• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.890-910
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• 2014

This paper investigates the spectrum hole utilization of cooperative schemes for the two-way relaying model in order to improve the utilization efficiency of limited spectrum holes in cognitive radio networks with imperfect spectrum sensing. We propose two specific bidirectional secondary data transmission (BSDT) schemes with two-step and three-step two-way relaying models, i.e., two-BSDT and three-BSDT schemes, where the spectrum sensing and the secondary data transmission are jointly designed. In the proposed cooperative schemes, the best two-way relay channel between two secondary users is selected from a group of secondary users serving as cognitive relays and assists the bi-directional communication between the two secondary users without a direct link. The closed-form asymptotic expressions for outage probabilities of the two schemes are derived with a primary user protection constraint over Rayleigh fading channels. Based on the derived outage probabilities, the spectrum hole utilization is calculated to evaluate the percentage of spectrum holes used by the two secondary users for their successful information exchange without channel outage. Numerical results show that the spectrum hole utilization depends on the spectrum sensing overhead and the channel gain from a primary user to secondary users. Additionally, we compare the spectrum hole utilization of the two schemes as the varying of secondary signal to noise ratio, the number of cognitive relays, and symmetric and asymmetric channels.

• Journal of Advanced Navigation Technology
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• v.24 no.2
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• pp.121-127
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• 2020

In this paper, we propose a dynamic spectrum hole grouping method that changes the grouping range of spectrum hole according to the resources amount required by secondary users in wideband spectrum cognitive wireless networks, and then the proposed method is applied to channel allocation for the secondary user service. The proposed method can improve waste of resources in the existing static spectrum hole grouping in virtue of grouping dynamically as much the predicted spectrum holes resources as secondary users require. Simulation results show that channel allocation method with the proposed dynamic grouping outperforms that with the static grouping method in resources utilization under acceptable secondary user service performance.

• Journal of Communications and Networks
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• v.16 no.2
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• pp.209-216
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• 2014

Cognitive radio (CR) has been proposed to solve the spectrum utilization problem by dynamically exploiting the unused spectrum. In CR networks, a spectrum selection scheme is an important process to efficiently exploit the spectrum holes, and an efficient channel allocation scheme must be designed to minimize interference to the primary network as well as to achieve better spectrum utilization. In this paper, we propose a multichannel selection algorithm that uses spectrum hole prediction to limit the interference to the primary network and to exploit channel characteristics in order to enhance channel utilization. The proposed scheme considers both the interference length and the channel capacity to limit the interference to primary users and to enhance system performance. By using the proposed scheme, channel utilization is improved whereas the system limits the collision rate of the CR packets.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1767-1769
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• 2011

In a CR (cognitive radio) network, channel selection is one of the important issues for the efficient channel utilization. When the CR user exploits the spectrum of primary network, the interference to the primary network should be minimized. In this paper, we propose a spectrum hole prediction based channel selection scheme to minimize the interference to the primary network. To predict spectrum hole, statistic properties of primary user's traffic is used. By using the predicted spectrum hole, channel is selected and it can reduce the possibility of interference to the primary user and increase the efficiency of spectrum utilization. The performance of proposed channel selection scheme is evaluated by the computer simulation.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.219-225
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• 2022

In this paper, we propose a CAC exploring a RA based dynamic spectrum hole grouping for secondary users' QoS enhancement in multi-classes cognitive radio networks. The RA based dynamic spectrum hole grouping uses SU multi-classes overlaying spectrum structure suggested here. Multiclass SUs are divided into real and non real, and real SUs have a priority for resource utilization against non real. The amount of resource required by real SUs is supported by Wiener prediction and the dynamic spectrum hole grouping, and that required by non real SU is supported by the remained available amount without prediction. In the simulations, we compare the proposed CAC performances using the dynamic spectrum hole grouping in terms of SU connection's blocking(dropping) rate and resource utilization efficiency according to multi-classes traffic characteristics, and then we show the proposed CAC can guarantee the desired QoS of multi-classes secondary users.

• Journal of information and communication convergence engineering
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• v.8 no.3
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• pp.273-276
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• 2010

Opportunistic spectrum access (OSA) allows unlicensed users to share licensed spectrum in space and time with no or little interference to primary users, with bring new research challenges in MAC design. We propose a cognitive MAC protocol using statistical channel information and selecting appropriate idle channel for transmission. The protocol based on the CSMA/CA, exploits statistics of spectrum usage for decision making on channel access. Idle channel availability, spectrum hole sufficiency and available channel condition will be included in algorithm statistical information. The model include the control channel and data channel, the transmitter negotiates with receiver on transmission parameters through control channel, statistical decision results (successful rate of transmission) from exchanged transmission parameters of control channel should pass the threshold and decide the data transmission with spectrum hole on data channel. The proposed protocol's simulation will show that proposed protocol does improve the throughput performance via traditional opportunistic spectrum access MAC protocol.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.728-729
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• 2010

Opportunistic spectrum access (OSA) allows unlicensed users to share licensed spectrum in space and time with no or little interference to primary users, with bring new research challenges in MAC design. We propose a cognitive MAC protocol using statistical channel utilization information and selecting appropriate spectrum hole for multi-channel data transmission. The protocol based on the CSMA/CA, exploits statistics of spectrum usage for decision making on channel access.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.12
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• pp.1755-1757
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• 2013

Dynamically exploiting unused-spectrum, cognitive radio has been proposed to solve spectrum utilization problem. In cognitive radio, it is important to minimize the interference to primary service as well as to provide efficient channel allocation. In this paper, we propose a multi-channel allocation scheme based on spectrum hole prediction. Proposed scheme considered both interference length and channel capacity to limit the interference to primary user as well as to enhance system performance. Simulation results show the proposed scheme improves the system throughput.

• Journal of Communications and Networks
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• v.11 no.2
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• pp.122-133
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• 2009

As wireless spectrum resources become more scarce while some portions of frequency bands suffer from low utilization, the design of cognitive radio (CR) has recently been urged, which allows opportunistic usage of licensed bands for secondary users without interference with primary users. Spectrum sensing is fundamental for a secondary user to find a specific available spectrum hole. Cooperative spectrum sensing is more accurate and more widely used since it obtains helpful reports from nodes in different locations. However, if some nodes are compromised and report false sensing data to the fusion center on purpose, the accuracy of decisions made by the fusion center can be heavily impaired. Weighted sequential probability ratio test (WSPRT), based on a credit evaluation system to restrict damage caused by malicious nodes, was proposed to address such a spectrum sensing data falsification (SSDF) attack at the price of introducing four times more sampling numbers. In this paper, we propose two new schemes, named enhanced weighted sequential probability ratio test (EWSPRT) and enhanced weighted sequential zero/one test (EWSZOT), which are robust against SSDF attack. By incorporating a new weight module and a new test module, both schemes have much less sampling numbers than WSPRT. Simulation results show that when holding comparable error rates, the numbers of EWSPRT and EWSZOT are 40% and 75% lower than WSPRT, respectively. We also provide theoretical analysis models to support the performance improvement estimates of the new schemes.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1708-1717
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• 2023

The grade analysis of lead-zinc ore is the basis for the optimal development and utilization of deposits. In this study, a method combining Prompt Gamma Neutron Activation Analysis (PGNAA) technology and machine learning is proposed for lead-zinc mine borehole logging, which can identify lead-zinc ores of different grades and gangue in the formation, providing real-time grade information qualitatively and semi-quantitatively. Firstly, Monte Carlo simulation is used to obtain a gamma-ray spectrum data set for training and testing machine learning classification algorithms. These spectra are broadened, normalized and separated into inelastic scattering and capture spectra, and then used to fit different classifier models. When the comprehensive grade boundary of high- and low-grade ores is set to 5%, the evaluation metrics calculated by the 5-fold cross-validation show that the SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naive Bayes) and RF (Random Forest) models can effectively distinguish lead-zinc ore from gangue. At the same time, the GNB model has achieved the optimal accuracy of 91.45% when identifying high- and low-grade ores, and the F₁ score for both types of ores is greater than 0.9.