Cluster-based Cooperative Spectrum Sensing Research Articles

Optimized Cluster-Based Cooperative Spectrum Sensing Over Weibull, Nakagami, Rician, and Rayleigh Fading Channels

This paper reports cluster-based cooperative spectrum sensing (CB-CSS) over Weibull, Nakagami, Rician, and Rayleigh fading channels in cognitive radio networks (CRNs). The performance assessment of centralized cooperative spectrum sensing (CCSS) and CB-CSS with different fusion rules over the Weibull fading channel is presented. The goal is to improve detection performance in a practical and efficient manner for CB-CSS. For this purpose, an expression for the optimal inter-fusion rule threshold (fusion rule between cluster head (CH) and fusion center (FC)) is proposed to minimize total error probability (TEP) over Weibull for CB-CSS. Simulations are conducted using analytical equations for missed detection and false alarm probability. The obtained expression for optimal inter-fusion rule threshold which minimizes TEP is verified by the results. The proposed model is validated with other fading channels such as Nakagami, Rician, and Rayleigh to show the superiority of the approach. It is observed that for a given range of detection threshold and for 4 × 6 cluster size (4 clusters and 6 CR users inside each cluster), the lowest total error probability is achieved over the Weibull fading channel. Minimum TEP over the Weibull fading channel for given network parameters is TEP = 1.656 × 10 − 6 when intra- and inter-fusion rules are l = 1 (OR fusion) and c = 3 . The least number of clusters and users essential to achieving target error bound over different fading channels are also determined.

Hybrid fusion of Pietra–Ricci index detector information for cooperative spectrum sensing

The Pietra–Ricci index detector (PRIDe) has been recently proposed as one of the simplest techniques for data-fusion cooperative spectrum sensing (CSS), attaining robustness against time-varying signal and noise levels, constant false alarm rate, and high performance. This paper proposes a hybrid sensing information fusion scheme for cluster-based CSS, in which the PRIDe is applied to data-fusion at the cluster heads (CHs), in combination with decision-fusion of CHs’ decisions at the fusion center. A modified version of the k-means clustering algorithm is used to form the clusters, where a CH is defined as the cluster member closest to the cluster’s centroid. Theoretical and computer-simulation results trade the spectrum sensing performance against the energy per bit effectively transmitted during the data communication interval, unveiling that, contrary to the common thought, cluster-based CSS may not provide energy savings compared to CSS without clustering. Moreover, it is demonstrated that the performance rank of the well-known decision-fusion rules AND, OR and MAJ is strongly affected by the distance between the primary network transmitter and the secondary users, as well as by the path-loss exponent of the sensing channel.

Enhancing Throughput Over Varied Fading Channels for Cluster-Based Cooperative Spectrum Sensing in Cognitive Wireless Networks

Enhancing Throughput Over Varied Fading Channels for Cluster-Based Cooperative Spectrum Sensing in Cognitive Wireless Networks

Snake Optimization Technique for Spectrum Handoff in Cluster based Cognitive Radio Network

In Cognitive Radio (CR) networks, the use of Secondary Users (SU) in the spectrum has an undesirable effect on spectrum handoff, which causes a handoff delay. The handoff procedure can result in service outages and considerable transmission quality delays, making it a regular source of concern for the SU. An effective spectrum handoff strategy that utilizes the Spectrum Binary Snake Optimization (SBSO) algorithm and the M/G/1 queuing model has been proposed in this study. The use of Cluster Based Cooperative Spectrum Sensing improves SU performance and reduces channel congestion. In order to report the active and inactive channels in the spectrum, the cluster head is connected to the SU base station, and a decision report is subsequently generated by the fusion center. With the use of a bitwise and mutation operator format, SBSO reduces the overall service time required for handoff in the approach that is being proposed. The proposed methodology also provides a framework for observing how primary user activity and spectrum handoff delays behave in the presence of potential interruptions in a CR network. The simulation model of the proposed work optimizes the packet delivery ratio with the three benchmark functions, and provides optimal handoff, and is compared to SBSO and other models that offer a better trade off over delay achievement.

Optimization of inter-fusion rule threshold for energy-efficient cluster-based cooperative spectrum sensing over Nakagami and Rician fading channels

ABSTRACT In cooperative spectrum sensing (CSS), in order to maximise the energy efficiency (EE) of cognitive radio (CR) users, users are divided into clusters. In this paper, the optimal inter-fusion rule threshold formula for different number of clusters and users is proposed for maximum EE in cluster-based CSS (CB-CSS). Different fusion rules are investigated for this purpose over Nakagami and Rician fading channel conditions. Simulations are conducted for different number of clusters and users using AND and OR intra-fusion rules and inter-fusion rule threshold is optimised in CB-CSS over the Nakagami-m fading channel and the Rician fading channel. The obtained mathematical expression for the inter-fusion rule threshold is verified by the results. It is investigated that when number of clusters are and number of users in a cluster are , OR-AND (, ) fusion rule achieves 2.46% more EE than AND-OR (, ) fusion rule in case of Nakagami fading channel and 5.06% more EE than AND-OR fusion rule in case of Rician fading channel. Further, EE with respect to number of users is compared between additive white gaussian noise (AWGN) channel and Nakagami and Rician fading channels.

Cluster-ID-Based Throughput Improvement in Cognitive Radio Networks for 5G and Beyond-5G IoT Applications.

Cognitive radio (CR), which is a common form of wireless communication, consists of a transceiver that is intelligently capable of detecting which communication channels are available to use and which are not. After this detection process, the transceiver avoids the occupied channels while simultaneously moving into the empty ones. Hence, spectrum shortage and underutilization are key problems that the CR can be proposed to address. In order to obtain a good idea of the spectrum usage in the area where the CRs are located, cooperative spectrum sensing (CSS) can be used. Hence, the primary objective of this research work is to increase the realizable throughput via the cluster-based cooperative spectrum sensing (CBCSS) algorithm. The proposed scheme is anticipated to acquire advanced achievable throughput for 5G and beyond-5G Internet of Things (IoT) applications. Performance parameters, such as achievable throughput, the average number of clusters and energy, have been analyzed for the proposed CBCSS and compared with optimal algorithms.

Adaptive fusion based clustering approach in cognitive radio networks

Recently, the cognitive radio (CR), which is a technology to effectively use the radio band, has been introduced to allow unlicensed users to use the spectrum efficiently. The key problem of the CR system is the overhead issue in control channel as the number of secondary users (SUs) is very high in the cooperative spectrum sensing (CSS) scenario. To alleviate this problem, the Cluster-based CSS solution is useful to prevent crowding on the control channel. Therefore, this paper uses a neural network (NN) approach based on a supervised learning algorithm at the fusion center (FC) for the suggested clustering technique. Scheme suggests a combination of hard decisions, using NN approach to increase the efficiency of CRNs with multiple clusters and to decrease the overhead issue between the FC to cluster head (CH). Also, under the Rayleigh fading channel, the proposed scheme is examined and simulated to test their detection efficiency and compare their performance with conventional schemes. The simulation results prove that proposed clustering approach enhances the accuracy of the system. Also, it is more robust than other conventional schemes.

Optimization techniques for spectrum handoff in cognitive radio networks using cluster based cooperative spectrum sensing

Spectrum handoff has an undesirable effect in utilizing the space for Secondary user (SU) in the spectrum, which causes a handoff delay in cognitive radio network. The SU frequently faces the problem of handoff process which is likely to interrupt the service and substantial delay over the quality of service during the transmission. It struggles towards identifying the channel during the handoff by occupying a major role in today’s era. Based on this research, an effectual spectrum handoff scheme is anticipated using Spectrum Binary Particle Swarm Optimization (SpecBPSO) algorithm and M/G/1 queuing model. Towards improving the efficiency of SU and reducing the congestion over channel, Cluster Based Cooperative Spectrum Sensing (CBCSS) is used. The cluster head is selected dynamically based on the sensing signal of the SU. The cluster head is associated with the SU base station to report the active and inactive channel in the spectrum and later decision report is generated by the fusion center. In this proposed method, SpecBPSO uses the Boolean variable to reduce the total service time for handoff to find the optimal global value using bitwise and mutation operator format. This study work also presents an outline to observe the outcome of primary user’s activity and the delay performance of spectrum handoff with the possible interruptions in a CR network. The simulation setup of the proposed work is compared with spectrum particle swarm optimization (SpecPSO), binary particle swarm optimization (BPSO) and ant colony optimization that provide a better tradeoff over the delay achievement, maximize the SNR with the three benchmark functions and optimal handoff.

Inter and Intra Fusion Schemes for Energy Efficient CB-CSS in Cognitive Wireless Networks

ABSTRACT The main challenge of Cognitive Radio (CR) is to identify the occurrence of primary users (PU) effectively to mitigate interference with licenced communications. The occurrence of PU is identified by spectrum sensing (SS). Due to shadowing and deep fading, SS may produce false sensing results. Cooperative spectrum sensing (CSS) can mitigate these problems but at the cost of extra energy consumption especially when the CR users are battery functioned. In CSS, the fusion rule directly affects the energy efficiency (EE) of CR users, it can be optimised to increase EE. Moreover, the energy consumption of the CR users can be minimised if they are grouped into clusters. In this paper intra and inter fusion schemes have been proposed in a cluster-based CSS (CB-CSS). The overall EE is maximised by optimising the inter and intra fusion rule threshold of CB-CSS. An iterative algorithm has been presented that determines the optimal value of inter and intra fusion rule. Results show that for 6X6cluster size, optimum inter and intra fusion rule threshold is found to be 4 and 5, respectively.

Energy efficient collaborative spectrum sensing with clustering of secondary users in cognitive radio networks

Collaborative sensing also known as cooperative spectrum sensing (CSS) is an efficient spectrum sensing technique to improve the sensing accuracy in cognitive radio (CR). However, it brings extra collaborative sensing overhead due to mutual exchange of large information among CR users. In CSS, the number of cooperative users, fusion rule, transmission power and sensing time affects the energy efficiency (EE) of the CSS. In this study, a cluster-based CSS is proposed with four fusion rules. Optimised fusion rule is determined that maximises the EE. For the proposed CSS, joint optimal sensing time and transmission power of energy efficient CSS is determined. The joint optimisation design problem is formulated as function of sensing time and transmission power subjected to primary user protection constraints. An iterative algorithm is proposed to determine joint optimal sensing time and transmission power that maximises the EE of CSS. Results show that EE is maximum for AND–OR fusion at sensing duration 2.3 ms and transmission power 1.11 W at SNR = −20 dB. Maximum EE at optimal point is = 3.735 Mbits/Hz/J.

A Cluster-Based Cooperative Spectrum Sensing Strategy to Maximize Achievable Throughput

In this paper, a novel cooperative spectrum sensing (CSS) strategy is proposed for cognitive radio networks (CRN) with imperfect reporting channels. This CSS strategy uses simultaneously four techniques to overcome undesirable effects of reporting channels, which are errors and overhead traffic. First, it uses an energy efficient clustering algorithm to maximize the CRN lifetime. Second, in each cluster, an incremental weighing fusion rule is used to improve the accuracy of local sensing performed by secondary users. Third, it selects more reliable improved decisions for sending to the fusion center, to decrease overhead traffic. Fourth, it employs a space–time block code to reduce the probability of errors in reporting channels. We determine the optimal settings of the proposed strategy, such as number of clusters, and their corresponding members by maximizing the achievable throughput of the CRN. Numerical and simulation results will prove the proposed CSS strategy yields the highest throughput for the CRN, while it guarantees the maximum lifetime of CRN, and maximum protection of primary users.

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.

Cluster-Based Cooperative Subcarrier Sensing Using Antenna Diversity-Based Weighted Data Fusion

Cooperative spectrum sensing (CSS) is used in cognitive radio (CR) networks to improve the spectrum sensing performance in shadow fading environments. Moreover, clustering in CR networks is used to reduce reporting time and bandwidth overhead during CSS. Thus, cluster-based cooperative spectrum sensing (CBCSS) has manifested satisfactory spectrum sensing results in harsh environments under processing constraints. On the other hand, the antenna diversity of multiple input multiple output CR systems can be exploited to further improve the spectrum sensing performance. This paper presents the CBCSS performance in a CR network which is comprised of single- as well as multiple-antenna CR systems. We give theoretical analysis of CBCSS for orthogonal frequency division multiplexing signal sensing and propose a novel fusion scheme at the fusion center which takes into account the receiver antenna diversity of the CRs present in the network. We introduce the concept of weighted data fusion in which the sensing results of different CRs are weighted proportional to the number of receiving antennas they are equipped with. Thus, the receiver diversity is used to the advantage of improving spectrum sensing performance in a CR cluster. Simulation results show that the proposed scheme outperforms the conventional CBCSS scheme.

A Cluster-Based Cooperative Spectrum Sensing in Cognitive Radio Network Using Eigenvalue Detection Technique with Superposition Approach

Cognitive radio (CR) networks have been active area of research because of its ability to opportunistically share the spectrum. A cluster-based cooperative spectrum sensing (CCSS) has a tremendous impact on sensing reliability compared with cooperative spectrum sensing. The energy detection (ED) technique requires perfect knowledge of noise power. An eigenvalue-based spectrum sensing has mitigated the noise uncertainty problem. Sensing and reporting time slots are rigidly separated in the conventional ED and eigenvalue-based detection (EVD) schemes. In CCSS, more reporting time slots are required as the number of CR users (CRUs) increases. If the reporting time slots of other CRUs as sensing time slots with a superposition allocation, the more reliable channel sensing can be achieved. In this paper, we propose CCSS using EVD technique with a superposition approach scheme where the reporting time slot is properly utilized to sense the primary user's (PU's) signal more accurately by rescheduling the reporting time slot for CRUs and cluster heads (CHs). Simulation result shows that the proposed EVD scheme has better detection probability than the conventional CCSS using both ED and EVD techniques.

Cluster-Based Cooperative Spectrum Sensing Assignment Strategy for Heterogeneous Cognitive Radio Network

Cognitive radio (CR) is proposed as an efficient way to address the issue of spectrum shortage and underutilization, in which cooperative spectrum sensing (CSS) is used to enhance sensing performance. One of the most fundamental problems of CSS is how to appropriately assign the secondary users (SUs) to sense the primary user (PU) channels. In this paper, we study the CSS problem under a more practical scenario where we take the heterogeneous characteristics of both SUs and PU channels into consideration. With the objective to maximize the achievable throughput for SUs, we propose a cluster-based CSS to obtain a proper assignment policy, in which all the SUs in the same cluster cooperate in sensing the same set of PU channels. Moreover, the cluster-based CSS problem is formulated as a Maximum-Weight one-sided Biclique (MWB) problem. We prove that the MWB problem is NP-complete, and a greedy heuristic algorithm with polynomial computational complexity is developed to find the suboptimal assignment policy. Evaluation results show that our proposed greedy algorithm can achieve a close-to-optimal solution with far less complexity.

Super-allocation and Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks

Super-allocation and Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks

Optimization of Cluster-Based Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks with Soft Data Fusion

This paper investigates cluster-based cooperative spectrum sensing issues in two-layer hierarchical cognitive radio networks with soft data fusion. We first define a two-phase reporting protocol in the paper. In the first phase, secondary users forward their soft sensing information to cluster heads (CHs) over large-scale fading. In the second phase, all CHs transmit the aggregated soft energy information to the fusion center (FC) with different weights. Thus we derive the network false alarm (FA) and the detection probabilities as functions of the FC decision threshold, the clustering algorithm and different weights. Given a target on the detection probability, minimizing the FA probability is then formulated as a constraint optimization problem within two scenarios including additive white Gaussian noise environment and Rayleigh fading environment. A close-form upper bound of the FA probability is derived and a novel clustering scheme is also proposed for each scenario. Numerical results show that the proposed schemes achieve a satisfying performance.

Cluster-based cooperative spectrum sensing over correlated log-normal channels with noise uncertainty in cognitive radio networks

In this study, the authors consider the problem of cooperative spectrum sensing (CSS) based on linear combination of observations over correlated log-normal shadow-fading channels. To reduce the effects of imperfect reporting channels, a cluster-based CSS framework and a new cluster head selection algorithm are proposed. Using the received energies (as local observations) from different clusters, the fusion centre can make the final decision by linearly combining the noisy cluster observations. To calculate the combination weights, the authors come across the problem of joint distribution approximation of sum of the correlated log-normal random variables corresponding to different clusters. A joint moment generating function (MGF) matching algorithm is proposed in this study to estimate the summations by a single log-normal vector. Monte Carlo simulations confirm the accuracy of the proposed MGF-based approach in estimating the desired statistics and efficiency of the cluster-based spectrum-sensing algorithm in terms of primary signal detection.