Cooperative Spectrum Sensing Scheme Research Articles

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive IoT networks

Sequential fusion‐based defense strategy against sophisticated Byzantine attacks in cognitive <scp>IoT</scp> networks

CSL-SFNet for Cooperative Spectrum Sensing in Cognitive Satellite Network with GEO and LEO Satellites

In a cognitive satellite network (CSN) with GEO and LEO satellites, there is a large propagation losses between the sensing satellite and the ground station. The results of spectrum sensing from a single satellite may be inaccurate, which will create serious interference in the primary satellite system. Cooperative spectrum sensing (CSS) has become the key technology for solving the above problems in recent years. However, most of the current CSS techniques are model-driven. They are difficult to model and implement in CSNs since their detection performance is strongly dependent on an assumed statistical model. Thus, we propose a novel CSS scheme, which uses convolutional neural networks (CNNs), self-attention (SA) modules, long short-term memory networks (LSTMs), and soft fusion networks, called CSL-SFNet. This scheme combines the advantages of CNNs, SA modules, and LSTMs to extract the features of the input signals from the spatial and temporal domains. Additionally, the CSL-SFNet makes use of a novel soft fusion technique that improves detection performance while also considerably reducing communication overhead. The simulation results demonstrate that the proposed algorithm can achieve a detection probability of 90% when the signal-to-noise ratio is −20 dB; it has a shorter running time and always outperforms the other CSS algorithms.

Multi-objective distributed cooperative spectrum sensing for cognitive radio networks using greedy-based coalitional game

In this work, we propose a distributed cooperative spectrum sensing (CSS) scheme for cognitive radio networks applying a greedy-based coalitional game. We formulate the cooperative spectrum sensing problem as a multi-objective optimization problem using the weighted global criteria method. The objective of the formulated problem is to jointly consider the relative impacts of the three key performance metrics in CSS: throughput gain, penalty cost, and energy overhead. By integrating these three distinct metrics into a global optimization problem, we aimed to achieve a well-balanced trade-off, essential for deploying the CSS scheme across diverse network and application environments. We prove that the formulated CSS problem is non-deterministic polynomially hard (NP-hard). The computational complexity of the formulated CSS problem is decreased by proposing a heuristic scheme that produces a sub-optimal solution in polynomial time. We develop the heuristic scheme by putting forward a greedy-based coalitional game theoretic model. The proposed scheme exploits the spatial diversity of secondary users (SUs) in forming distributed coalitions to minimize the effects of attenuation and distortion on the transmitted signal. Further sensing energy overhead is reduced by dynamically deciding the sensing duration for the SUs based on their received signal strength. To establish the stability of the coalitional game model, we carry out proof which shows that the proposed heuristic algorithm converges to a stable coalition structure. Simulation-based results show the effectiveness of the proposed scheme in terms of average detection accuracy, utility, and energy overhead compared to conventional schemes.

UAV aided virtual cooperative spectrum sensing for cognitive radio networks.

Cooperative spectrum sensing (CSS) involves multiple secondary users (SUs) reporting primary user (PU) channel sensing states to the fusion center (FC). However, the high overheads associated with multi-user CSS impose power limitations that limit its usefulness in unmanned aerial vehicle (UAV) networks. To address this challenge, we propose a virtual CSS, where a single UAV conducts CSS while following a circular flight trajectory in the air. The novelty of our approach is presenting a working frame structure for the UAV flight, including sensing and data transmission periods with further division of the sensing time into mini-sensing slots. In the virtual CSS, UAV performs local sensing decisions in each mini-slot and accumulates them for a final decision. The proposed virtual CSS scheme exploits sequential decision fusion (SDF), which sequentially adds individual mini-slot decisions. Additionally, we leverage machine learning (ML), employing AdaBoost ensembling classifier (ENC), to inspect flight conditions and reconfigure mini-slot periods dynamically for both traditional decision fusion (TDF) and our proposed SDF schemes. Furthermore, we identify an optimal decision threshold (ODT) for the proposed SDF, enabling the comparison of sequential results with an adjustable threshold through majority voting. This novel approach results in energy efficiency and improved throughput for virtual CSS using SDF, surpassing the performance of TDF, which relies on collecting entire mini-slot reports for its final decision. Simulation results demonstrate the effectiveness of the proposed SDF following the ENCODT (SDF-ENCODT) scheme compared to existing techniques from the literature. We explore varying levels of UAV flight velocities, moving radius, detection probability demand, and channel signal-to-noise ratio (SNR), reinforcing the significance of our contribution. Our research highlights the motivation to address spectrum scarcity in UAV communication by proposing an innovative virtual CSS scheme based on SDF. The proposed approach enhances spectrum utilization, overcomes power limitations, and substantially improves CSS for UAV networks.

A geodesic projection-based data fusion scheme for cooperative spectrum sensing

In this paper, a novel method based on data fusion scheme and genetic algorithm is proposed to improve the cooperative spectrum sensing (CSS) performance. Firstly, to reduce the interference of abnormal secondary users (SUs), a geodesic projection method based on information geometric framework is developed to fuse signal received by all SUs, which maps multiple SUs to a representative point on the manifold. Subsequently, the sample set is calculated by signal fusion method, and divided into training set and test set. Finally, the improved genetic algorithm based on Kullback-Leibler divergence is designed to cluster the fused signal on the manifold space, which can obtain a classifier to judge the status of primary user. Simulation results illustrate the developed CSS scheme effectiveness when compared with other CSS methods.

Graph Learning-Based Cooperative Spectrum Sensing in Cognitive Radio Networks

In cooperative spectrum sensing (CSS), received signal strengths (RSSs) of multiple secondary users (SUs) are combined to improve sensing performance. In existing CSS schemes, RSS levels are often assumed in the same order of magnitude, which, however, is not the case due to, e.g., shadowing in some scenarios, leading to performance degradation. To address this issue, in this letter, we first reveal that the expectation of a RSS matrix from SUs has the property of low rank. Then, using this property, we propose a graph learning method to select SUs of the same order of RSS levels. Specifically, a probability matrix of graph edges is used to represent the correlations of SUs, and a graph learning problem is formulated for learning the probability matrix, which is further solved by using alternating direction method of multipliers (ADMM). Then SUs with high correlations are collected for implementing CSS. Moreover, false-alarm and detection probabilities of the proposed detector are analyzed. Numerical results demonstrate the superiority of the proposed detector compared to state-of-the-art detectors.

Distributed-Satellite-Clusters-Based Spectrum Sensing with Two-Stage Phase Alignment.

We investigate a distributed-satellite-clusters (DSC)-system-based spectrum sensing, to enhance the ability for sensing weak signals. However, the spectrum-sensing performance may be significantly decreased by the phase deviations among different satellite clusters, where the deviations may be caused by the movement and the perturbation of satellites. To eliminate such a decrement, we propose a cooperative spectrum-sensing scheme in the presence of phase deviations, where the deviations are alleviated by a special two-stage phase synchronization. Specifically, the phase compensation is first performed relying on broadcasting reference signals and the ephemeris, to address the challenges of the deviations caused by the movement. Then, a two-bit feedback algorithm, having a dynamic disturbance step size, is further adopted for controlling and mitigating the deviations caused by the perturbation. Additionally, we provide the closed-form expression of the correct detection probability of the proposed spectrum-sensing scheme, using the specially derived probability density function of the sum of the shadowed-Rician random variables with independently identical distribution. Simulation results show that the proposed scheme can achieve the best spectrum-sensing performance, comparing with the traditional energy detection, eigenvalue ratio test and the generalized likelihood ratio test.

On Selection of Parameters for Cooperative Spectrum Sensing Schemes over κ–μ Fading Channels

The current paper studies comprehensive analysis of various cooperative spectrum sensing (CSS) schemes in generalized κ − μ fading environment. More precisely, the optimization of several network parameters as well as throughput performance analysis of various CSS schemes are studied. In each CSS scheme, every cognitive radio user (CRU) first senses and receives the primary user (PU) signal using sensing channels, performs an energy detection (ED) operation. Depending upon the type of the scheme, CRU makes and forwards its local binary decision or simply forwards its data to the fusion center (FC) with the help of reporting channels. Then FC implements any one of the CSS schemes for making a final decision about busy or free status on PU. The novel expressions for missed and false detection probabilities subject to CSS scheme under fading are developed. The performance of several decision and data CSS schemes through total error rate, considering different network and κ − μ fading parameters is investigated. The impact of erroneous channel conditions on each CSS scheme is investigated. The expressions for optimal system parameters of decision-based CSS schemes under fading are developed and optimal values for different network conditions are estimated. Further, the throughput performance of decision-based CSS schemes for different networks and κ − μ fading parameters values is studied.

Eigenvalue fusion-based machine learning approach for cooperative spectrum sensing in cognitive radio

Spectrum sensing is the most critical and fundamental function of the cognitive radio (CR) for dynamic spectrum usage. Machine learning (ML) techniques that allow CRs to learn the environment adaptively are most essential in spectrum sensing. This paper proposes a novel machine learning-based cooperative spectrum sensing (CSS) technique using various advanced ML schemes to enhance the probability of detection. ML classifiers used in the fusion centre of the CSS schemes are trained with eigenvalue-based test statistics extracted from the covariance matrix of a received signal. The proposed system recognises the received signal samples as a PU signal or a noise signal and distinguishes the PU signal from noise effectively under low SNR conditions using a threshold that possesses the self-learning ability. The simulation results exhibit the performance analysis of various ML algorithms for CSS under different wireless scenarios and their suitability is compared with conventional approaches.

Spectrum Sensing Under Illegal Spectrum Access Behaviors in Multiple Authorized Users Scenario

In this paper, we detect illegal spectrum access behaviors in the dynamic spectrum sharing of the cognitive wireless networks, in which multiple authorized users (AUs) work in the same channel. First, we detect whether there are users occupy the channel, and if the channel is occupied, then recognize whether there is the illegal user (IU) and infer the number of AUs roughly. In the light of the thorny challenge that legal spectrum utilization behaviors and illegal spectrum access behaviors coexist probabilistically, the spectrum sensing problem is formulated as a mathematical model of ternary hypothesis test. Moreover, when multiple AUs are working at the same time, it is extremely difficult to detect IU. To tackle the problem, taking into account the various potential combinations of AUs and IU, we exploit a two-step detector and derive related detection schemes based on the generalized likelihood ratio test (GLRT), the Rao test, and the Wald test following the generalized multi-hypothesis Neyman-Pearson (GMNP) criterion respectively. Additionally, we design a cooperative spectrum sensing (CSS) scheme on the basis of the global GMNP criteria, in which the distributed detection framework composed of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> sensing nodes and a fusion center (FC). Finally, to verify the performance of the proposed detection scheme in a variety of parameter configurations, we provide comprehensive simulations and find that there will be at most <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$({{2^{N}}-1})$ </tex-math></inline-formula> detection valleys when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${N}$ </tex-math></inline-formula> AUs work at the same time. Compared with single-sensing node detection, the CSS scheme can significantly improve the detection performance of the proposed detection schemes, especially in the upper bound and valley of the detection performance.

Differential Evolution Based Machine Learning Scheme for Secure Cooperative Spectrum Sensing System

The secondary users (SUs) in cognitive radio networks (CRNs) can obtain reliable spectrum sensing information of the primary user (PU) channel using cooperative spectrum sensing (CSS). Multiple SUs share their sensing observations in the CSS system to tackle fading and shadowing conditions. The presence of malicious users (MUs) may pose threats to the performance of CSS due to the reporting of falsified sensing data to the fusion center (FC). Different categories of MUs, such as always yes, always no, always opposite, and random opposite, are widely investigated by researchers. To this end, this paper proposes a hybrid boosted tree algorithm (HBTA)-based solution that combines the differential evolution (DE) and boosted tree algorithm (BTA) to mitigate the effects of MUs in the CSS systems, leading to reliable sensing results. An optimized threshold and coefficient vector, determined against the SUs employing DE, is utilized to train the BTA. The BTA is a robust ensembling machine learning (ML) technique gaining attention in spectrum sensing operations. To show the effectiveness of the proposed scheme, extensive simulations are performed at different levels of signal-to-noise-ratios (SNRs) and with different sensing samples, iteration levels, and population sizes. The simulation results show that more reliable spectrum decisions can be achieved compared to the individual utilization of DE and BTA schemes. Furthermore, the obtained results show the minimum sensing error to be exhibited by the proposed HBTA employing a DE-based solution to train the BTA. Additionally, the proposed scheme is compared with several other CSS schemes such as simple DE, simple BTA, maximum gain combination (MGC), particle swarm optimization (PSO), genetic algorithm (GA), and K-nearest neighbor (KNN) algorithm-based soft decision fusion (SDF) schemes to validate its effectiveness.

Cooperative Spectrum Sensing Based on Convolutional Neural Networks

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

Efficient Approximations for Optimization of N-Out-of-K Rule for Heterogeneous Cognitive Radio Networks

Spectrum sensing (SS) has attracted much attention due to its important role in the improvement of spectrum efficiency. However, the limited sensing time leads to an insufficient sampling point due to the tradeoff between sensing time and communication time. Although the sensing performance of cooperative spectrum sensing (CSS) is greatly improved by mutual cooperation between cognitive nodes, it is at the expense of computational complexity. In this paper, efficient approximations of the N-out-of-K rule-based CSS scheme under heterogeneous cognitive radio networks are provided to obtain the closed-form expression of the sensing threshold at the fusion center (FC), where the false alarm probability and its corresponding detection probability are approximated by the Poisson distribution. The computational complexity required to obtain the optimal sensing threshold at the FC has greatly decreased and theoretical derivations state that the approximation error is negligible. The simulations validate the effectiveness of the proposed scheme.

Improved likelihood ratio statistic‐based cooperative spectrum sensing for cognitive radio

Cooperative spectrum sensing (CSS) is a technique where multiple cognitive radio users cooperate among themselves to make binary decisions about the presence of a primary user. The single cognitive user often faces the hidden terminal problem. However, CSS tackles this problem by sending local sensing-based decisions to the fusion centre. A major drawback of conventional energy detection is the poor performance at low SNR regime. In this work, likelihood ratio statistics is considered as a test-statistic due to its highest statistical power. An improved likelihood ratio statistic-based CSS scheme is proposed by considering several past sensing events. The proposed scheme mitigates the poor detection at low SNR regime and misdetections arising due to sudden drops in signal energy. Furthermore, the generalised Byzantine attack is taken into account considering a security aspect. The proposed scheme is also shown to outperform Anderson Darling-based malicious user detection in CSS at a low SNR regime. The proposed scheme is verified and validated over empirical spectrum data. The performance improvement is at the cost of computational time, which in practice is very low and is justified by the significant performance improvements of the proposed scheme at low SNR regime.

Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks

Cooperative spectrum sensing is a key technology of cognitive radio networks (CRNs) to the reliability of spectrum sensing but is prone to be affected by a series of user characteristics, which results in a serious decline in the throughput of CRNs and the harmful interference to the primary user network. The performance analysis and optimisation of existing cooperative spectrum-sensing schemes do not completely cover key user characteristics. In this study, the joint effects of key user characteristics are studied with the objective to determine the parameters that affect the cooperative spectrum-sensing functionality. To this aim, the mobile CRN model and spatial–temporal spectrum-sensing model are formulated. Furthermore, the authors propose a dynamic double threshold energy detection (DDTED) scheme to derive the miss-detection probability and false alarm probability involving user characteristics. Moreover, the performance optimisation is carried out by a dynamic threshold factor. Finally, simulation results show that a variety of user characteristics have different effects on the performance of cooperative spectrum sensing, and the proposed DDTED scheme can achieve better performance than the existing approach.

Enhanced Multiband Multiuser Cooperative Spectrum Sensing for Distributed CRNs

Multi-band cooperative spectrum sensing (CSS) can provide opportunistic spectrum access to secondary users (SUs) in cognitive radio networks (CRNs). In multi-band CSS, the sensing task is divided among SUs based on their capability, residual energy, channel conditions, etc. However, SUs’ scheduling to sense a subset of channels in distributed CRNs is challenging mainly because of the lack of a central entity and the changing conditions due to new SUs entering the network. To address these challenges, we propose a two-stage multi-band multi-user CSS (M2CSS) scheme to assign a subset of channels to SUs for spectrum sensing. We first formulate an optimization problem to choose a leader for each channel. We then formulate an optimization problem to select cooperative SUs for each channel such that SUs with similar sensing information for the same channel are not selected. Further, we propose an enhanced M2CSS (EM2CSS) scheme to allow new SUs to participate in the sensing process. We formulate an optimization problem to assign multiple channels to joining SUs. We then distribute the sensing assignments among existing and joining SUs to minimize sensing energy consumption. Extensive simulation results show the efficacy of the proposed schemes when compared to existing schemes.

Scalable Learning-Based Heterogeneous Multi-Band Multi-User Cooperative Spectrum Sensing for Distributed IoT Systems

The emerge of Internet of Things (IoT) brings up revolutionary changes to wireless communications. Cognitive radio (CR) can be seen as one of the prominent solutions to spectrum scarcity in IoT, where multi-band cooperative spectrum sensing (CSS) is the key. However, lack of centralized control and increase in number of devices place a room for many challenges. One of the main challenges is secondary users' (SUs') scheduling to sense a subset of channels in heterogeneous distributed CR networks (CRNs). To overcome the aforementioned challenge, in this paper, we propose a novel heterogeneous multi-band multi-user CSS (HM2CSS) scheme. The proposed scheme allows heterogeneous SUs to sense multiple channels and consists of two stages. We formulate a mathematical model to optimize leader-selection for each channel in the first stage. We then formulate another optimization problem to determine corresponding cooperative SUs to sense these channels in the second stage. After that, diffusion learning is used to decide on the availability of channels. Simulations illustrate that the proposed scheme improves detection performance and CRN throughput, is scalable in terms of detection performance, and provides fair energy consumption for CSS on all channels compared to existing multi-band CSS schemes.

Energy-Efficient Cooperative Spectrum Sensing Scheme Based on Spatial Correlation for Cognitive Internet of Things

Due to the negative impact from spatial correlation, spatially correlated cognitive radio (CR) based devices participating in cooperative spectrum sensing may be harmful to the detection performance. In this paper, we propose an energy-efficient cooperative spectrum sensing scheme based on spatial correlation for cognitive Internet of Things (CIoT). To mitigate the communication overhead and ensure sufficient sensing accuracy, the CR-based devices (CRDs) can be grouped into several clusters. The member nodes undertake cooperative spectrum sensing tasks in turn, and send the local test statistic to their cluster head nearby. Then, by exploiting the spatial correlation of the members, the cluster head combines the sensing results and makes use of likelihood ratio test to obtain the cluster decision. After receiving the decisions from all clusters, the fusion center employs hard fusion scheme to make the final decision about spectrum occupancy. The simulation results show that our scheme not only provides the better sensing performance, but also improve the energy efficiency.

The Design and the implementation of a robust scheme to combat the effect of malicious nodes in Cognitive Radio Ad Hoc networks

Cognitive radio network, which enables dynamic spectrum access, addresses the scarcity of radio spectrum caused by ever-increasing demand for spectrum. Cognitive radio technology ensures the efficient utilisation of underutilised licenced spectrum by secondary users (SU). Secondary users, sense the radio environment before utilising the available spectrum to avoiding signal interference. The SU cooperatively sense the spectrum to ensure global view of the network. unfortunately, cooperative sensing is vulnerable to Byzantine attacks whereby SU falsify the spectrum reports for selfish reasons. Hence, this study proposes the implementation of a scheme to combat the effects of Byzantine attack in cognitive radio network. The proposed scheme, known as the extreme studentized cooperative consensus spectrum sensing (ESCCSS), was implemented in an ad hoc cognitive radio networks environment where the use of a data fusion centre (DFC) is not required. Cognitive radio nodes perform their own data fusion making spectrum access decisions. They fuse their own reports with reports from neighbouring nodes. To evaluate the performance of our scheme and its effectiveness in combating the effect of byzantine attack, comparative results are presented. The comparative results show that the ESCCSS outperformed the Attack-Proof Cooperative Spectrum Sensing scheme.

Spectrum sensing in cognitive vehicular networks for uniform mobility model

Cooperation among the sensing nodes in cognitive radio (CR) networks improves spectrum sensing (SS) reliability. However, determining the efficient set of secondary users (SUs) to be involved in cooperative SS (CSS) becomes challenging in vehicular networks (VNs) due to the mobility of primary user (PU) and/or SU nodes. This work explores an energy detection-based CSS scheme in CR enabled VNs (CR-VNs) considering the uniform velocity motion of PU and SU nodes. A distance-dependent distribution function is developed first to find the probability of a SU resides in a specific coverage of the PU transmission zone. This distribution function is then used in deriving the expression of the probability of detection and probability of false alarm using majority rule at the fusion centre. The efficacy of the mathematical analysis is studied through a large set of simulation results that highlight the enhanced SS performance in terms of the probability of detection by ∼ 2.11 , and ∼ 6.56 % at uniform velocity over the existing works in CR-VN.