• Title/Summary/Keyword: 커버리지 모델링

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Cooperative Spectrum Sensing in Cognitive Radio Systems with Weight Value Applied (인지무선 시스템에서 부사용자의 거리에 따른 가중치가 적용된 협력 스펙트럼 센싱)

  • Yun, Heesuk;Yun, Jaesoon;Bae, Insan;Jang, Sunjeen;Kim, Jaemoung
    • Journal of Satellite, Information and Communications
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    • v.9 no.3
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    • pp.91-97
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    • 2014
  • In this paper, we propose weighted detection probability with distance between primary user and secondary users by using cooperative spectrum sensing based on energy detection. And we analysis and simulate the result. We suggest different distance between primary user and secondary users and the wireless channel between primary user and secondary users is modeled as Gaussian channel. From the simulation results of the cooperative spectrum sensing with weighted method make coverage bigger compared with non-weight, and We show higher sensing efficiency when we put weight detection probability than before method.

Triangular Prism Method Based on an Enhanced Sampling Method (개선된 샘플링 방법에 기초한 삼각프리즘법)

  • Jin, Gang-Gyoo
    • Journal of the Korean Institute of Intelligent Systems
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    • v.23 no.2
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    • pp.93-99
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    • 2013
  • Fractal theory has been adopted as an effective tool for modelling complex and irregular natural phenomena facing in the fields of Computer Science, Engineering, Medical, Climatology and so on. In this paper, we presents an algorithm which enhances the performance of the triangular prism method(TPM) which has been widely used for fractal dimension extraction of natural terrains and images. For this, existing sampling methods are analyzed and a new sampling method which takes their merits is proposed. The effectiveness of the proposed algorithm is tested on fractal terrain maps and its performance is compared with that of other methods.

Energy Efficiency Enhancement of Macro-Femto Cell Tier (매크로-펨토셀의 에너지 효율 향상)

  • Kim, Jeong-Su;Lee, Moon-Ho
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.1
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    • pp.47-58
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    • 2018
  • The heterogeneous cellular network (HCN) is most significant as a key technology for future fifth generation (5G) wireless networks. The heterogeneous network considered consists of randomly macrocell base stations (MBSs) overlaid with femtocell base stations (BSs). The stochastic geometry has been shown to be a very powerful tool to model, analyze, and design networks with random topologies such as wireless ad hoc, sensor networks, and multi- tier cellular networks. The HCNs can be energy-efficiently designed by deploying various BSs belonging to different networks, which has drawn significant attention to one of the technologies for future 5G wireless networks. In this paper, we propose switching off/on systems enabling the BSs in the cellular networks to efficiently consume the power by introducing active/sleep modes, which is able to reduce the interference and power consumption in the MBSs and FBSs on an individual basis as well as improve the energy efficiency of the cellular networks. We formulate the minimization of the power onsumption for the MBSs and FBSs as well as an optimization problem to maximize the energy efficiency subject to throughput outage constraints, which can be solved the Karush Kuhn Tucker (KKT) conditions according to the femto tier BS density. We also formulate and compare the coverage probability and the energy efficiency in HCNs scenarios with and without coordinated multi-point (CoMP) to avoid coverage holes.

Automatic Test Case Generation Through 1-to-1 Requirement Modeling (1대1 요구사항 모델링을 통한 테스트 케이스 자동 생성)

  • Oh, Jung-Sup;Choi, Kyung-Hee;Jung, Gi-Hyun
    • The KIPS Transactions:PartD
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    • v.17D no.1
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    • pp.41-52
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    • 2010
  • A relation between generated test cases and an original requirement is important, but it becomes very complex because a relation between requirement models and requirements are m-to-n in automatic test case generation based on models. In this paper, I suggest automatic generation technique for REED (REquirement EDitor), 1-to-1 requirement modeling tool. Test cases are generated though 3 steps, Coverage Target Generation, IORT (Input Output Relation Tree)Generation, and Test Cases Generation. All these steps are running automatically. The generated test cases can be generated from a single requirement. As a result of applying to three real commercial systems, there are 5566 test cases for the Temperature Controller, 3757 test cases for Bus Card Terminal, and 4611 test cases for Excavator Controller.

Data Congestion Control Using Drones in Clustered Heterogeneous Wireless Sensor Network (클러스터된 이기종 무선 센서 네트워크에서의 드론을 이용한 데이터 혼잡 제어)

  • Kim, Tae-Rim;Song, Jong-Gyu;Im, Hyun-Jae;Kim, Bum-Su
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.21 no.7
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    • pp.12-19
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    • 2020
  • The clustered heterogeneous wireless sensor network is comprised of sensor nodes and cluster heads, which are hierarchically organized for different objectives. In the network, we should especially take care of managing node resources to enhance network performance based on memory and battery capacity constraints. For instances, if some interesting events occur frequently in the vicinity of particular sensor nodes, those nodes might receive massive amounts of data. Data congestion can happen due to a memory bottleneck or link disconnection at cluster heads because the remaining memory space is filled with those data. In this paper, we utilize drones as mobile sinks to resolve data congestion and model the network, sensor nodes, and cluster heads. We also design a cost function and a congestion indicator to calculate the degree of congestion. Then we propose a data congestion map index and a data congestion mapping scheme to deploy drones at optimal points. Using control variable, we explore the relationship between the degree of congestion and the number of drones to be deployed, as well as the number of drones that must be below a certain degree of congestion and within communication range. Furthermore, we show that our algorithm outperforms previous work by a minimum of 20% in terms of memory overflow.