Massive multiple-input multiple-output (massive-MIMO) is a promising technology for next generation wireless communications systems due to its capability to increase the data rate and meet the enormous ongoing data traffic explosion. However, in non-reciprocal channels, such as those encountered in frequency division duplex (FDD) systems, channel state information (CSI) estimation using downlink (DL) training sequence is to date very challenging issue, especially when the channel exhibits a shorter coherence time. In particular, the availability of sufficiently accurate CSI at the base transceiver station (BTS) allows an efficient precoding design in the DL transmission to be achieved, and thus, reliable communication systems can be obtained. In order to achieve the aforementioned objectives, this paper presents a feasible DL training sequence design based on a partial CSI estimation approach for an FDD massive-MIMO system with a shorter coherence time. To this end, a threshold-based approach is proposed for a suitable DL pilot selection by exploring the statistical information of the channel covariance matrix. The mean square error of the proposed design is derived, and the achievable sum rate and bit-error-rate for maximum ratio transmitter and regularized zero forcing precoding is investigated over different BTS topologies with uniform linear array and uniform rectangular array. The results show that a feasible performance in the DL FDD massive-MIMO systems can be achieved even when a large number of antenna elements are deployed by the BTS and a shorter coherence time is considered.
Caffeine (1,3,7-trimethylxanthine), which is the most widely consumed stimulant in the world, had been isolated and estimated gravimetrically in fifteen different commercial kinds of tea found in the Iraqi market.The kinds of tea were chosen according to their differences in the degree of fermentation and the method of processing i.e. black , gray and green . The isolated caffeine was identified by melting point, sublimation, TLC, chemical tests, UV , IR , HPLC and CHNO analysis. &nb
... Show MoreIn this paper, ARIMA model was used for Estimating the missing data(air temperature, relative humidity, wind speed) for mean monthly variables in different time series at three stations (Sinjar, Baghdad , AL.Hai) which represented different parts of Iraq from north to south respectively
Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin
... Show MoreThe paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms
... Show MoreConcentration of natural occurring radioactive material (NORM) in Markazia Degasing Station in North Rumaila oilfield (NDS) was measured in this study. Then, radiological assessment due to existing of NORM in different samples including soil, sludge, scale, oil, and water collected from different stages of oil and gas production NDS was done. Radioactivity concentration of Ra-226, Th-232 and K-40 were measured using gamma spectrometry system based on HPGe detector with efficiency of 30%. The results show that some locations within NDS are contaminated with NORM. The activity in Bq/kg of Ra-226, Th-232 and K-40 range between 15.19 in oil to 68.73 in sludge, 8.5 in oil to 23.45 in sludge and 80.23 in oil to 319.73 in saline water samples r
... Show MoreBenzene is a hydrocarbon chemical consisting of six atoms arranged in a ring structure. At normal ambient temperatures; it is a liquid, which evaporates rapidly at room temperature and is highly flammable. It has a characteristic of aromatic odor and is slightly soluble in water (1.5 g/liter at 20ºC) but miscible with most other organic solvents [1]. Long-term inhalation of benzene causes blood disorders. It specifically affects bone marrow [2]. And it may cause anemia, excessive bleeding, damage to the immune system and DNA [3, 4]. Increased incidence of leukemia (cancer of the tissues that form white blood cells) has been observed in people occupationally exposed to
... Show MoreAbstract The purpose of this study, teach the art of performing Olympic lifts (snatch and, clean and jerk) using the two methods are instructional (self-learning associated with the model) and (reverse style of partial way). Identify the effectiveness of these methods in learning the art of performance and style of the best Olympic lifting in the learning and retention of novice for Olympic lifts. The research sample consisted of 16 lifters were selected purposively representing specialist center for the care of athletic talent to weightlifting for ages 14 years. The sample was divided into two experimental, Each group (8) eight weightlifters. The experimental group used the style of the first self-learning associated with the m
... Show MoreThe purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals
... Show More