Cooperative Spectrum Sensing Technique Research Articles

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.

Performance enhancement in clustering cooperative spectrum sensing for cognitive radio network using metaheuristic algorithm

Spectrum sensing describes, whether the spectrum is occupied or empty. Main objective of cognitive radio network (CRN) is to increase probability of detection (Pd) and reduce probability of error (Pe) for energy consumption. To reduce energy consumption, probability of detection should be increased. In cooperative spectrum sensing (CSS), all secondary users (SU) transmit their data to fusion center (FC) for final measurement according to the status of primary user (PU). Cluster should be used to overcome this problem and improve performance. In the clustering technique, all SUs are grouped into clusters on the basis of their similarity. In cluster technique, SU transfers their data to cluster head (CH) and CH transfers their combined data to FC. This paper proposes the detection performance optimization of CRN with a machine learning-based metaheuristic algorithm using clustering CSS technique. This article presents a hybrid support vector machine (SVM) and Red Deer Algorithm (RDA) algorithm named Hybrid SVM–RDA to identify spectrum gaps. Algorithm proposed in this work outperforms the computational complexity, an issue reported with various conventional cluster techniques. The proposed algorithm increases the probability of detection (up to 99%) and decreases the probability of error (up to 1%) at different parameters.

Optimized Statistical Beamforming for Cooperative Spectrum Sensing in Cognitive Radio Networks

In cognitive radio (CR), cooperative spectrum sensing (CSS) employs a fusion of multiple decisions from various secondary user (SU) nodes at a central fusion center (FC) to detect spectral holes not utilized by the primary user (PU). The energy detector (ED) is a well-established technique of spectrum sensing (SS). However, a major challenge in designing an energy detector-based SS is the requirement of correct knowledge for the distribution of decision statistics. Usually, the Gaussian assumption is employed for the received statistics, which is not true in real practice, particularly with a limited number of samples. Another big challenge in the CSS task is choosing an optimal fusion strategy. To tackle these issues, we have proposed a beamforming-assisted ED with a heuristic-optimized CSS technique that utilizes a more accurate distribution of decision statistics by employing the characterization of the indefinite quadratic form (IQF). Two heuristic algorithms, genetic algorithm with multi-parent crossover (GA-MPC) and constriction factor particle swarm-based optimization (CF-PSO), are developed to design optimum beamforming and optimum fusion weights that can maximize the global probability of detection pd while constraining the global probability of false alarm pf to below a required level. The simulation results are presented to validate the theoretical findings and to asses the performance of the proposed algorithm.

Performance Comparison of Energy Detection Based non – Cooperative Spectrum Sensing Techniques in Cognitive Radio

Cognitive radio is a ground-breaking software-defined radio paradigm that offers Dynamic spectrum access, allowing secondary users to use the frequency band allotted to the principal user when it is not in use and vacate when the prime application returns. The ability to sense the spectrum is critical to cognitive radio's efficiency. Energy detection sensing is the simplest and most often used spectrum sensing approach, owing to its ease of implementation in cognitive radio applications. The three-energy detection-based algorithms adopted for different scenarios have been compared in this study. The algorithms include the double-threshold energy detection, adaptive single threshold energy detection, and the adaptive double threshold spectrum sensing algorithm. Since the noise prediction in the practical situation is difficult, the necessity is to find the best algorithm in this condition. The other equally important parameters for efficiently sensing the spectrum are spectrum efficiency and less interference to the primary user. Simulation findings show that the adaptive double threshold approach outperforms the other two algorithms in all respect. The detection probability of the method is typically found to be substantially greater as compared to other two techniques. In addition, the likelihood of a false alarm is significantly reduced. Furthermore, when the signal-to-noise ratio value is low, often below -5dB, the performance of this approach is poor. MATLAB is used to run all of the simulations.

A Study of Cognitive Radio Sensing Techniques for Optimum Spectrum Utilization

Background: The spectrum scarcity plays a vital role in wireless communications. We are in the situation to use it through an efficient methodology. Objective: To identify the holes in the spectrum through an efficient spectrum sensing and to allocate the bands to the unlicensed users (Secondary Users- SU). Methods: It has been proposed to make a comparative study among the existing spectrum sensing methods based on the following parameters such as Probability detection (Pd) measurement, Algorithm, Decision fusion method, Network model. Results: Comparative study has been made to find the pros and cons of the existing techniques with their limitations and the field of application. CSS (Cooperative Spectrum Sensing) technique significantly consumes less energy and reporting time to FC (Fusion Centre) since it utilizes LLR (Log Likelihood Ratio) Method to find the Probability of detection using Chair-varshney rule in local sensing with parallel report approach in the cluster based network. Conclusion: Through the study and the comparison of parameters in literature, it is found that CSS provide better detection. Therefore, this technique can be considered as an efficient technique to find the holes and to share the frequency with SU’s.

Cooperative Spectrum Sensing Using Extreme Learning Machines for Cognitive Radio Networks

In this article, a technique for cooperative spectrum sensing (CSS) using the Extreme Learning Machine (ELM) is proposed. ELMs are feedforward neural networks where the hidden layer parameters are not tuned, and only output weights are optimized. The simulations were done for both the fading and non-fading environments. Different combination of activation function and weight initialization scheme is deployed for calculating channel occupancy detection. The obtained results are compared with popular fusion schemes and well-known Machine Learning (ML) techniques. Further, the proposed algorithm's performance is compared with some of the recent CSS techniques in the literature. The primary metrics for comparison were the receiver operating characteristic (ROC) curve, the area under the curve (AUC), detection performance, and energy consumption. We obtained the ELM model's computational performance during the training phase and calculated the channel detection probability. These results demonstrate the potential superiority of ELM over established methods. In particular, the work presented here shows a better trade-off between training time and detection performance.

Spectrum Allocation of Secondary Users Using Optimization Problem of Linear Cooperative Spectrum Sensing Techniques

Spectrum Allocation of Secondary Users Using Optimization Problem of Linear Cooperative Spectrum Sensing Techniques

Optimisation of censoring‐based cooperative spectrum sensing approach with multiple antennas and imperfect reporting channel scenarios for cognitive radio network

In this article, we have employed an energy detector (ED)-based cooperative spectrum sensing (CSS) with multi-antenna for cognitive radio network (CRN). The spectrum sensing error and energy efficiency (EE) are the key performance parameters in CRN which are affected by the threshold selection method, number of antennas employed at each cognitive user (CU), reporting error probability and cooperative fusion-rule applied at fusion center (FC). Therefore, we have derived the expression for sensing error by considering the effect of all these parameters and have optimized the cooperative fusion-rule at FC by formulating mathematical expression for optimal K in k-out-of-M rule to minimize the sensing error. Since CSS improves the sensing performance of CRN at the cost of increased overhead bits due to more CUs reporting to FC, results reduced EE. We have employed censoring approach to reduce the energy consumption and hence increase the EE of CSS technique. Further, we have illustrated the sensing error and EE improvement achieved under the censoring approach when different threshold selection approaches are employed at each CU. The percentage EE enhancement in censoring approach are 19.53% and 19.9% with constant false-alarm rate (CFAR) and minimized-error probability (MEP) approaches, respectively in comparison to that of the non-censoring approach.

Learning Frameworks for Cooperative Spectrum Sensing and Energy-Efficient Data Protection in Cognitive Radio Networks

This paper studies learning frameworks for energy-efficient data communications in an energy-harvesting cognitive radio network in which secondary users (SUs) harvest energy from solar power while opportunistically accessing a licensed channel for data transmission. The SUs perform spectrum sensing individually, and send local decisions about the presence of the primary user (PU) on the channel to a fusion center (FC). We first design a new cooperative spectrum-sensing technique based on a convolutional neural network in which the FC uses historical sensing data to train the network for classification problem. The system is assumed to operate in a time-slotted manner. At the beginning of each time slot, the FC uses the current local decisions as input for the trained network to decide whether the PU is active or not in that time slot. In addition, legitimate transmissions can be vulnerable to a hidden eavesdropper, which always passively listens to the communication. Therefore, we further propose a transfer learning actor–critic algorithm for an SU to decide its operation mode to increase the security level under the constraint of limited energy. In this approach, the SU directly interacts with the environment to learn its dynamics (i.e., an arrival of harvested energy); then, the SU can either stay idle to save energy or transmit to the FC secured data that are encrypted using a suitable private-key encryption method to maximize the long-term effective security level of the network. We finally present numerical simulation results under various configurations to evaluate our proposed schemes.

Improved weighted average consensus in distributed cooperative spectrum sensing networks

AbstractThis work proposes a fully distributed improved weighted average consensus (IWAC) technique applied to a cooperative spectrum sensing (CSS) problem in cognitive radio systems. This method allows the secondary users to cooperate based on only local information exchange without a fusion centre. We have compared 4 rules of average consensus (AC) algorithms. The first rule is the simple AC without weights. The AC rule presents performance comparable to the traditional CSS techniques such as the equal gain combining rule, which is a soft combining centralised method. Another technique is the weighted AC (WAC) rule, using the weights based on the SUs' channel condition. This technique results in a performance similar to that of the maximum ratio combining with soft combining (centralised CSS). Two new AC rules are analysed, namely, WAC accuracy exchange (WAC‐AE) and IWAC; the former relates the weights to the channel conditions of the SUs' neighbours, whereas the latter combines the conditions of WAC and WAC‐AE in the same rule. All methods are compared with each other and with the hard combining centralised CSS. The WAC‐AE results in a similar performance of the WAC technique but with fast convergence, whereas the IWAC can deliver suitable performance with small complexity increment. Moreover, the IWAC method results in a similar convergence rate than the WAC‐AE method but slightly higher than the AC and WAC methods. Hence, the computational complexity of IWAC, WAC‐AE, and WAC is proven to be very similar. The analyses are based on numerical Monte Carlo simulations, whereas the algorithm's convergence is evaluated for both fixed and dynamic mobile communication scenarios and under additive white Gaussian noise and Rayleigh channels.

Optimization in Cooperative Spectrum Sensing Techniques of CRN

Optimization in Cooperative Spectrum Sensing Techniques of CRN

Performance Evolution Of CR-AD HOC Network by Updated Joint Cooperative Spectrum Sensing Technique

Performance Evolution Of CR-AD HOC Network by Updated Joint Cooperative Spectrum Sensing Technique

Bayesian Detector Based Superior Selective Reporting Mechanism for Cooperative Spectrum Sensing in Cognitive Radio Networks

Cognitive radio network(CRN) coupled with spectrum sensing technology enables unlicensed secondary users (SUs) to opportunistically access the unused licensed spectrum of primary users (PUs). Cooperative Spectrum Sensing (CSS) significantly improves the detection probability of primary user transmission. Nevertheless, current CSS techniques render shortcomings including energy consumption and overhead in sensing phase. Overheads are consequence of multiple cooperative SUs reporting their decision to the fusion center. In this paper, we propose Bayesian Detector based Superior Selective Reporting Cooperative Sensing(BD- SSRCS)scheme. Superior Selective Reporting (SSR)scheme, competently reduces reporting overhead and mitigates interference to PUs. Bayesian based sensing technique for local sensing improves detection performance, spectrum utilization and secondary user throughput. Our analysis and simulation results manifest the outcome of presented work in terms of higher detection probability, lower miss detection rate and lesser detection overhead, as opposed to the traditional cooperative sensing methods. Moreover, miss detection probability and sensing time can be reduced by ideally choosing sensing time allocation factor.

Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks

Cognitive wireless sensor network (CWSN) is a combination of cognitive radio and wireless sensor networks (WSNs), which can effectively solve spectrum resource scarcity problem in WSNs. As one of the key technologies in CWSNs, spectrum sensing enables sensors to detect and opportunistic access underutilized idle licensed bands, which remarkably improves the spectrum utilization and system performance. And cooperative spectrum sensing (CSS) has emerged to enhance detection performance of spectrum sensing. Focusing on CSS schemes in CWSNs, this paper explores the cooperative and noncooperative schemes comprehensively. The characteristics of the existing CSS schemes are analyzed in CWSNs. And then, according to the behavior of cognitive sensors, CSS techniques are classified into noncooperative and cooperative schemes. Concentrating on energy consumption, cooperative CSS schemes are classified into three categories: censoring, clustering, and sensor selection. The performance of three kinds of CSS schemes is compared and analyzed thoroughly. Finally, the difficulties and challenges of CSS scheme are addressed in CWSNs.

Performance Analysis of Cooperative Spectrum Sensing Technique for Low SNR Regime over Fading Channels for Cognitive Radio Networks

Cognitive Radio is a technology for Next Generation Wireless Networks to support Dynamic Spectrum Access which addresses the spectrum scarcity and under utilization problems experienced by today’s wireless communication networks. Spectrum Sensing is an important function of cognitive radio that identifies the unused licensed frequency bands of Primary Users for opportunistic access by Secondary Users. In this paper, we have proposed a sensing technique incorporating wavelet denoising to enhance the sensing performance at low SNR regime. The combination of wavelet denoising preceeding energy detector, hard decision for local sensing and OR fusion rule at the fusion center for cooperative sensing has improved the sensing performance. We have quantified the performance improvement of the proposed technique in fading environments and studied the effect of wavelet denoising. Probability of missed detection has been reduced by 38% over Rayleigh Fading channel and 52% over Nakagami Fading channel compared to conventional sensing technique for ten users cooperation. Sensing time has been reduced by 1 msec and throughput improved by 50 Mbits/Sec/Hz compared to conventional energy detection based sensing method.

Performance Analysis of Centralized Cooperative Spectrum Sensing Technique for Cognitive Radio Networks

Performance Analysis of Centralized Cooperative Spectrum Sensing Technique for Cognitive Radio Networks

Sequential Cooperative Spectrum Sensing Technique in Time Varying Channel

Cognitive radio opportunistically accesses the spectrum while the licensed user is idle. A spectrum sensing procedure to monitor primary users' existence is therefore vital to cognitive radios. In this paper, we investigate the energy detection based sequential cooperative spectrum sensing technique in time varying channels. By utilizing past local observations from previous sensing slots, cognitive radio nodes can aggregate the current and previously received energy values to improve the detection performance. We propose the weighted sequential energy detector (SED) in which a fixed number of past observations are taken for decision making. In addition, we propose two adaptive schemes, namely the Two-Stage SED and the Differential SED, where a cognitive radio user uses previously received energy values until it detects a change in primary user's activity. The probability of false alarm and detection for the Weighted SED and Two-Stage SED schemes are derived. The results show that the proposed schemes achieve better detection performance over the conventional cooperative energy detection technique. In particular, the Two-Stage SED and Differential SED schemes can satisfy the IEEE 802.22 standard's requirement of 10% false alarm and 90% detection in scenarios that conventional techniques cannot accommodate.

Analysis and Algorithm for Robust Adaptive Cooperative Spectrum-Sensing

The optimal data-fusion rule was first established for multiple-sensor detection systems in 1986. Most subsequent works have been focused on the corresponding implementation aspects. The probability of false alarm and the probability of miss detection used in this data-fusion rule are quite difficult to precisely enumerate in practice. Although the improved data-fusion implementation techniques are now available, most existing cooperative spectrum-sensing techniques are still based on the simple energy-detection algorithm, which is prone to failure in many scenarios. In this paper, we propose a novel adaptive cooperative spectrum-sensing scheme based on our recently proposed single-reception spectrum-sensing technique. We also found that the commonly-used sample-average estimator for the cumulative weights in the data-fusion rule becomes unreliable in time-varying environments. To overcome this drawback, we adopt a temporal discount factor, which is crucial to the probability estimators. New theoretical analysis to justify the advantage of our proposed new estimators over the conventional sample-average estimators and to determine the optimal numerical value of the proposed discount factor is presented. The Monte Carlo simulation results are also provided to demonstrate the superiority of our proposed adaptive cooperative spectrum-sensing method in both stationary and time-varying environments.

Machine Learning Techniques for Cooperative Spectrum Sensing in Cognitive Radio Networks

We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the channel available and the channel unavailable class. Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques.

Chaotic Stochastic Resonance Energy Detection Fusion Used in Cooperative Spectrum Sensing

A cooperative spectrum-sensing technique in cognitive radio (CR) networks based on the data fusion of various chaotic stochastic resonance (CSR) energy detectors is proposed in this paper. Due to noisy uncertainty in unpredictable wireless communication environments, detection performance of conventional energy detection cannot be guaranteed. By introducing the CSR system with various CSR noise types, and with the data fusion on these CSR-based energy detectors, the detection probability can be improved under the same constant false-alarm rate (CFAR), particularly under low signal-to-noise ratio (SNR) circumstances. Theoretical analyses and computer simulation results verify the effectiveness of the proposed novel cooperative spectrum-sensing approach under serious wireless conditions.