Detection of hidden users in cognitive radio networks

This work addresses the problem of estimating the interference temperature in cognitive radio systems. The proposed technique exploits higher order moments of the received signal to achieve accurate estimation of the noise and signal power. Then, the noise power is used for the evaluation of the interference temperature. Performance analysis has been carried out in comparison with the conventional energy detection method. The numerical results, obtained from wide simulation trials, have evidenced the efficiency of our method for estimating the interference temperature in cognitive radio communications.

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“…According to [18], let M 2 be the second-order moment of the received signal can be expressed as follows: …”

Section: Interference Temperature Estimationmentioning

confidence: 99%

Improving the interference temperature estimation for Dynamic Spectrum Access in cognitive radios

Benedetto

Giunta

Guzzon

et al. 2013

2013 IEEE Global Conference on Signal and Information Processing

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“…The concept of looking into the radio frequency spectrum and identifying an idle channel to start transmission with optimum communications protocols (i.e., modulation techniques, appropriate transmission power levels, etc., suited to the environment) is known as cognitive radio [1,2]. This concept was proposed to overcome the spectrum scarcity problem in the currently available frequency bands.…”

Section: Introductionmentioning

confidence: 99%

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

Jan

Koo

2018

Applied Sciences

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A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis) is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission power based on the hypothesis result (the energy level of the sensed signal) and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM) is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node's battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the conventional two-class hypothesis for spectrum sensing in cognitive radio networks.

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“…Energy detectors are computationally less complex in comparison with all other detectors, which measure the received signal energy and compare the result with a preselected constant to take decision of the PU spectrum. The low signal-to-noise ratio (SNR) environment of the fading channel and noise uncertainty drastically reduces the performance of energy detector and it is, therefore, difficult to get reliable spectrum decisions with them [4,5].…”

Section: Introductionmentioning

confidence: 99%

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

Gul

Qureshi

Elahi

et al. 2018

International Journal of Antennas and Propagation

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In cognitive radio network (CRN), secondary users (SUs) try to sense and utilize the vacant spectrum of the legitimate primary user (PU) in an efficient manner. The process of cooperation among SUs makes the sensing more authentic with minimum disturbance to the PU in achieving maximum utilization of the vacant spectrum. One problem in cooperative spectrum sensing (CSS) is the occurrence of malicious users (MUs) sending false data to the fusion center (FC). In this paper, the FC takes a global decision based on the hard binary decisions received from all SUs. Genetic algorithm (GA) using one-to-many neighbor distance along with z-score as a fitness function is used for the identification of accurate sensing information in the presence of MUs. The proposed scheme is able to avoid the effect of MUs in CSS without identification of MUs. Four types of abnormal SUs, opposite malicious user (OMU), random opposite malicious user (ROMU), always yes malicious user (AYMU), and always no malicious user (ANMU), are discussed in this paper. Simulation results show that the proposed hard fusion scheme has surpassed the existing hard fusion scheme, equal gain combination (EGC), and maximum gain combination (MGC) schemes by employing GA.

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Detection of hidden users in cognitive radio networks

Cited by 20 publications

References 22 publications

Improving the interference temperature estimation for Dynamic Spectrum Access in cognitive radios

Improving the interference temperature estimation for Dynamic Spectrum Access in cognitive radios

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

Defense against Malicious Users in Cooperative Spectrum Sensing Using Genetic Algorithm

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