• Journal of information and communication convergence engineering
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• 제12권2호
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• pp.97-103
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• 2014

In spectrum sensing, there is a tradeoff between the probability of missed detection and the probability of a false alarm according to the value of the sensing threshold. Therefore, it is important to determine the sensing threshold suitable to the environment of cognitive radio networks. In this study, we consider a cognitive radio-based ad hoc network where secondary users directly communicate by using the same frequency band as the primary system and control their transmit power on the basis of the distance between them. First, we investigate a condition in which the primary and the secondary users can share the same frequency band without harmful interference from each other, and then, propose an algorithm that controls the sensing threshold dynamically on the basis of the transmit power of the secondary user. The analysis and simulation results show that the proposed sensing threshold control algorithm has low probabilities of both missed detection and a false alarm and thus, enables optimized spectrum sharing between the primary and the secondary systems.

• Journal of information and communication convergence engineering
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• 제9권4호
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• pp.391-395
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• 2011

Cognitive radio (CR) technique is a useful tool for improving spectrum utilization by detecting and using the vacant frequency bands while avoiding interference to the primary user. The sensing performance in a CR network can be improved by allowing some CR users to perform cooperative spectrum sensing. In this paper, we propose a new sensing algorithm that utilizes an adaptive energy threshold for cooperative spectrum sensing in which a changeable energy threshold is adopted by the CR users for improving local sensing performance. Through the proposed scheme, the reliability of global decision can be enhanced mainly due to the improvement in local sensing performance.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2368-2372
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• 2005

We proposed a novel method for object recognition using the Smart tag system in the previous research. We identified the object easily, but could not assure the object pose, because the threshold problem was not solved. So we propose a new method to solve this threshold problem. This method uses a smart tag to decide the threshold by recording color information of the image when the object feature is extracted. This method records the original of the object color information at the smart tag first. And then it records the object image information, the circumstance image information and the sensors information continuously when the object feature is extracted through the experiments. Finally, it estimates the current threshold by recorded information. This method can be applied the threshold to each objects. And it can solve the difficult threshold decision problem easily. To approve the possibility of our method, we implemented our approach by using easy and simple techniques as possible.

• 한국위성정보통신학회논문지
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• 제8권1호
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• pp.61-65
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• 2013

본 논문에서는 기존 에너지 스펙트럼 센싱 할 경우 고정된 FA를 기반으로 정해진 임계값에 따라 센싱이 진행된다. 하지만 SNR 상태가 높다면 낮은 레벨의 SNR에서보 비해 오경보 확률이 일어날 확률이 상대적으로 적어진다. 따라서 90%로 이상의 검출확률을 얻는 구간에 대하여 FA를 제어하는 방법으로 오경보 확률을 높게 설정하지 않아도 검출확률이 유지 되는지를 확인한다. 따라서 CR사용자의 SNR 상태에 따라 FA값을 컨트롤하여 적응적 임계값을 얻는 시스템 모델을 제안하고 성능을 분석한다.

• 한국위성정보통신학회논문지
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• 제8권1호
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• pp.66-70
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• 2013

본 논문에서는 협력하고자하는 소출력 기기들이 협력 스펙트럼 센싱 할 경우 협력 노드들이 같은 FA(False Alarm)가지고 있다 가정하며, 이때 최적의 임계값을 셋팅하고 서로 정보를 공유하는 시스템 모델을 제안하고 성능을 분석한다. 협력하고자하는 모든 노드의 False alarm이 같아도 각 채널에 따라 임계값이 달라지게 된다. 임계값이 낮아지면 검출확률이 낮아지게 되고, 반대로 임계값이 높을 때 검출확률은 높아지는 특성을 가지기 때문에, 따라서 가장 높음 임계값을 선택하여 세팅하고 공유하게 된다. 이는 협력스펙트럼 센싱시 가장 높은 임계값을 공유함으로써 고정되어 있는 임계값을 보다 높은 검출성능을 보일 수 있다.

• 한국통신학회논문지
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• 제40권2호
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• pp.253-263
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• 2015

최근 다수의 후 순위 사용자(Secondary User)가 각각의 검출 결과를 융합 센터(Fusion Center)에 보고하여 대역 검출의 성능을 향상시키기 위한 협력 대역 검출 기법이 이루어지고 있다. 또한 선 순위 사용자(Primary User)에게 할당된 주파수 대역을 융합 센터가 공유하는 인지 무선(Cognitive Radio)기술이 개발되고 있다. 이 논문에서는 분산 협력 대역 검출 환경에서 후 순위 사용자의 검출 정보가 융합 센터로 보고되는 채널의 오류 확률을 고려한 기존 분산 협력 대역 검출 기법의 성능 저하를 보완하는 새로운 분산 협력 대역 검출 기법을 제안하였다. 또한 분산 협력 대역 검출 기법의 오류 확률을 최소화 하는 검출 문턱값의 최적화 방법을 수식의 유도를 통해 제안하였다. 최적의 검출 문턱값은 분산 협력 대역 검출의 성능을 최대화 하는 것을 확인하였다.

• Journal of information and communication convergence engineering
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• 제9권3호
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• pp.251-255
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• 2011

In this paper, we investigate an adaptive cognitive radio (CR) scheme where a sensing duration and a detection threshold for spectrum sensing are adaptively determined according to the channel condition in a fading channel. We optimize the sensing duration and detection threshold of a secondary user to maximize the performance of the secondary user guaranteeing a primary user's secure communication. In addition, we analyze the effect of channel fading on the optimization of the sensing duration and detection threshold. Our numerical results show that the performance of the adaptive CR scheme can be drastically improved if a secondary user can take the advantage of channel information between primary and secondary users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권8호
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• pp.2755-2773
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• 2015

Cooperative spectrum sensing increases the detection performance in a cognitive radio network, based on the number of sensing nodes. However, as the number of sensing nodes increases, the reporting overhead linearly increases. This paper proposes two kinds of cooperative spectrum sensing with limited reporting in a centralized cognitive radio network, a soft combination with threshold-based reporting (SC-TR) and a soft combination with contention-based reporting (SC-CR). In the proposed SC-TR scheme, each sensing node reports its sensing result to the fusion center through its own reporting channel only if the observed energy value is higher than a decision threshold. In the proposed SC-CR scheme, sensing nodes compete to report their sensing results via shared reporting channels. The simulation results show that the proposed schemes significantly reduce the reporting overhead without sacrificing the detection performance too much.

• 대한원격탐사학회지
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• 제22권5호
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• pp.367-372
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• 2006

Cloud detection algorithm is being developed as primary one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-IR and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithms and preliminary test results of both algorithms.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.200-203
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• 2006

Cloud detection algorithm is being developed as major one of the 16 baseline products of CMDPS (COMS Meteorological Data Processing System), which is under development for the real-time application of data will be observed from COMS Meteorological Imager. For cloud detection from satellite data, we studied two different algorithms. One is threshold technique based algorithm, which is traditionally used, and another is artificial neural network model. MPEF scene analysis algorithm is the basic idea of threshold cloud detection algorithm, and some modifications are conducted for COMS. For the neural network, we selected MLP with back-propagation algorithm. Prototype software of each algorithm was completed and evaluated by using the MTSAT-1R and GOES-9 data. Currently the software codes are standardized using Fortran90 language. For the preparation as an operational algorithm, we will setup the validation strategy and tune up the algorithm continuously. This paper shows the outline of the two cloud detection algorithm and preliminary test result of both algorithms.