective function, a particle swarm optimization algorithm is employed to solve operating parameters for the quantizer. Furthermore, we derive the upper bound performance on cooperative energy detection (CED) in CSS. To validate the effectiveness of the proposed approach, The quantizer is compared with other schemes in terms of detection performance. Simulation results show that 2- or 3-bit quantization of the proposed approach achieves comparable performance to upper bound of CED without quantization.

Decision‐fusion‐based reliable CSS scheme in CR networks

Cognitive radio (CR) is a rapidly growing technology that can be employed to effectively utilise the radio spectrum. The detection accuracy of the CR user is compromised when a network is under degrading conditions like fading and shadowing effects. Cooperative spectrum sensing (CSS) has been extensively employed to overcome these issues. In CSS, all secondary users communicate with the fusion centre (FC) to share the PU information. Hard fusion schemes are applied to make a correct decision about PU presence at FC; however, such approaches lack reliable decision-making. Hence, there is a need to construct more accurate and reliable fusion schemes. In this study, a neural network (NN)-based decision fusion scheme at FC is used to construct a reliable decision. In the proposed scheme, NN is used as a machine learning tool that is trained using input data and output data to generate the desired fusion model. It has been evaluated through simulation results that the proposed fusion scheme sensing accuracy is much better as compared to conventional fusion schemes and other state-of-the-art schemes proposed in the literature. Further, the proposed fusion scheme is tested using the clustering approach to make it more reliable and accurate.

A Cooperative Spectrum Sensing Scheme Based on Compressive Sensing for Cognitive Radio Networks

A Cooperative Spectrum Sensing Scheme Based on Compressive Sensing for Cognitive Radio Networks

Hybrid cooperative spectrum sensing scheme based on spatial–temporal correlation in cognitive radio enabled VANET

Cognitive radio enabled vehicular ad-hoc networks (CR-VANETs) are one of the promising architectures in the future vehicular ad-hoc networks. In this study, the joint spatial-temporal correlation is exploited to improve decision accuracy of cooperative spectrum sensing (CSS) while reducing overhead introduced by cooperation in the CR-VANETs. Firstly, a theoretical method is presented to analyse the impact of spatial-temporal correlation on cooperative sensing performance when using soft combining in the CR-VANET. Then, the expression of the optimal probability of detection with respect to spatial-temporal correlation is given for a target probability of false alarm by employing likelihood ratio test. Additionally, the user selection problem is formulated as an efficient double-threshold optimisation problem by considering both sensing accuracy and stability to achieve the optimal probability of detection. Finally, a hybrid CSS scheme based on spatial-temporal correlation is designed for the CR-VANET. Simulation results reveal that the proposed scheme could achieve significant sensing performance gain by selecting a subset of secondary users for combination, and could reduce user selection frequency by employing spatial-temporal diversity.

Biologically Inspired Cooperative Spectrum Sensing Scheme for Maritime Cognitive Radio Networks

Spectrum sensing is imperative to the success of maritime cognitive radio networks (MCRNs). Spectrum sensing in MCRNs is challenging because of the sea surface movement, the channel interference, and the unstable link quality. Recent research reveals that existing spectrum sensing schemes work well for lower sea states; however, they have failed to perform effectively at higher sea states. There are two disadvantages of performing spectrum sensing at higher sea states: 1) low probability of detection may also cause interference with primary system and 2) energy wastage. In this paper, a biologically inspired cooperative spectrum sensing scheme (BIC3S) is proposed to deal with the reliability and energy consumption challenges associated with the sea environment. It is based on the task allocation model of an insect colony. The proposed BIC3S chooses participating secondary users for cooperative spectrum sensing according to their given sea state. Further, it enables the secondary users to decide whether or not to perform spectrum sensing based on their sea state. Simulation results are presented to demonstrate the performance of the proposed scheme in terms of energy consumption, detection performance, and throughput. It is shown that the proposed BIC3S consumes less energy and, at the same time, achieves higher detection probability and fewer probability of false alarms compared to the existing schemes. Moreover, BIC3S provides better adaptation capabilities for the sea environment.

Cooperative Spectrum Sensing Using Quantized Energy Statistics in the Absence of Dedicated Reporting Channel

Reporting channels are essential for implementing cooperative spectrum sensing (CSS) in cognitive radio technology. In this paper, a novel decentralized CSS scheme capable of operating in the absence of dedicated reporting channels is presented. Existing schemes which operate in the absence of dedicated reporting channels rely on the transmission of hard decision bits from cooperating secondary users (CSUs). The proposed scheme supports a mobile spectrum sensor termed as the leading secondary sensor node (LSSN) to assist a contending secondary transmitter in spectrum sensing. Unlike existing schemes, the proposed CSS framework offers the freedom of using multiple bits for transmitting a quantized version of the test statistics between secondary user (SU) nodes. In order to reduce energy consumption and system complexity, the CSUs do not take part in sensing but only act as relays for retransmitting the quantized test statistics from LSSN to the contending SU transmitter. The proposed network protocol also assists the SU transmitters in avoiding collisions with each other. Closed form expressions for the probability of false alarm and detection have been derived. Monte-Carlo simulations are used to validate the analytical expressions. The proposed scheme can outperform the recently proposed best-selection reporting scheme when the number of CSUs is less and the contending CSUs and the SU transmitter lie in an area of poor primary signal reception.

Robust Cooperative Spectrum Sensing Based on Adaptive Reputation and Evidential Reasoning Theory in Cognitive Radio Network

In this paper we investigate the problem of cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs) in the presence of malicious attacks from secondary users (SUs). We propose a novel CSS approach based on a new adaptive reputation and evidential reasoning theory (ERT) to detect the malicious SUs, to strengthen the sensing ability, and to keep good stability and generalization ability. Different from the previous work, we assume that the fusion center has no prior information of the radios in CRNs except the condition that the honest SUs are in majority. Specifically, to counter different attack strategies, we present an adaptive reputation which fully considers the signal-to-noise ratio, historical sensing data information, and current local sensing results via an energy detection method. The proposed adaptive reputation can effectively identify the malicious SUs when the CRNs are with single-attack or multiple-attack models. Furthermore, we present a CSS scheme via the proposed adaptive reputation and ERT on the basis of local sensing results derived from the energy detection method. The new CSS scheme has good robustness and generality and can decrease the computational complexity and overcome the evidence conflict problems of Dempster–Shafer theory-based CSS schemes. Extensive simulations are carried out to demonstrate the performance and the superiority of the proposed approach in contrast with previous methods.

Energy-Efficiency Joint Cooperative Spectrum Sensing and Power Allocation Scheme for Green Cognitive Radio Network: A Soft Decision Fusion Approach

This paper focus on a jointly spectrum sensing parameter and energy efficiency (EE) optimization problem in OFDMA CRN system for enabling Green Communication. In this perspective, we firstly propose an algorithm to choose less spatially-correlated cognitive users to reduce the shadowing effect in wireless network. Furthermore, based on Lagrangian duality theorem with the aid of parametric transformation, the algorithm called an Iterative Dinkelbach Scheme (IDS) is proposed to optimize both transmission power allocation and sensing duration of the cognitive users (Cus) for maximizing Energy Efficiency under the constraints of overall outage of cognitive network, interference to the PU, maximum transmission power and minimum data rate requirement. Numerical result proves the effectiveness of our proposed algorithm. Compared with existing schemes, our proposed scheme outperforms in enhancing the EE with the same parameters.

MAC Based Energy Efficiency in Cooperative Cognitive Radio Network in the Presence of Malicious Users

In cognitive radio networks, cooperative spectrum sensing (CSS) is generally adopted for improving spectrum sensing accuracy to increase spectrum utilization and avoid interference with the primary users. However, some malicious secondary users (SUs) may affect the CSS performance by inducing false observation bits for fusion. The message authentication code (MAC) is a promising technique to avoid the damage from the spectrum sensing data falsification (SSDF) attacks. In this paper, as both the more spectrum sensing nodes and the MAC reporting bits result in extra energy consumption, we propose an energy efficiency model to capture the effects of the length of MAC and the number of cooperative SUs under independent and collaborative SSDF attacks, respectively, and analyze the existence of the optimal length of MAC and the optimal number of cooperative SUs that can achieve the maximum value of energy efficiency, respectively. Simulation results are provided to show that the CSS scheme based on MAC can resist SSDF attacks and the accuracy of the theoretical analysis is also validated.

Optimal Number of Cooperators in the Cooperative Spectrum Sensing Schemes

In this paper, considering a cognitive radio (CR) network, we propose a hard combining cooperative sensing scheme that embeds a solution of finding optimal number of users who can participate in user cooperation. The solution to our scheme includes two cases, one when single antenna is used at the CR receiver, and the other, when multiple antennas are employed. Moreover, we have derived the closed-form expression for Bayes risk, which is a measure of probability of error. Bayes risk constitutes false alarm and missed detection probabilities. We have found optimum number of users, who can participate in the fusion scheme, by minimising the probability of error. Our simulation results show the improvement in receiver operating characteristics curve, when optimum number of users are allowed to participate in the fusion scheme.

Performance Comparison of Cognitive Radio Sensor Networks for Industrial IoT With Different Deployment Patterns

Square lattice (SL), triangle lattice (TL), and hexagon lattice (HL) are widely used regular deployment patterns for industrial Internet of Things (IoT). However, the performance of cognitive-radio-based access with these three deployment patterns has not been explored yet. This paper first designs a transmission scheduling method named cognitive access for regular topology (CART) in cognitive radio sensor networks (CRSNs) with minimal occupation of channels and high transmission efficiency. CART consists of a timeslot-and-channel allocation scheme, a cooperative spectrum-sensing scheme, and a scheme for reporting spectrum-sensing results (SSRs) in SL, TL, and HL. This paper also analyzes CART’s cooperative spectrum-sensing performance, reception bandwidth, and transmission delay for different transmission interference range and coverage patterns. Based on numerical analysis and simulation, this paper makes comparison between CART and optimal transmission scheduling method for single channel, and that among these three deployment patterns. Results show the following insights for efficient deployment of CRSN: 1) TL yields the optimal cooperative spectrum-sensing performance among three patterns by data fusion rule of 3-out-of-7; 2) under the same conditions and area for deployment, SL provides the optimal reception bandwidth; 3) SL and TL lead to low transmission delay for critical complete coverage; 4) SL leads to the lowest transmission delay for critical multiple coverage; 5) the false alarm (FA) probability of individual node has little influence on transmission delay, while the probability of primary user’s (PU) occurrence has noticeably influence on it; and 6) increasing the ratio of transmission timeslot to spectrum-sensing timeslot may decrease transmission delay

History based forward and feedback mechanism in cooperative spectrum sensing including malicious users in cognitive radio network.

In cognitive radio communication, spectrum sensing plays a vital role in sensing the existence of the primary user (PU). The sensing performance is badly affected by fading and shadowing in case of single secondary user(SU). To overcome this issue, cooperative spectrum sensing (CSS) is proposed. Although the reliability of the system is improved with cooperation but existence of malicious user (MU) in the CSS deteriorates the performance. In this work, we consider the Kullback-Leibler (KL) divergence method for minimizing spectrum sensing data falsification (SSDF) attack. In the proposed CSS scheme, each SU reports the fusion center(FC) about the availability of PU and also keeps the same evidence in its local database. Based on the KL divergence value, if the FC acknowledges the user as normal, then the user will send unified energy information to the FC based on its current and previous sensed results. This method keeps the probability of detection high and energy optimum, thus providing an improvement in performance of the system. Simulation results show that the proposed KL divergence method has performed better than the existing equal gain combination (EGC), maximum gain combination (MGC) and simple KL divergence schemes in the presence of MUs.

Cooperative Spectrum Sensing With M-Ary Quantized Data in Cognitive Radio Networks Under SSDF Attacks

In this paper, we address the challenging and important cooperative spectrum sensing (CSS) problem with M-ary quantized data under spectrum sensing data falsification (SSDF) attacks. We introduce a probabilistic SSDF attack model to characterize the attacks by a malicious secondary user (SU). We analyze the attack behavior and derive the condition to nullify the detection capability of the fusion center (FC). To defend against the SSDF attacks, we propose a novel attack-proof CSS scheme with M-ary quantized data, mainly including a malicious SU identification method and an adaptive linear combination rule. By using the malicious SU identification approach, FC identifies malicious SUs and removes them from the data fusion process. The adaptive linear combination rule adjusts the weighted coefficients with the distribution parameter sets of identified normal SUs estimated using a maximum likelihood-based estimator. FC performs the spectrum sensing process with M-ary quantized data from the identified normal SUs. Comprehensive evaluation is conducted. Evaluation results show that the proposed malicious SU identification method can remove malicious SUs successfully and the proposed CSS scheme with M-ary quantized data is robust against the SSDF attacks.

Group-Based Multibit Cooperative Spectrum Sensing for Cognitive Radio Networks

In cooperative spectrum sensing (CSS), a multibit combination rule shows better sensing performance than one-bit hard combination rules at the sacrifice of the reporting overhead. To overcome the tradeoff between the sensing performance and the reporting overhead, we propose a novel group-based multibit CSS scheme with a limited reporting overhead. The proposed scheme adopts contention-based reporting to restrain the reporting overhead while achieving multiuser diversity with an increased number of secondary users (SUs). Moreover, SUs report one-bit sensing results to a fusion center instead of sending multibit quantization information, and the rest of the information is embedded in the time slot. The simulation results demonstrate that, as the number of SUs increases, the proposed scheme improves the sensing performance and the average throughput of SUs, whereas the conventional one-bit or multibit combination schemes show a tradeoff between the sensing performance and the throughput of SUs.

Analytical performance of soft data fusion‐aided spectrum sensing in hybrid terrestrial‐satellite networks

SummaryThe cognitive radio (CR) technology is developed to help secondary users to utilize spectrum holes of the primary users (PUs) opportunistically. Accurate detection of spectrum holes via cooperative spectrum sensing (CSS) is essential for realization of CR protocols in next generation hybrid terrestrial‐satellite networks. Soft data fusion refers to a class of efficient CSS schemes for CR networks to find presence or absence of a PU. Each CR user senses the PU and transmits its sensing information in terms of energy obtained using energy detector to fusion center (FC). Next, any of the soft data schemes, which differ in technique of combining the energy information, may be performed at FC to acquire a final decision about a PU. In the present paper, numerical analysis of various soft data‐aided CSS schemes in different fading/shadowing environments is studied. In particular, closed‐form expressions of detection probability for the known soft data schemes are derived for each of the popular fading models. A comparative performance of soft combining schemes is illustrated for various network and channel conditions. Further, an optimal detection threshold that minimizes the total error is also indicated for different soft schemes. Copyright © 2016 John Wiley & Sons, Ltd.

A new joint eigenvalue distribution of finite random matrix for cognitive radio networks

A new joint eigenvalue distribution (JED) based on dual extreme eigenvalues of finite random matrix is proposed in this study. Different from conventional JED based on K(K ≥ 2) variables, only two variables are included in the proposed formulation. The upper and lower bounds of the new JED are determined. The new JED provides a simple and efficient way to deduce the distributions of key characteristics of finite random matrix, such as the extreme (largest and smallest) eigenvalues, standard condition number, and scaled largest eigenvalue. Moreover, a novel cooperative spectrum sensing (CSS) scheme based on the new JED is proposed for cognitive radio networks. The simulation results verify the proposed JED and the proposed CSS scheme can improve sensing performance.

Cooperative spectrum sensing in the presence of primary user emulation attack in cognitive radio network: multi-level hypotheses test approach

The inherent nature of cognitive radio (CR) networks has brought new threats to wireless communications. Primary user emulation attack (PUEA) has been widely studied as a serious threat to cooperative spectrum sensing (CSS) in CR networks. In PUEA, a malicious user can obstruct CR users from accessing idle frequency bands by imitating licensed primary user (PU) signal characteristics. The present study introduces a new CSS scheme in the presence of a malicious PUEA based on multi-level hypothesis testing (MLHT). In the proposed method, generalizing from binary hypothesis testing to MLHT, we partition the decision space to four decision options and apply minimum Bayes cost criteria to determine the channel status. We also discuss practical limitation issues that need to be considered when applying the MLHT approach. Simulation results are provided to indicate the performance improvement of the proposed MLHT method against PUEA, compared with the conventional method.

Cumulative Cooperative Spectrum Sensing Scheme to Defend Against Selfish Users

Background/Objectives: This paper considers combatting the jamming and selfish attacks in Cognitive Radio (CR) networks operating at the white (unused) and grey (underused) spaces in the RF spectrum. CR facilitates effective utilization of RF spectrum resources but still vulnerable to various network attacks. Methods/Statistical Analysis: This study considered the jamming and selfish type of attack detection and to defend against them because of its severity. An Intrusion Detection System (IDS) is formed to combat the against jamming and selfish type of attacks in CR. Findings: In this system cumulative-sum algorithm is used, which incorpates the control centre information over the channel. The cumulative-sum based algorithm shows improved performance in provisioning better packet delivery ratio, throughput and minimum energy and packet drop. Further, this algorithms works better for more number of Secondary Users (SU), and the Unlicensed Users. Applications/Improvements: This work can be enhanced further by simulating under various network attacks and in the presence of numerous SU.