Clustering Algorithm-Based Data Fusion Scheme for Robust Cooperative Spectrum Sensing

Cooperative spectrum sensing is envisaged to increase reliability in cognitive radio networks. Several users cooperate to detect the availability of a wireless channel. In this framework, some malicious users may affect the decisions taken by the cognitive users by providing false information, introducing spectrum sensing data falsification (SSDF). In this paper, the effect of multiple malicious users on the energy efficiency of a cognitive radio network is given. A low-overhead security protocol is proposed to address SSDF attacks under a trade-off between energy efficiency and security. An analytical study is provided to set the optimal number of security bits required to maximize energy efficiency. Simulation results show a considerable improvement on the achievable energy efficiency, and the optimal number of bits explicitly depends on the selected fusion rule, the number of malicious users and the number of legitimate users.

The Cognitive Radio based Internet of Things (CR-IoT) is a promising technology that provides IoT endpoints, i.e., CR-IoT users the capability to share the radio spectrum otherwise allocated to licensed Primary Users (PUs). Cooperative Spectrum Sensing (CSS) improves spectrum sensing accuracy in a CR-IoT network. However, its performance may be degraded by potential attacks of the malicious CR-IoT users that send their incorrect sensing information to the corresponding Fusion Center (FC). This study presents a promising Machine Learning (ML)-based malicious user detection scheme for a CR-IoT network that uses a Support Vector Machine (SVM) algorithm to identify and classify malicious CR-IoT users. The classification allows the FC to make a more robust global decision based on the sensing results (i.e., energy vectors) which are reported only by the normal CR-IoT users. The effectiveness of the proposed SVM algorithm based ML in a CR-IoT network with the malicious CR-IoT users is verified via simulations.

Cognitive Radio Network (CRN) is an intelligent technique to access the radio spectrum. In CRN the Secondary Users (SUs) sense the spectrum hole of the Primary User (PU) band opportunistically. Spectrum sensing is the major step in CRN to observe the Primary User (PU) signal. For individual user, sensing ability is affected by fading and shadowing. Cooperative Spectrum Sensing (CSS) provide solution to the problem of fading and shadowing where multiple SUs cooperates with each other to catch the absence and presence of PU signal. However, it is vulnerable to a variety of malicious User (MU) attacks. These MUs are unfriendly to the sensing results that could be achieved by CSS. In the proposed work performance of hard combination schemes are tested under varying Signal to Noise Ratios (SNRs). The performance of the hard combination schemes such as majority voting, logical-AND and logical-OR are tested in the presence of Lazy Users (LUs) and Opposite Malicious Users (OMU) at different SNR levels. Results indicate that hard combination scheme such as majority voting outperforms all other schemes, such as logical-OR and logical-AND.

In cognitive radio networks, cooperative spectrum sensing typically requires two essential phases: the phase of primary user's signal detection by cognitive users and the phase of initial detection result reporting from the cognitive users to a fusion center, which are referred to as detection and reporting phases, respectively. Common control channels (also called dedicated reporting channels) from the cognitive users to fusion center are assumed in previous research to avoid interfering with the primary user in the reporting phase. This, however, requires additional channel resources and increases implementation complexity due to the dedicated reporting channels management. In this paper, we propose an alternative cooperative spectrum sensing framework without dedicated reporting channels and present an interference analysis of its impact on primary users. We show that the interference caused by the proposed scheme is controllable and can be constrained to satisfy a given primary user's quality-of-service (QoS) requirement. By jointly considering the detection and reporting phases, we further examine the receiver operating characteristics (ROC) performance of the proposed cooperative spectrum sensing scheme in Rayleigh fading environment. Numerical results illustrate that, with a guaranteed detection probability constraint, a minimized false alarm probability can be achieved through an optimization of the time durations between the detection and reporting phases.

Malicious user is internal attacker that disturb communication. In case of cognitive radio, malicious user attacks primary signal detection and influences the fidelity of sensing outcome. This paper studies malicious user attack and its impacts to sensing outcome in cognitive radio network (CRN). We consider a number of malicious user that emulate primary user (PU) signal. Malicious users are randomly deployed around secondary user (SU) at a certain distance. The distance between PU transmitter and SU is 100 km. We define different number of malicious user and observe the impact to detection error rate. The change of transmission power of malicious user is also studied. These two cases is investigated under two hypothesis of Neyman Pearson criterion. The derived results show that increasing number of malicious user can increase probability of error rate. Furthermore, increasing power transmission of malicious user can increase probability of error rate as well.

CooperativeCooperative communication in cognitive radio networks (CRNs) is an emerging communication prototype which offers enhanced reachability and improved capacity. In such mode of communication, the Cognitive Users (CUs) can work as relay nodes for conveying the signal received from the Primary Users (PUs) to required destinations, while satisfying the PUs' and the CUs' minimum rate requirements. The PUs' are motivated to cooperate by the incentive to attain a higher PU rate with low power requirement, while concurrently allowing an access to CUs to share their spectral band. This arrangement is usually called as Overlay CRN. Several schemes are available in the literature which provide efficient spectral access for multiple PUs and CUs in cooperative CRNs. However, in such schemes the PUs enhanced performance does not necessarily translates into a satisfactory performance for the CUs and vice versa. Existing cooperative distribution algorithms such as Pragmatic Distribution Algorithms (PDA) and Conventional Distribution Algorithms (CDA) do not support the transmission rate enhancement for both PUs and CUs simultaneously and an improved performance of one network (PUs or CUs) results into poor performance of the other. In this paper, an improved algorithm namely 'Incremental Pragmatic Distribution Algorithm (IPDA)' is proposed, which attempts to improve the average transmission rate of both CUs and PUs.

In wireless communication, there is an enormous increase in the number of users which results in spectrum shortage. This can be defeated through dynamic spectrum access scheme and cognitive radio is the best solution to achieve this. A cognitive radio (CR) is programmable, configurable and can dynamically use the spectrum in the wireless environment. One of the important tasks of CR is to perform spectrum sensing. Sensing done on spectrum facilitates the cognitive radio user to find the unoccupied area of the spectrum. This CR is prone to many attacks in the wireless environment like primary user emulation attack (PUEA), denial of service attack, replay attack, etc. Among them, the most important attack is PUEA. When a user mimics the primary user and acquires priority over other users to access the spectrum then it is termed as PUEA. In this work, PUEA detection and its defense techniques have been modeled to avoid the degradation of spectrum. To detect the malicious users feature detection-based sensing with double threshold has been proposed. If the detected signal falls above the upper threshold or below the lower threshold then it is considered as primary user signal, whereas if the signal falls in between the thresholds then that signal is identified as malicious user signal. The detection of PUEA cannot be accurate due to factors like false alarm and miss detection. Hence, a game model has been designed for the legitimate nodes to reach strategic defense decisions in the presence of multiple attackers. Based on the actions of the players, the defenders’ payoff is determined to obtain optimal defense decisions.

The problem of full-duplex primary users (PUs) signal extraction is studied for cognitive radio networks, and the polarization Jones vector distance (PJVD) is used as the extraction metric. Based on the orientation and magnitude difference of the signal samples from different PUs, the PJVD based signal extraction (PJVD-SE) can efficiently detect each PU's signal polarization by calculating the PJVD between any two samples. Due to the Gaussian distributed noise, the resultant PJVD for primary samples is noncentral chi-squared distributed. Based on the distribution, the extraction error probability is investigated. Theoretical analysis shows that the proposed PJVD-SE technique outperforms the polarization similarity based technique in terms of both error probability and computation complexity. It is found that the performance gain is proportional to the SU's antenna number, PUs' polarization angle and PUs' signal amplitude difference. In addition, the presented analytical studies are verified through extensive simulation results and the performance of PJVD-SE is validated.

Cognitive radio technology is proposed to improve spectrum utilization efficiency through exploiting the unused portions of spectrum in an opportunistic manner. In cognitive radio networks (CRNs), primary users (PUs) are allowed to lease out their unused spectrum to share with cognitive users (CUs). In a CRN consisting of multiple PUs and CUs, the spectrum pricing and resource allocation scheme have to be optimally designed, so that the efficient spectrum sharing among PUs and CUs can be supported and the QoS requirements of both PUs and CUs can be guaranteed. In this paper, we consider the joint spectrum pricing and resource allocation problem of a CRN with imperfect channel condition, i.e., channel state information of the PUs is not fully accessible to the CUs. To characterize the willingness of PUs to share subchannels and the intention of CU on accessing subchannels, a pricing scheme for both PUs and CUs is designed. Addressing the competition of CUs in channel accessing and power allocation, a non-cooperative Game model is formulated. Through solving the Nash equilibrium solution of the Game model, the optimal pricing and resource allocation strategy can be obtained. Numerical results demonstrate the efficiency of the proposed algorithm.

Outlier-aware cooperative spectrum sensing in cognitive radio networks

Wireless Communication is a system for communicating information from one point to other, without utilizing any connections like wire, cable, or other physical medium. Cognitive Radio (CR) based systems and networks are a revolutionary new perception in wireless communications. Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum. Centralized Cooperative Spectrum Sensing (CSS) is a kind of spectrum sensing. Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix (SCM) of the received signal. Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector (PRIDe), Hadamard Ratio (HR) detector, Gini Index Detector (GID), etc. This paper presents the simulation and comparative performance analysis of PRIDe with various other detectors like GID, HR, Arithmetic to Geometric Mean (AGM), Volume-based Detector number 1 (VD1), Maximum-to-Minimum Eigenvalue Detection (MMED), and Generalized Likelihood Ratio Test (GLRT) using the MATLAB software. The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

Energy detection (ED) technique is a vastly used sensing technique in CRNs because of its operational simplicity. However at low SNR, the performance of ED is badly degraded. Matched Filter (MF) detection is an alternate sensing technique at low SNR, as it increases SNR of the received signal. MF detector gives far better performance when compared to ED at low SNR. But the problem with MF detector is that it must have priori knowledge about Primary User (PU) signal, therefore we need dedicated MF detector for each PU. Motivated by above drawback of ED and MF in this paper we proposed a new MF technique by which requirement of priori knowledge about PU signal can be eliminated as well as performance at low SNR is improved. At the MF detector front end, we perform blind estimation of PU signal parameters and accordingly update the coefficient of MF transfer function. Blind Estimation of signal parameters solves the problem of having priori information about PU signal for MF detector. Performance analysis and comparison of ED, conventional MF detector and proposed MF detector also have been done in this paper which show that the proposed MF detector perform better than ED and almost same as the conventional MF detector.

Spectrum sensing ranks amongst cognitive radio's most critical and demanding activities. For radio resource networks to use idle licensed bandwidth, secondary users need to identify the primary user's signal precisely. Cognitive radio networks (CRNs) have been proposed to improve the efficiency of spectrum use to meet the increasing demand for wireless bandwidth; they allow channel communication between secondary (unlicensed) and primary (licensed) users without interruption. A secondary user in a CRN must at all times track the presence of a primary user's signal to prevent the primary user (PU) from attempting to intervene whether the statistical variance of the PU data traffic would boost the prediction outcome provided by multiple current teaching algorithms in estimating the PU's average OFF time and the need for the best adaptive learning to predict accurate PU activities. The objective of the work has been modeled to identify the PUs signal. First, the data has been created via quadrature phase shift keying modulation and quadrature amplitude modulation by convolutional neural network (CNN). A dataset with different orthogonal frequency‐division (OFDM) multiple access signals is generated. Then cyclo‐stationary functions are extracted using the accumulation process fast Fourier Transform. This research aims to use glow worm swarm optimization (GWSO) to automatically check for the optimal design of CNNs without any manual work being required. Also, k‐Nearest Neighbors (kNN) replaces Softmax, as it outperforms conventional CNN architectures with noisy signals. A comparison of the proposed classifier with another machine learning classifier, namely support vector machine, Decision tree (DT), clearly shows that SVM and DT are outperformed by the improved convolution neural network‐glow worm swarm optimization (ICNN‐GWSO) classification. Results of simulation show that the improved scheme outperforms conventional spectrum sensing systems, both in fading and non‐fading settings, where efficiency is measured using measures such as chance, the total likelihood of error, and the ability to optimize data transfer possibilities.

Cognitive radio (CR) is seen as a promising technology to make radio spectrum usage more effective by providing an opportunistic access for secondary users to the licensed spectrum areas. CR systems need to detect the presence of a primary user (PU) signal by continuously sensing the spectrum area of interest. Radiowave propagation effects like fading and shadowing often complicate sensing of spectrum holes because the PU signal can be weak in a particular area. Cooperative spectrum sensing is seen as a prospective solution to enhance the detection of PU signals. This paper studies distributed spectrum sensing in a cognitive radio context. We investigate distributed energy detection schemes without using any fusion center. We propose the usage of distributed, diffusion least mean square (LMS) type of power estimation algorithms. In this paper an Adapt and Combine (ATC) diffusion based power estimation scheme is proposed and the performance is compared with the Combine and Adapt (CTA) and ring-around schemes in a common framework. The PU signal is assumed to be slowly fading. We analyse the resulting energy detection performance and verify the theoretical findings through simulations.

In this paper, we propose a spectrum access method based on energy harvesting with optimal power allocation. Specifically, in the first phase, the primary user broadcasts its signal. The cognitive user receives the primary signal, and splits the power into two parts, one is to decode information, another is to harvest energy. In the second phase, the cognitive user forwards the primary signal by using the power harvested in the first phase, whlich assists the primary user to achieve the target rate. The cognitive user can access the primary spectrum to transmit its own signal by using its own power as a reward. We study the optimal power allocation to maximize the cognitive achievable rate, meanwhile the target rate of the primary user is achieved. Simulation results indicate that the proposed method can improve the performances of both the primary and cognitive users.