Stochastic Approach to a Rain Attenuation Time Series Synthesizer for Heavy Rain Regions

2016 ◽  
Vol 6 (5) ◽  
pp. 2379
Author(s):  
Masoud Mohebbi Nia ◽  
Jafri Din ◽  
Hong Yin Lam ◽  
Athanasios D. Panagopoulos
Keyword(s):  
Time Series ◽  
Communication Systems ◽  
Measurement Data ◽  
Likelihood Estimation ◽  
Heavy Rain ◽  
Stochastic Approach ◽  
Rain Attenuation ◽  
Least Square ◽  
Model Parameters ◽  
Cir Model

<p>In this work, a new rain attenuation time series synthesizer based on the stochastic approach is presented. The model combines a well-known interest-rate prediction model in finance namely the Cox-Ingersoll-Ross (CIR) model, and a stochastic differential equation approach to generate a long-term gamma distributed rain attenuation time series, particularly appropriate for heavy rain regions. The model parameters were derived from maximum-likelihood estimation (MLE) and Ordinary Least Square (OLS) methods. The predicted statistics from the CIR model with the OLS method are in good agreement with the measurement data collected in equatorial Malaysia while the MLE method overestimated the result. The proposed stochastic model could provide radio engineers an alternative solution for the design of propagation impairment mitigation techniques (PIMTs) to improve the Quality of Service (QoS) of wireless communication systems such as 5G propagation channel, in particular in heavy rain regions.</p>

scholarly journals Stochastic Approach to a Rain Attenuation Time Series Synthesizer for Heavy Rain Regions

2016 ◽  
Vol 6 (5) ◽  
pp. 2379
Author(s):  
Masoud Mohebbi Nia ◽  
Jafri Din ◽  
Hong Yin Lam ◽  
Athanasios D. Panagopoulos
Keyword(s):  
Time Series ◽  
Communication Systems ◽  
Measurement Data ◽  
Likelihood Estimation ◽  
Heavy Rain ◽  
Stochastic Approach ◽  
Rain Attenuation ◽  
Least Square ◽  
Model Parameters ◽  
Cir Model

<p>In this work, a new rain attenuation time series synthesizer based on the stochastic approach is presented. The model combines a well-known interest-rate prediction model in finance namely the Cox-Ingersoll-Ross (CIR) model, and a stochastic differential equation approach to generate a long-term gamma distributed rain attenuation time series, particularly appropriate for heavy rain regions. The model parameters were derived from maximum-likelihood estimation (MLE) and Ordinary Least Square (OLS) methods. The predicted statistics from the CIR model with the OLS method are in good agreement with the measurement data collected in equatorial Malaysia while the MLE method overestimated the result. The proposed stochastic model could provide radio engineers an alternative solution for the design of propagation impairment mitigation techniques (PIMTs) to improve the Quality of Service (QoS) of wireless communication systems such as 5G propagation channel, in particular in heavy rain regions.</p>

Pricing S&P500 barrier put option of American type under Heston–CIR model with regime-switching

2019 ◽  
Vol 06 (02) ◽  
pp. 1950014 ◽  
Author(s):  
Farshid Mehrdoust ◽  
Idin Noorani
Keyword(s):  
Expectation Maximization ◽  
Regime Switching ◽  
Likelihood Estimation ◽  
Least Square ◽  
Hidden Markov Chain ◽  
Cir Model ◽  
Binomial Tree ◽  
Put Option ◽  
Rate Processes ◽  
Tree Method

In this paper, we consider the regime-switching Heston–CIR model, where the parameters of the volatility process are modulated by a Hidden Markov chain and the unobserved regimes. Then, we calibrate the parameters of the volatility and interest rate processes by the expectation maximization (EM) and maximum likelihood estimation (MLE) algorithms, respectively. Next, we use the least square Monte-Carlo (LSM) algorithm to determine the S&P500 American barrier put option under the Heston–CIR model. Finally, by the binomial tree method as a benchmark, we provide some numerical experiments to illustrate the accuracy of the achieved results.

Experiments Using Lattice Filters to Identify the Dynamics of a Flexible Beam

1985 ◽  
Vol 107 (3) ◽  
pp. 187-191 ◽  
Author(s):  
N. Sundararajan ◽  
R. C. Montgomery
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Measurement Data ◽  
Equation Model ◽  
Least Square ◽  
Mode Shapes ◽  
Flexible Beam ◽  
Large Space ◽  
Lattice Filters ◽  
Natural Modes

An approach for identifying the dynamics of large space structures is applied to a free-free beam. In this approach the system’s order is determined on-line, along with mode shapes, using recursive lattice filters which provide a linear least square estimate of the measurement data. The mode shapes determined are orthonormal in the space of the measurements and, hence, are not the natural modes of the structure. To determine the natural modes of the structure, a method based on the fast Fourier transform is used on the outputs of the lattice filter. These natural modes are used to obtain the modal amplitude time series from the measurements. The modal time series provides the input data for an output error parameter identification scheme that identifies the autoregressive moving average (ARMA) parameters of the difference equation model of the modes. Results using this approach for experimentally identifying the dynamics of a flexible beam hardware are presented.

Satellite and Terrestrial Links Rain Attenuation Time Series Generator for Heavy Rain Climatic Regions

2013 ◽  
Vol 61 (6) ◽  
pp. 3396-3399 ◽  
Author(s):  
Sotiris A. Kanellopoulos ◽  
Athanasios D. Panagopoulos ◽  
Charilaos I. Kourogiorgas ◽  
John D. Kanellopoulos
Keyword(s):  
Time Series ◽  
Heavy Rain ◽  
Rain Attenuation ◽  
Climatic Regions

scholarly journals HALF LOGISTIC MODIFIED EXPONENTIAL DISTRIBUTION: PROPERTIES AND APPLICATIONS

2020 ◽  
pp. 276-287
Author(s):  
Arun Kumar Chaudhary ◽  
Vijay Kumar
Keyword(s):  
Exponential Distribution ◽  
Likelihood Estimation ◽  
Real Data ◽  
Least Square ◽  
Estimation Methods ◽  
Logistic Distribution ◽  
Model Parameters ◽  
Type I ◽  
Data Set ◽  
Von Mises

In this study, we have introduced a three-parameter probabilistic model established from type I half logistic-Generating family called half logistic modified exponential distribution. The mathematical and statistical properties of this distribution are also explored. The behavior of probability density, hazard rate, and quantile functions are investigated. The model parameters are estimated using the three well known estimation methods namely maximum likelihood estimation (MLE), least-square estimation (LSE) and Cramer-Von-Mises estimation (CVME) methods. Further, we have taken a real data set and verified that the presented model is quite useful and more flexible for dealing with a real data set. KEYWORDS— Half-logistic distribution, Estimation, CVME ,LSE, , MLE

Performance Investigation of FSO–OFDM Communication Systems under the Heavy Rain Weather

2017 ◽  
Vol 39 (1) ◽  
Author(s):  
Florence Rashidi ◽  
Jing He ◽  
Lin Chen
Keyword(s):  
Communication System ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Attenuation Factor ◽  
Heavy Rain ◽  
Rain Attenuation ◽  
Optical Signal ◽  
Atmospheric Conditions ◽  
Free Space Optical ◽  
Rainfall Condition

AbstractThe challenge in the free-space optical (FSO) communication is the propagation of optical signal through different atmospheric conditions such as rain, snow and fog. In this paper, an orthogonal frequency-division multiplexing technique (OFDM) is proposed in the FSO communication system. Meanwhile, considering the rain attenuation models based on Marshal & Palmer and Carbonneau models, the performance of FSO communication system based on the OFDM is evaluated under the heavy-rain condition in Changsha, China. The simulation results show that, under a heavy-rainfall condition of 106.18 mm/h, with an attenuation factor of 7 dB/km based on the Marshal & Palmer model, the bit rate of 2.5 and 4.0 Gbps data can be transmitted over the FSO channels of 1.6 and 1.3 km, respectively, and the bit error rate of less than 1E − 4 can be achieved. In addition, the effect on rain attenuation over the FSO communication system based on the Marshal & Palmer model is less than that of the Carbonneau model.

scholarly journals Analysis of Fade Dynamic at Ku-Band in Malaysia

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Siat Ling Jong ◽  
Michele D’Amico ◽  
Jafri Din ◽  
Hong Yin Lam
Keyword(s):  
Communication Systems ◽  
Satellite Communication ◽  
Heavy Rain ◽  
Rain Attenuation ◽  
Time Interval ◽  
Radio Communication ◽  
System Operator ◽  
Typical Day ◽  
One Year ◽  
Ku Band

This work investigates fade dynamics of satellite communication systems in equatorial heavy rain region based on a one year of Ku-band propagation measurement campaign carried out in Universiti Teknologi Malaysia (UTM), Johor, Malaysia. First order statistics of rain attenuation are deduced and the results are found to be in good agreement with those obtained from other beacon measurements gathered within the same area (Kuala Lumpur). Moreover, the fade duration and slope statistics of the satellite signal variations are also carefully derived and subsequently compared with the ITU-R recommendation model. Such information is useful for the system operator and radio communication engineer for the design of appropriate fade mitigation techniques as well as the quality of service that could be offered to the user (according to the time interval for a typical day). Further evaluation on the performances of several ITU-R models in the heavy rain region are needed based on the measurement database available of this climatic region.

scholarly journals Parallel kd-Tree Based Approach for Computing the Prediction Horizon Using Wolf’s Method

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
J. J. Águila ◽  
E. Arias ◽  
M. M. Artigao ◽  
J. J. Miralles
Keyword(s):  
Dynamical System ◽  
Time Series ◽  
Lyapunov Exponent ◽  
Measurement Data ◽  
Original System ◽  
Model Parameters ◽  
Maximal Lyapunov Exponent ◽  
Science And Engineering ◽  
Prediction Horizon ◽  
Means Of Measurement

In different fields of science and engineering, a model of a given underlying dynamical system can be obtained by means of measurement data records called time series. This model becomes very important to understand the original system behaviour and to predict the future values of that system. From the model, parameters such as the prediction horizon can be computed to obtain the point where the prediction becomes useless. In this work, a new parallel kd-tree based approach for computing the prediction horizon is presented. The parallel approach uses the maximal Lyapunov exponent, which is computed by Wolf’s method, as an estimator of the prediction horizon.

scholarly journals Investigation into Rain Attenuation Prediction Models at Locations in Lagos Using Remote Sensing

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Olufunke G. Darley ◽  
Abayomi I. Yussuff ◽  
Adetokunbo A. Adenowo
Keyword(s):  
Remote Sensing ◽  
Communication Systems ◽  
Prediction Models ◽  
Measurement Data ◽  
Rain Attenuation ◽  
Physical Contact ◽  
Annual Rainfall ◽  
Integration Time ◽  
Tropical Region ◽  
Victoria Island

This paper investigated the performances of some rain attenuation prediction models at some GSM network locations in Lagos, Nigeria, using remote sensing at Ku band. Remote sensing is a collection and interpretation of information about an object without physical contact with the object being measured. Three popular terrestrial prediction models were considered in this work. These are ITU-R P.530-17, Lin and Silva Mello Models. Ten years (2010-2019) annual rainfall data with hourly integration time were sourced from the Nigerian Meteorological Agency (NIMET) and link budgets for three microwave links (Tarzan Yard, Kofo Abayomi and GLO Shop) in Victoria Island at 18 GHz were obtained from Global Communications Limited (GLO), Nigeria. Data analysis and comparison of the microwave links rainfall estimates were carried out to identify the most suitable of the three models at the selected locations of interest. Measurement data obtained from both NIMET and GLO were used to validate the predicted attenuation data from the three selected models. The ITU-R P.530-17 prediction model overestimated the measurement at Tarzan Yard; closely followed by Silva Mello, while Lin underestimated the measured data.  Again, at Kofo Abayomi station, the ITU-R model overestimated the measurement, while both Silva Mello and Lin models underestimated the measurement. At the GLO Shop, the Silva Mello overestimated the measured value, while ITU-R and Lin underestimated the measurement. At 0.01% of time exceeded, NIMET measurement was higher (at 48.2 dB) than that of Tarzan Yard, Kofo Abayomi and GLO shop (43.1, 46.3 and 37.0 dB respectively). These results will provide useful information in mitigating signal outages due to rain for mobile communication systems. Keywords- Path attenuation, Prediction models, Rainfall rate, Terrestrial microwave links, Tropical region

scholarly journals Bayesian Robust Multivariate Time Series Analysis in Nonlinear Models with Autoregressive and t-Distributed Errors

2021 ◽  
Vol 5 (1) ◽  
pp. 20
Author(s):  
Alexander Dorndorf ◽  
Boris Kargoll ◽  
Jens-André Paffenholz ◽  
Hamza Alkhatib
Keyword(s):  
Time Series ◽  
Nonlinear Models ◽  
Multivariate Time Series ◽  
Functional Model ◽  
Measurement Data ◽  
Model Parameters ◽  
Correlated Noise ◽  
Posterior Density ◽  
Stochastic Properties ◽  
T Distribution

Many geodetic measurement data can be modelled as a multivariate time series consisting of a deterministic (“functional”) model describing the trend, and a stochastic model of the correlated noise. These data are also often affected by outliers and their stochastic properties can vary significantly. The functional model of the time series is usually nonlinear regarding the trend parameters. To deal with these characteristics, a time series model, which can generally be explained as the additive combination of a multivariate, nonlinear regression model with multiple univariate, covariance-stationary autoregressive (AR) processes the white noise components of which obey independent, scaled t-distributions, was proposed by the authors in previous research papers. In this paper, we extend the aforementioned model to include prior knowledge regarding various model parameters, the information about which is often available in practical situations. We develop an algorithm based on Bayesian inference that provides a robust and reliable estimation of the functional parameters, the coefficients of the AR process and the parameters of the underlying t-distribution. We approximate the resulting posterior density using Markov chain Monte Carlo (MCMC) techniques consisting of a Metropolis-within-Gibbs algorithm.

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