• 제목/요약/키워드: Optimal sensing period

검색결과 23건 처리시간 0.02초

Optimal sensing period in cooperative relay cognitive radio networks

  • Zhang, Shibing;Guo, Xin;Zhang, Xiaoge;Qiu, Gongan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제10권12호
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    • pp.5249-5267
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    • 2016
  • Cognitive radio is an efficient technique to improve spectrum efficiency and relieve the pressure of spectrum resources. In this paper, we investigate the spectrum sensing period in cooperative relay cognitive radio networks; analyze the relationship between the available capacity and the signal-to-noise ratio of the received signal of second users, the target probability of detection and the active probability of primary users. Finally, we derive the closed form expression of the optimal spectrum sensing period in terms of maximum throughput. We simulate the probability of false alarm and available capacity of cognitive radio networks and compare optimal spectrum sensing period scheme with fixed sensing period one in these performance. Simulation results show that the optimal sensing period makes the cognitive networks achieve the higher throughput and better spectrum sensing performance than the fixed sensing period does. Cooperative relay cognitive radio networks with optimal spectrum sensing period can achieve the high capacity and steady probability of false alarm in different target probability of detection. It provides a valuable reference for choosing the optimal spectrum sensing period in cooperative relay cognitive radio networks.

인지 무선 네트워크에서 시스템 비용함수를 이용한 적응적 센싱주기 (Sensing Period Adaptation using the Cost Function in the Cognitive Radio Networks)

  • 고상;박형근
    • 전기학회논문지
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    • 제61권2호
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    • pp.321-323
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    • 2012
  • Cognitive radio has been recently proposed to dynamically access unused-spectrum. Since the spectrum availability for opportunistic access is determined by spectrum sensing, sensing is identified as one of the most crucial issues of cognitive radio networks. The PHY-layer sensing, as a part of spectrum sensing in cognitive radio, concerns the sensing mechanism to determine channel to be sensed and to access. One of the important issues in the PHY-layer sensing control is to find an available sensing period and trade-off between spectrum sensing and data transmission. In this paper, we show the relationship between spectrum sensing and data transmission according to the sensing period. We analyze and propose the new scheme to evaluate optimal sensing period.

무선인지통신 시스템에서 최적 협업 센싱 방식 (Optimal cooperative sensing scheme in cognitive radio communication systems)

  • 이동준;이영진
    • 한국항행학회논문지
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    • 제12권5호
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    • pp.429-436
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    • 2008
  • 본 논문에서는 무선인지통신망에서 스펙트럼 센싱을 위한 협업 센싱을 하는 경우에 센싱 시간 및 협업 센싱 단말 수의 최적값들을 결정하는 방식에 대해서 다룬다. 협업 스펙트럼 센싱에서는 전송 중인 CU (Cognitive User) 외에 휴지중인 다른 CU들이 활성화되어 센싱에 참여함으로써 센싱 시간을 줄일 수 있다. 이로 인한 전송 CU의 전송률 이득과 CU들의 센싱으로 인한 에너지 소모간의 관계를 모두 고려하는 혼합 정수 계획법 문제를 풀어서 최적값들을 구할 수 있다.

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Throughput Maximization for Cognitive Radio Users with Energy Constraints in an Underlay Paradigm

  • Vu, Van-Hiep;Koo, Insoo
    • Journal of information and communication convergence engineering
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    • 제15권2호
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    • pp.79-84
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    • 2017
  • In a cognitive radio network (CRN), cognitive radio users (CUs) should be powered by a small battery for their operations. The operations of the CU often include spectrum sensing and data transmission. The spectrum sensing process may help the CU avoid a collision with the primary user (PU) and may save the energy that is wasted in transmitting data when the PU is present. However, in a time-slotted manner, the sensing process consumes energy and reduces the time for transmitting data, which degrades the achieved throughput of the CRN. Subsequently, the sensing process does not always offer an advantage in regards to throughput to the CRN. In this paper, we propose a scheme to find an optimal policy (i.e., perform spectrum sensing before transmitting data or transmit data without the sensing process) for maximizing the achieved throughput of the CRN. In the proposed scheme, the data collection period is considered as the main factor effecting on the optimal policy. Simulation results show the advantages of the optimal policy.

무선 인지 네트워크에서 군집형 데이터 슬롯의 미검출 확률 추정에 기반한 최적 스펙트럼 센싱 구조 (Optimal Spectrum Sensing Framework based on Estimated Miss Detection Probability for Aggregated Data Slots in Cognitive Radio Networks)

  • 우혁;이동준
    • 한국항행학회논문지
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    • 제17권5호
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    • pp.506-515
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    • 2013
  • 무선 인지 네트워크의 많은 연구는 한 번의 스펙트럼 센싱 수행으로 한 개의 데이터 슬롯을 전송하는 프레임 구조에 대하여 연구하였다. 프레임 길이가 짧은 경우, 데이터 전송률이 낮아지는 문제가 있다. 본 논문에서는 한 번의 스펙트럼 센싱 수행으로 다수의 데이터 슬롯을 전송하는 군집형 데이터 슬롯 구조에 대해서 연구한다. 이를 위해서 Chapman-Kolmogorov 방정식을 이용하여 선순위 사용자의 전송 확률을 모델링하고 이를 이용하여 후순위 사용자의 전송률을 최대화하기 위한 최적화 문제를 제시한다. 최적화 문제의 해를 구하여 최적의 스펙트럼 센싱 시간과 데이터 슬롯의 길이와 군집할 데이터 슬롯의 수를 도출한다.

Optimal Time Period for Using NDVI and LAI to Estimate Rice Yield

  • Yang, Chwen-Ming;Chen, Rong-Kuen
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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    • pp.10-12
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    • 2003
  • This study was to monitor changes of leaf area index (LAI) and normalized difference vegetation index (NDVI), calculated from ground-based remotely sensed high resolution reflectance spectra, during rice (Oryza sativa L. cv. TNG 67) growth so as to determine their relationships and the optimum time period to use these parameters for yield prediction. Field experiments were conducted at the experimental farm of TARI to obtain various scales of grain yield and values of LAI and NDVI in the first and the second cropping seasons of 2001-2002. It was found that LAI and NDVI can be mutually estimated through an exponential relationship, and hence plant growth information and spectral remote sensing data become complementary counterparts through this linkage. Correlation between yield and LAI was best fitted to a nonlinear function since about 7 weeks after transplanting (WAT). The accumulated and the mean values of LAI from 15 days before heading (DBH) to 15 days after heading (DAH) were the optimum time period to predict rice yield for First Crops, while values calculated from 15 DBH to 10 DAH were the optimal timing for Second Crops.

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Classification of Convective/Stratiform Radar Echoes over a Summer Monsoon Front, and Their Optimal Use with TRMM PR Data

  • Oh, Hyun-Mi;Heo, Ki-Young;Ha, Kyung-Ja
    • 대한원격탐사학회지
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    • 제25권6호
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    • pp.465-474
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    • 2009
  • Convective/stratiform radar echo classification schemes by Steiner et al. (1995) and Biggerstaff and Listemaa (2000) are examined on a monsoonal front during the summer monsoon-Changma period, which is organized as a cloud cluster with mesoscale convective complex. Target radar is S-band with wavelength of 10cm, spatial resolution of 1km, elevation angle interval of 0.5-1.0 degree, and minimum elevation angle of 0.19 degree at Jindo over the Korean Peninsula. For verification of rainfall amount retrieved from the echo classification, ground-based rain gauge observations (Automatic Weather Stations) are examined, converting the radar echo grid data to the station values using the inverse distance weighted method. Improvement from the echo classification is evaluated based on the correlation coefficient and the scattered diagram. Additionally, an optimal use method was designed to produce combined rainfalls from the radar echo and Tropical Rainfall Measuring Mission Precipitation Radar (TRMM/PR) data. Optimal values for the radar rain and TRMM/PR rain are inversely weighted according to the error variance statistics for each single station. It is noted how the rainfall distribution during the summer monsoon frontal system is improved from the classification of convective/stratiform echo and the use of the optimal use technique.

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

  • Kim, Nari;Lee, Yang-Won
    • 한국측량학회지
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    • 제34권4호
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    • pp.383-390
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    • 2016
  • Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods.

Study on spectral indices for crop growth monitoring

  • Zhang, Xia;Tong, Qingxi;Chen, Zhengchao;Zheng, Lanfeng
    • 대한원격탐사학회:학술대회논문집
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    • 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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    • pp.1400-1402
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    • 2003
  • The objective of this paper is to determine the suitable spectral bands for monitoring growth status change during a long period. The long-term ground-level reflectance spectra as well as LAI and biomass were obtained in xiaotangshan area, Beijing, 2001. The narrow-band NDVI type spectral indices by all possible two bands were calculated their correlation coefficients R$^2$ with biomass and LAI. The best NDVIs must have higher R$^2$ with both biomass and LAI. The reasonable band centers and band widths were determined by a systematically increasing bandwidth centered over a wavelength. In addition, the first 19 bands of MODIS were simulated and investigated. Each developed spectral indices was then validated by the biomass and LAI time series using the generalized vector angle. It turned out that six new NDVI type indices within 750-1400nm were developed. NDVI(811_10,957_10) and NDVI(962_10,802_10) performed best. No satisfactory conventional NDVI formed by red and NIR bands were found effective. MODIS_NDVI(band19, band17) and MODIS_NDVI(band19, band2) were much better than MODIS_NDVI(band2,band1) for growth monitoring.

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Throughput Analysis and Optimization of Distributed Collision Detection Protocols in Dense Wireless Local Area Networks

  • Choi, Hyun-Ho;Lee, Howon;Kim, Sanghoon;Lee, In-Ho
    • Journal of Communications and Networks
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    • 제18권3호
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    • pp.502-512
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    • 2016
  • The wireless carrier sense multiple access with collision detection (WCSMA/CD) and carrier sense multiple access with collision resolution (CSMA/CR) protocols are considered representative distributed collision detection protocols for fully connected dense wireless local area networks. These protocols identify collisions through additional short-sensing within a collision detection (CD) period after the start of data transmission. In this study, we analyze their throughput numerically and show that the throughput has a trade-off that accords with the length of the CD period. Consequently, we obtain the optimal length of the CD period that maximizes the throughput as a closed-form solution. Analysis and simulation results show that the throughput of distributed collision detection protocols is considerably improved when the optimal CD period is allocated according to the number of stations and the length of the transmitted packet.