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

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.

Cognitive Radio (CR) networks enable dynamic spectrum access and can significantly improve spectral efficiency. Cooperative Spectrum Sensing (CSS) exploits the spatial diversity between CR users to increase sensing accuracy. However, in a realistic scenario, the trustworthy of CSS is vulnerable to Spectrum Sensing Data Falsification (SSDF) attack. In an SSDF attack, some malicious CR users deliberately report falsified local sensing results to a data collector or Fusion Center (FC) and, then, affect the global sensing decision. In the present study, we investigate an analytical model for a hard SSDF attack and propose a robust defense strategy against such an attack. We show that FC can apply learning and estimation methods to obtain the attack parameters and use a better defense strategy. We further assume a log-normal shadow fading wireless environment and discuss the attack parameters that can affect the strength of SSDF attack. Simulation results illustrate the effectiveness of the proposed defense method against SSDF attacks, especially when the malicious users are in the majority.

The ever-increasing demand for higher data rates in wireless communications in the face of inadequate or underutilized spectral resources has motivated the introduction of cognitive radio. In cognitive radio network, unlicensed users continuously scan a vast range of frequencies to detect `white spaces' or `spectrum holes' that are temporarily and spatially not being used by licensed user, process of which is commonly known as spectrum sensing. In order to implement the cooperative spectrum sensing among Cognitive Radio (CR) users, data fusion schemes are superior to that of decision fusion ones in terms of detection performance but suffer from the drawback of huge traffic overhead when restriction of bandwidth of communication channels and energy consumption of the radio network comes into consideration. In this paper, a combination of data and decision fusion approach is implemented to mutually adventure the advantages of both and a selective weight setting algorithm is proposed by utilizing normal and modified deflection coefficients maximization under Neyman-Pearson criterion in order to obtain a final decision. Also, cluster-based Cooperative Spectrum Sensing (CSS) has been proposed in cognitive radio network to improve the energy efficiency. The simulations show promising results as the hybridization process visibly reduces the network traffic overhead while attaining a highly reasonable detection performance.

From the last decade much more emphasis were drawn to develop robust Cognitive Radio Networks (CRNs). For robust CRNs, Spectrum Sensing is one of the most important issue for its better existence in licensed spectrum. Spectrum sensing is used for finding unoccupied frequency bands, called white space or spectrum holes. However, spectrum sensing performance in practice is often compromised with multipath fading, shadowing and receiver uncertainty issues due to low signal to noise ratio (SNR). In this paper, we propose a robust double threshold feature detector (DTFD) that detect even very low SNR primary user. Here we have used DTFD in cooperative spectrum sensing scenario in cognitive radio networks (CRNs) on a additive white Gaussian noise (AWGN) channel. Here individual detectors employ double threshold method in feature based detection of Orthogonal Frequency Division Multiplexing (OFDM) signal, and sends their results to fusion center (FC) where final decision takes place. Detection is done by using popular autocorrelation property of cyclic prefix (CP) in OFDM signal. From proposed technique, our objective is to achieve a better sensing performance under very low SNR, such that the primary networks and the CRNs can coexist in better way. Simulation results are done under theoretical perspective which illustrate that the proposed sensing technique can reliably detect OFDM signals at SNR as low as −20 dB.

In cognitive radio (CR) networks, cooperative spectrum sensing is utilized to improve the sensing performance to avoid potential interference to primary users (PUs) and increase spectrum access opportunities for secondary users (SUs). A cooperative spectrum sensing process is divided into three phases: individual sensing/detection, reporting/fusion, and data transmission. In the reporting phase, one or more reporting channels are needed to transmit individual sensing results to a fusion center (FC), and global spectrum sensing results are determined at the FC. The number of required reporting channels depends on the number of spectrum sensors or SUs, which relates to reporting channel efficiency and channel scheduling complexity. That is to say, the reporting channel design can be a challenge, especially when fixed assignment scheduling is used. Therefore, in this paper, we design a reporting channel scheme based on random access protocols, including slotted Aloha and reservation-Aloha. Performance evaluations in terms of PU detection probabilities and false alarm probabilities considering the proposed reporting channels are presented. In addition, the impact of soft/unquantized spectrum sensors or detectors (SUs) and malicious SUs is considered in this paper. Analytical and simulation results illustrate the effectiveness of the proposed reporting channel scheduling methods in improving the cooperative spectrum sensing performance.

This study aims the throughput maximization of carrier sense multiple access (CSMA) in random access cognitive radio networks (CRNs) by using cooperative spectrum sensing. In the cooperative spectrum sensing, multiple cognitive radio (CR) users individually monitor the spectrum for the primary users (PUs)' activities and send their local decisions to the CR access point. Upon receiving the individual decisions, the CR access point gives the final decision about the PU's presence at the spectrum and sends it to all CR users in the network. At decision stage, CR access point utilizes the k-out-of-N decision fusion rule with optimal k value in order to obtain maximum CRN throughput. In this paper, we model a CRN utilizing CSMA medium access control scheme and focus on analyzing the throughput performance of it. Throughput performances of our CRN model which separately utilizes AND, OR, MAJORITY fusion rules and k-out-of-N decision fusion rule with the optimum k value are obtained and compared under the same network level probability of detection constraint. The throughput results obtained show that our CRN model utilizing k-out-of-N decision fusion rule with the optimum k value achieves higher throughput performance than AND, OR and MAJORITY fusion rules when they all have the same CRN load.

Energy-Decisive and Upgrade Cooperative Spectrum Sensing in Cognitive Radio 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.

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.

In cognitive radio (CR) networks, the secondary users (SUs) need to find idle channels via spectrum sensing for their transmission. In this paper, we study the problem of designing the sensing time to minimize the SU transmission delay under the condition of sufficient protection to primary users (PUs). Energy detection sensing scheme is used to prove that the formulated problem indeed has one optimal sensing time which yields the minimum SU transmission delay. Then, we propose a novel cooperative spectrum sensing (CSS) framework, in which one SU's reporting time can be used for other SUs' sensing. The analysis focuses on two fusion strategies: soft information fusion and hard information fusion. Under soft information fusion, it is proved that there exists one optimal sensing time that minimizes the SU transmission delay. Under hard information fusion, for time varying channels, the novel multi-slot CSS is derived. The performance of SU transmission delay is studied in both perfect and imperfect reporting channels. Some simple algorithms are derived to calculate the optimal sensing settings that minimize the SU transmission delay. Computer simulations show that fundamental improvement of delay performance can be obtained by the optimal sensing settings. In addition, the novel multi-slot CSS scheme shows a much lower transmission delay than CSS based on general frame structure.

The quality of the reporting channel and the relaying mechanism play a significant role in cooperative spectrum sensing (CSS). However, these factors are frequently overlooked when it comes to discussing the performance of CSS in cognitive radio (CR) networks. In this work we investigate the performance of a relay-based CSS approach proposed for CR applications. Specifically, we analyze the detection accuracy of a CR network considering channel impairments, amplify-and-forward relying protocol, and a selection combining scheme. First, a closed form expression for the average false alarm probability is derived. Then, the probability density function (PDF) of the end-to-end signal-to-noise ratio (SNR) is obtained and a closed-form expression for the average detection probability at high SNR applications is derived. The sensing and the reporting channels are assumed to be subjected to independent and identically distributed Rayleigh fading. The PDF approach is used in our analysis. This approach is not easily tractable as it involves higher orders of Bessel functions. However, an approximated polynomial representation of a first-order modified Bessel function of the second kind makes this task possible. The results obtained validate the derived closed form expressions and show the importance of considering reporting channel statistics and relaying mechanism for accurate performance analysis of the CSS in CR networks.

Cognitive Radio (CR) networks offer the solution to the problem of spectrum deficiency by increasing the use of the spectrum. In CR networks, Cooperative Spectrum Sensing (CSS) aids to improve the sensing performance by utilizing spatial diversity and enhances the spectrum access possibilities for Secondary Users (SUs) or unlicensed users. With the help of this CR and CSS, the aim is to enhance the utilization of the available spectrum. In this paper, a mathematical model has been developed for optimizing the number of SUs to participate in the CSS to maximize the spectrum utilization.

Cooperative spectrum sensing (CSS) is a promising technology in cognitive radio (CR) networks. Among the many existing CSS methods, the cooperation in a single channel is studied extensively. However, CR networks usually deal with multiple channels, therefore multi-channel CSS needs be studied. In this paper, we investigate the problems that how to optimally assign secondary users (SU) to cooperatively sense multiple channels and how to optimally set the sensing time and sensing thresholds. An optimization problem of multi-channel CSS is formulated to maximize the average throughput of CR networks subject to the constraints of probability of detection for each channel. An exhaustive algorithm and a greedy algorithm are proposed to obtain the optimal solutions of the optimization problem. Finally, both analytical and numerical results are presented to demonstrate the effectiveness of our proposed algorithms. It is also shown that the greedy algorithm with a low complexity achieves the same performance as the exhaustive algorithm.

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.

In recent years, the security issues of the cognitive radio (CR) networks have drawn a lot of research attentions. Primary user emulation attack (PUEA), as one of common attacks, compromises the spectrum sensing, where a malicious user forestalls vacant channels by impersonating the primary user to prevent other secondary users from accessing the idle frequency bands. In this paper, we propose a new cooperative spectrum sensing scheme, considering the existence of PUEA in CR networks. In the proposed scheme, the sensing information of different secondary users is combined at a fusion center and the combining weights are optimized with the objective of maximizing the detection probability of available channels under the constraint of a required false alarm probability. We also investigate the impact of the channel estimation errors on the detection probability. Simulation and numerical results illustrate the effectiveness of the proposed scheme in cooperative spectrum sensing in the presence of PUEA.