K Factor

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Christoph F Mecklenbrauker - One of the best experts on this subject based on the ideXlab platform.

  • time and frequency varying K Factor of non stationary vehicular channels for safety relevant scenarios
    IEEE Transactions on Intelligent Transportation Systems, 2015
    Co-Authors: Laura Bernado, Andreas F. Molisch, Thomas Zemen, Fredrik Tufvesson, Christoph F Mecklenbrauker
    Abstract:

    Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to fast changes in the environment. We characterize the distribution of the envelope of the first delay bin in vehicle-to-vehicle channels by means of its Rician $K$ -Factor. We analyze the time–frequency variability of this channel parameter using vehicular channel measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant scenarios in intelligent transportation systems (ITS) . This data enables a frequency-variability analysis from an IEEE 802.11p system point of view, which uses 10 MHz channels. We show that the small-scale fading of the envelope of the first delay bin is Rician distributed with a varying $K$ -Factor. The later delay bins are Rayleigh distributed. We demonstrate that the $K$ -Factor cannot be assumed to be constant in time and frequency. The causes of these variations are the frequency-varying antenna radiation patterns, as well as the time-varying number of active scatterers, and the effects of vegetation. We also present a simple but accurate bimodal Gaussian mixture model, which allows to capture the $K$ -Factor variability in time for safety-relevant ITS scenarios.

Bertrand Vignal - One of the best experts on this subject based on the ideXlab platform.

  • forecasting electricity spot marKet prices with a K Factor gigarch process
    Applied Energy, 2009
    Co-Authors: Abdou Kâ Diongue, Dominique Guegan, Bertrand Vignal
    Abstract:

    In this article, we investigate conditional mean and conditional variance forecasts using a dynamic model following a K-Factor GIGARCH process. Particularly, we provide the analytical expression of the conditional variance of the prediction error. We apply this method to the German electricity price marKet for the period August 15, 2000-December 31, 2002 and we test spot prices forecasts until one-month ahead forecast. The forecasting performance of the model is compared with a SARIMA-GARCH benchmarK model using the year 2003 as the out-of-sample. The proposed model outperforms clearly the benchmarK model. We conclude that the K-Factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria.

  • forecasting electricity spot marKet prices with a K Factor gigarch process
    Documents de travail du Centre d'Economie de la Sorbonne, 2007
    Co-Authors: Abdou Kâ Diongue, Dominique Guegan, Bertrand Vignal
    Abstract:

    In this article, we investigate conditional mean and variance forecasts using a dynamic model following a K-Factor GIGARCH process. We are particularly interested in calculating the conditional variance of the prediction error. We apply this method to electricity prices and test spot prices forecasts until one month ahead forecast. We conclude that the K-Factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria.

  • A K- Factor GIGARCH process : estimation and application to electricity marKet spot prices,
    2004
    Co-Authors: Dominique Guegan, Abdou Kâ Diongue, Bertrand Vignal
    Abstract:

    Some crucial time series of marKet data, such as electricity spot prices, exhibit long memory, in the sense of slowly-decaying correlations combined with heteroscedasticity. To e able to model such a behaviour, we consider the K-Factor GIGARCH process and we propose two methods to address the related parameter estimation problem. For each method, we develop the asymptotic theory for this estimation.

Andreas F. Molisch - One of the best experts on this subject based on the ideXlab platform.

  • Estimation of the K-Factor for Temporal Fading From Single-Snapshot Wideband Measurements
    IEEE Transactions on Vehicular Technology, 2019
    Co-Authors: Pan Tang, Jianhua Zhang, Andreas F. Molisch, Peter J. Smith, Mansoor Shafi, Lei Tian
    Abstract:

    The Ricean K-Factor is the ratio of the power of the deterministic multipath component (MPC) and the power of all other stochastic MPCs. The classical moment-based method has been successfully used to estimate the K-Factor from time series measurements. In this paper, we apply this method to estimate the K-Factor for narrowband temporal selectivity, based upon the analysis of frequency-selectivity in single-snapshot $\text {wideband}$ measurements. We also derive the theoretical bias of this $\text {estimator}$ and find it depends jointly on the $\text {number}$ of $\text {channel}$ transfer function envelope samples across the $\text {measurement}$ bandwidth, the correlation among such samples, and the K-Factor values. Qualitative analysis indicates that the bias increases nearly linearly with the K-Factor ( $K\geq 1$ on the linear scale) and is affected by correlation amongst the samples. Furthermore, the bias is inversely proportional to the number of samples. Simulations confirm the validity of the derivations. Moreover, a measurement campaign is designed at 28 GHz with a system bandwidth of 400 MHz in an urban micro-cell scenario. The proposed estimator is used to extract the statistics of the K-Factor in line of sight and non-line of sight scenarios. The relationship between the K-Factor and distance is investigated and a linear model is used to characterize it.

  • time and frequency varying K Factor of non stationary vehicular channels for safety relevant scenarios
    IEEE Transactions on Intelligent Transportation Systems, 2015
    Co-Authors: Laura Bernado, Andreas F. Molisch, Thomas Zemen, Fredrik Tufvesson, Christoph F Mecklenbrauker
    Abstract:

    Vehicular communication channels are characterized by a non-stationary time- and frequency-selective fading process due to fast changes in the environment. We characterize the distribution of the envelope of the first delay bin in vehicle-to-vehicle channels by means of its Rician $K$ -Factor. We analyze the time–frequency variability of this channel parameter using vehicular channel measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant scenarios in intelligent transportation systems (ITS) . This data enables a frequency-variability analysis from an IEEE 802.11p system point of view, which uses 10 MHz channels. We show that the small-scale fading of the envelope of the first delay bin is Rician distributed with a varying $K$ -Factor. The later delay bins are Rayleigh distributed. We demonstrate that the $K$ -Factor cannot be assumed to be constant in time and frequency. The causes of these variations are the frequency-varying antenna radiation patterns, as well as the time-varying number of active scatterers, and the effects of vegetation. We also present a simple but accurate bimodal Gaussian mixture model, which allows to capture the $K$ -Factor variability in time for safety-relevant ITS scenarios.

Dirk Elhaus - One of the best experts on this subject based on the ideXlab platform.

  • use and misuse of the K Factor equation in soil erosion modeling an alternative equation for determining usle nomograph soil erodibility values
    Catena, 2014
    Co-Authors: K Auerswald, Peter Fiener, W Martin, Dirk Elhaus
    Abstract:

    Abstract The K Factor of the Universal Soil Loss Equation is the most important measure of soil erodibility that was adopted in many erosion models. The K Factor can be estimated from simple soil properties by a nomograph. Later, the classical K Factor equation was published to assist the calculation of K. This equation, however, does not fully agree with the nomograph, which still has to be used in these deviating cases. Here we show for a large soil data set from Central Europe (approximately 20,000 soil analyses) that the equation fails in considerably more than 50% of all cases. The failure can be large and may amount to half of the K Factor. To facilitate the K Factor calculation, we developed a set of equations that fully emulates the nomograph and supersedes the cumbersome reading of the nomograph.

Chen-kun Hsu - One of the best experts on this subject based on the ideXlab platform.

  • Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed
    Sustainability, 2019
    Co-Authors: Bor-shiun Lin, Chun-kai Chen, Kent Thomas, Chen-kun Hsu
    Abstract:

    The estimation of soil erosion in Taiwan and many countries of the world is based on the widely used universal soil loss equation (USLE), which includes the Factor of soil erodibility (K-Factor). In Taiwan, K-Factor values are referenced from past research compiled in the Taiwan Soil and Water Conservation Manual, but there is limited data for the downstream area of the Shihmen reservoir watershed. The designated K-Factor from the manual cannot be directly applied to large-scale regional levels and also cannot distinguish and clarify the difference of soil erosion between small field plots or subdivisions. In view of the above, this study establishes additional values of K-Factor by utilizing the double rings infiltration test and measures of soil physical–chemical properties and increases the spatial resolution of K-Factor map for Shihmen reservoir watershed. Furthermore, the established values of K-Factors were validated with the designated value set at Fuxing Sanmin from the manual for verifying the correctness of estimates. It is found that the comparative results agree well with established estimates within an allowable error range. Thus, the K-Factors established by this study update the previous K-Factor system and can be spatially estimated for any area of interest within the Shihmen reservoir watershed and improving upon past limitations.