• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.110-120
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• 2008

Cognitive Radio (CR) technology is proposed for using the unused spectrum band efficiently because of the spectrum scarcity problems. Spectrum sensing technology is one of the key challenge issues in cognitive radio technologies, which enables unlicensed users to identify and utilize vacant spectrum resource allocated to primary users. In this paper, the cooperative spectrum sensing technologies apply the ITS(Intelligent Transport System) and performance of signal detection analyzes. Then, we utilize the OR-rule and AND-rule for the cooperative signal detection. These data fusion rules improve the performance and reliability of the signal detection.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.943-951
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• 2012

In this paper, we propose a novel spectrum sensing scheme based on the order statistic for cooperative cognitive radio network in non-Gaussian noise environments. Specifically, we model the ambient noise as the bivariate isotropic symmetric ${\alpha}$-stable random variable, and then, propose a cooperative spectrum sensing scheme based on the order of observations and the generalized likelihood ratio test. From numerical results, it is confirmed that the proposed scheme offers a substantial performance improvement over the conventional scheme in non-Gaussian noise environments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1754-1769
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• 2013

For the purpose of enhancing the spectrum efficiency, cognitive radio (CR) technology has been recently proposed as a promising dynamic spectrum allocation paradigm. In CR, spectrum sensing is the key capability of secondary users in a cognitive radio network that aims for reducing the probability of harmful interference with primary users. However, the individual CRs might not be able to carry out reliable detection of the presence of a primary radio due to the impact of channel fading or shadowing. This paper studies the cooperative spectrum sensing scheme as means of optimizing the sensing performance in AWGN and Rayleigh channels. Results generated from simulation provide evidence of the impact of channel condition on the complementary receiver operating characteristic (ROC). Based on the results, it was found that with constant local SNRs at the secondary users, the probability of missed detection ($P_m$) of cooperative spectrum sensing in a cognitive radio network, calculated using a closed form expression, can be significantly minimized. Thus, the paper illustrates that improvement of the detection performance of the CR network can be achieved by establishing a centralized cooperation among neighboring cognitive radio users. Finally, verification of the validity of the fusion schemes utilized for combining the individual CR decisions is provided.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.1A
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• pp.87-94
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• 2010

It is widely known that cooperative spectrum sensing in which secondary users scattered in some region collaborate to detect primary users can significantly reduce the performance degradation due to the fading phenomenon. Most of previous works on cooperative spectrum sensing are based on the assumption that the local spectrum sensing decisions of secondary users are statistically independent. However, there can be practically some statistical correlation between the local decisions of any two secondary users in close proximity, which is caused by shadowing effect. In order to evaluate the effect of this correlation on the performance of collaborative spectrum sensing, we assumed that, for the case that a primary user are active in the spectrum of interest, any two local decisions are statistically correlated to each other with some level of constant correlation and independent otherwise, and analyzed the achievable throughput with the degree of correlation varying. The results showed that, as the degree of correlation gets higher, the throughput increases for the case of the AND fusion rule and decreases for the OR fusion rule.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1392-1413
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• 2022

In order to meet the rapid development of the unmanned aerial vehicle (UAV) communication needs, cooperative spectrum sensing (CSS) helps to identify unused spectrum for the primary users (PU). However, multi-UAV mode (MUM) requires the large communication resource in a cognitive UAV network, resulting in a severe decline of spectrum efficiency (SE) and energy efficiency (EE) and increase of energy consumption (EC). On this account, we extend the traditional 2D spectrum space to 3D spectrum space for the UAV network scenario and enable UAVs to proceed with spectrum sensing behaviors in this paper, and propose a novel multi-slot mode (MSM), in which the sensing slot is divided into multiple mini-slots within a UAV. Then, the CSS process is developed into a composite hypothesis testing problem. Furthermore, to improve SE and EE and reduce EC, we use the sequential detection to make a global decision about the PU channel status. Based on this, we also consider a truncation scenario of the sequential detection under the sensing delay constraint, and further derive a closed-form performance expression, in terms of the CSS performance and cooperative efficiency. At last, the simulation results verify that the performance and cooperative efficiency of MSM outperforms that of the traditional MUM in a low EC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.287-293
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• 2013

Cognitive radio has been recently proposed to dynamically access unused-spectrum. The CR users can share the same frequency band with the primary user without interference to each other. Usually each CR user needs to determine spectrum availability by itself depending only on its local observations. But uncertainty communication environment effects can be mitigated so that the detection probability is improved in a heavily shadowed environment. Soft detection is a primary user detection method of cooperative cognitive radio networks. In our research, we will improve system detection probability by using optimal cooperative node selection algorithm. New algorithm can find optimal number of cooperative sensing nodes for cooperative soft detection by using maximum ratio combining (MRC) method. Through analysis, proposed cooperative node selection algorithm can select optimal node for cooperative sensing according to the system requirement and improve the system detection probability.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.4
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• pp.2038-2057
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• 2017

Gaussian learning is a new technology in the computational intelligence area. However, this technology weakens the learning ability of a particle swarm and achieves a lack of diversity. Thus, this paper proposes a vector Gaussian learning strategy and presents an effective approach, named particle swarm optimization based on vector Gaussian learning. The experiments show that the algorithm is more close to the optimal solution and the better search efficiency after we use vector Gaussian learning strategy. The strategy adopts vector Gaussian learning to generate the Gaussian solution of a swarm's optimal location, increases the learning ability of the swarm's optimal location, and maintains the diversity of the swarm. The method divides the states into normal and premature states by analyzing the state threshold of the swarm. If the swarm is in the premature category, the algorithm adopts an inertia weight strategy that decreases linearly in addition to vector Gaussian learning; otherwise, it uses a fixed inertia weight strategy. Experiments are conducted on eight well-known benchmark functions to verify the performance of the new approach. The results demonstrate promising performance of the new method in terms of convergence velocity and precision, with an improved ability to escape from a local optimum.

• Journal of Internet Computing and Services
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• v.12 no.1
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• pp.1-10
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• 2011

The spectrum sensing is one of the fundamental functions to realize the cognitive radios. One of problems in the spectrum sensing is that the performance of spectrum sensing can be degraded due to fading and shadowing. In order to overcome the problem, cooperative spectrum sensing method is proposed, which uses a distributed detection model and can increase sensing performance. However, the performance of cooperative spectrum sensing can be still affected by the interference factors such as obstacle and malicious user. Especially, most of cooperative spectrum sensing methods only considered the stationary primary user. In the ubiquitous environment, however the mobile primary users should be considered. In order to overcome the aforementioned problem, in this paper we propose a robust spectrum detection method based on localization where we estimate the location of the mobile primary user, and then based on the location and transmission range of primary user we detect interference users if there are, and then the local sensing reporting from detected interference users are excluded in the decision fusion process. Through simulation, it is shown that the sensing performance of the proposed scheme is more accurate than that of conventional other schemes

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.4
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• pp.39-46
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• 2013

Cooperative spectrum sensing can improve sensing reliability, compared with single node spectrum sensing. In addition, Eigenvalue-based spectrum sensing has also drawn a great attention due to its performance improvement over the energy detection method in which the more smoothing factor, the better performance is achieved. However, the more smoothing factor in Eignevalue-based spectrum sensing requires the more sensing time. Furthermore, more reporting time in cooperative sensing will be required as the number of nodes increases. Subsequently, we in this paper propose an Eigenvalue and superposition-based spectrum sensing where the reporting time is utilized so as to increase the number of smoothing factors for autocorrelation calculation. Simulation result demonstrates that the proposed scheme has better detection probability in both local as well as global detection while requiring less sensing time as compared with conventional Eigenvalue-based detection scheme.

• Journal of electromagnetic engineering and science
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• v.10 no.3
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• pp.190-198
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• 2010

This paper evaluates the performance of two methods of spectrum sensing: the linear combining method and the selection combining method which is based on maximum SNR of sensing channel. We proposed a rule for global detection for the purpose of combating hidden terminal problems in spectrum sensing. Our analysis considers a situation when sensing channels experience the non-identically, independently distributed(n.i.d) Rayleigh fading. The average probabilities of global detection in these methods are derived and compared. In the scope of this paper, the reporting channels are assumed to be the AWGN channel with invariant and identical gain during the system's operation.