• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1489-1503
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• 2018

Spectrum reuse has attracted much concern of researchers and scientists, however, the dynamic spectrum access is challenging, since an individual secondary user usually just has limited sensing abilities. One key insight is that spectrum usage forecasting among secondary users, this inspiration enables users to obtain more informed spectrum opportunities. Therefore, spectrum usage forecasting is vital to cognitive radio networks (CRNs). With this insight, a spectrum usage forecasting model for the occurrence of primary users prediction is derived in this paper. The proposed model is based on auto regressive enhanced primary user emergence reasoning (AR-PUER), which combines linear prediction and primary user emergence reasoning. Historical samples are selected to train the spectrum usage forecasting model in order to capture the current distinction pattern of primary users. The proposed scheme does not require the knowledge of signal or of noise power. To verify the performance of proposed spectrum usage forecasting model, we apply it to the data during the past two months, and then compare it with some other sensing techniques. The simulation results demonstrate that the spectrum usage forecasting model is effective and generates the most accurate prediction of primary users occasion in several cases.

• Journal of Internet Computing and Services
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• v.12 no.2
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• pp.1-8
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• 2011

In this paper, we propose an effective method for cooperative spectrum sensing in cognitive radios where cognitive user(CR) with the highest reliability sensing data is only selected and allowed to report its local decision to FC as only decision making node. The proposed scheme enables CR users to implicitly compare their sensing data reliabilities based on their likelihood ratio, without any collaboration among cognitive radio users. Due to the mechanism, the proposed cooperative scheme can achieves a high spectrum sensing performance while only requiring extremely low cooperation resources such as signaling overhead and cooperative time in comparison with other existing methods such as maximum ratio combination (MRC) based, equal gain combination (EGC) based and conventional hard combination based cooperative sensing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1301-1312
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• 2020

Using decode-and-forward relaying in the cognitive radio networks, the spectrum efficiency can improve furthermore. The optimization algorithm of the spectrum sensing estimation time is presented for the cognitive relay networks in this paper. The longer sensing time will bring two aspects of the consequences. On the one hand, the channel parameters are estimated more accurate so as to reduce the interferences to the authorized users and to improve the throughput of the cognitive users. On the other hand, it shortens the transmission time so as to decease the system throughput. In this time, it exists an optimal sensing time to maximize the throughput. The channel state information of the sub-bands is considered as the exponentially distributed, so a stochastic programming method is proposed to optimize the sensing time for the cognitive relay networks. The computer simulation results using the Matlab software show that the algorithm is effective, which has a certain engineering application value.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.2
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• pp.184-191
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• 2017

This paper presents cluster based hybrid spectrum sensing system for M2M services. For each cluster, secondary nodes within the transmission radius of the primary node use hard decision method through local spectrum sensing to determine whether the primary node exists. And the other secondary nodes and the secondary nodes having poor radio channel conditions judge the existence of the primary node through the soft decision method of the values obtained by performing the cooperative spectrum sensing. In the proposed hybrid spectrum sensing system, the performance according to the number of secondary nodes is analyzed with the conventional system over Rayleigh fading channel. As the number of cooperative sensing users increased to 2, 3 and 4, the cluster error probability decreased to 0.5608, 0.5252 and 0.4001 at SNR of -10[dB] respectively. Since the proposed system uses less overhead traffic, it is found that it is more effective in terms of frequency usage than the conventional system using soft decision-soft decision and soft decision-hard decision methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.8C
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• pp.655-662
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• 2012

In this work, we propose an airborne cognitive radio system that searches a new spectrum band to avoid a communication interruption due to the interference from many radar signals. We develop a method of fast spectrum sensing based on an effective frequency by recognizing the interfering radar as well as geographical information. This effective frequency is calculated by the free-space path loss between a base station and a fighter with the speed parameter. From our analysis, it is verified that the maximum frequency searching time is reduced by half by using our method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.3-7
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• 2015

Recently, a lot of research effort has been directed toward spectrum sensing and identification of OFDM signals as the OFDM technique has been adopted for transmission in many wireless communications standards. Among them, two popular WLAN standards, IEEE 802.11a and IEEE 802.11n, have a very similar OFDM symbol structure in terms of the lengths of CP(cyclic prefix) and effective OFDM symbol and so it is not straightforward to distinguish them with existing spectrum sensing methods based on the difference in the parameters. In this paper, we present a spectrum sensing strategy for identifying them by exploiting the fact that they employ different bandwidths and examine its performance.

• Journal of Communications and Networks
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• v.13 no.4
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• pp.360-366
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• 2011

In this paper, we investigate the problem of power allocation in a heterogeneous network that is composed of a pair of cognitive users (CUs) and an infrastructure-based primary network. Since CUs have only limited effective spectrum-sensing ability and primary users (PUs) are not active all the time in all locations and licensed bands, we set up a new multi-area model to characterize the heterogeneous network. A novel combined interference-avoidance policy corresponding to different PU-appearance situations is introduced to protect the primary network from unacceptable disturbance and to increase the spectrum secondary-reuse efficiency. We use dual decomposition to transform the original power allocation problem into a two-layer optimization problem. We propose a low-complexity joint power-optimizing method to maximize the transmission rate between CUs, taking into account both the individual power-transmission constraints and the combined interference power constraint of the PUs. Numerical results show that for various values of the system parameters, the proposed joint optimization method with combined PU protection is significantly better than the opportunistic spectrum access mode and other heuristic approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4224-4243
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• 2021

Cognitive radio (CR) is a feasible intelligent technology and can be used as an effective solution to spectrum scarcity and underutilization. As the key function of CR, cooperative spectrum sensing (CSS) is able to effectively prevent the harmful interference with primary users (PUs) and identify the available spectrum resources by exploiting the spatial diversity of multiple secondary users (SUs). However, the open nature of the cognitive radio networks (CRNs) framework makes CSS face many security threats, such as, the malicious user (MU) launches Byzantine attack to undermine CRNs. For this aim, we make an in-depth analysis of the motive and purpose from the MU's perspective in the interweave CR system, aiming to provide the future guideline for defense strategies. First, we formulate a dynamic Byzantine attack model by analyzing Byzantine behaviors in the process of CSS. On the basis of this, we further make an investigation on the condition of making the fusion center (FC) blind when the fusion rule is unknown for the MU. Moreover, the throughput and interference to the primary network are taken into consideration to evaluate the impact of Byzantine attack on the interweave CR system, and then analyze the optimal strategy of Byzantine attack when the fusion rule is known. Finally, theoretical proofs and simulation results verify the correctness and effectiveness of analyses about the impact of Byzantine attack strategy on the throughput and interference.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.11A
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• pp.1105-1116
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• 2008

Cognitive radio is one of the most effective techniques to improve the spectrum utilization efficiency. To implement the cognitive radio, spectrum sensing is considered as the key functionality because only counting on it, can the secondary users identify the spectrum holes and utilize them efficiently without causing interference to primary users. Generally, there are several spectrum sensing methods; the most common and simplest method is energy detection. However, the conventional energy detection has some disadvantages, which are caused by noise, especially, uncertain noise power leads to degradation of energy detector. In this paper, to solve this problem, we proposed sliding window-based energy detection method which can devide the frequency band of primary signal and noise using sliding window to estimate the power of primary user without the noise effect and achieve the better performance. It can calculate the energy of primary user only and can detect the primary signal. The simulation result shows that our proposed method outperforms conventional one.