Characterization and Modeling of Large-Scale Fading for Radio Propagation in Rwanda

Radio links are extensively used for voice and data communications at long distance. We analyze the radio propagation parameters that affect the received signal level on radio links in Rwanda and we determine the best path loss model for prediction of the received signal level. Various models of propagation and the mathematical expressions of path loss are described here in order to come to the prediction of those propagation effects. By analyzing data collected for two links of MTN Rwanda: Gahengeri-Kibungo and Gahengeri-Jali, we find that the best predicting model is the normal distribution.

An Improved Characterization of Small Scale Fading Based on 2D Measurements and Modeling of a Moving Receiver in an Indoor Environment

Accurately characterizing the wireless small-scale fading channel has been a challenging task in the wireless communication era due to the surrounding environment. Therefore, this paper introduces a new technique to experimentally characterize the small-scale fading taking under consideration real environmental conditions. By conducting a two dimensional measurement while the mobile receiver is moving;a more accurate channel will be achieved. Two-dimensional measurement refers to collecting data from the receiver along the x and y direction. The two-dimensional measurement data contain far more information than a one-dimensional data collected. In order to represent the small-scale channel along with the real environmental conditions, new approaches are necessary to configure the two-dimensional system and to analyze the 2D data. The new approach this paper introduces for the characterization is that the measurements are conducted on a receiver while it is moving in a two dimensional manner, under different scenarios, Line-of-sight, Non-line-of-sight, and Two-wave-Diffuse Power. The experiment was conducted in a 7 meters long by 4 meters wide room, wherein the distance between the transmitter antenna and receiver is about 3 meters. Those scenarios represent different real-time conditions where obstacles differ from one scenario to another. For example, the line of sight scenario assumes there a clear line of sight between transmitter and receiver, Non line of sight assumes many obstacles between the transmitter and receiver, i.e. walls, cabinets, etc. and Two Wave Diffuse Power assumes a metallic reflector surrounding the receiver. The experiment showed more accurate results when compared to the one dimensional measurement that has been done in the past where receiver is moving in one direction and also receiver being fixed where a constructive and destructive interference is not captured. The two dimensional measurement technique, i.e. capturing data while receiver moving in both x and y directions, provided essential information regarding the constructive and destructive interference patterns caused by the interaction between the receiver while moving and the obstacles surrounding the receiver.

Fading Channels Parametric Data Simulation Supported by Real Data from Outdoor Experiments

Optimizing the estimates of received power signals is important as it can improve the process of transferring an active call from one base station in a cellular network to another base station without any interruptions to the call. The lack of effective techniques for estimation of shadow power in fading mobile wireless communication channels motivated the use of Kalman Filtering (KF) as an effective alternative. In our research, linear second-order state space Kalman Filtering was further investigated and tested for applicability. We first created simulation models for two KF-based estimators designed to estimate local mean (shadow) power in mobile communications corrupted by multipath noise. Simulations were used extensively in the initial stage of this research to validate the proposed method. The next challenge was to determine if the models would work with real data. Therefore, in [1] we presented a new technique to experimentally characterize the wireless small-scale fading channel taking into consideration real environmental conditions. The two-dimensional measurement technique enabled us to perform indoor experiments and collect real data. Measurements from these experiments were then used to validate simulation models for both estimators. Based on the indoor experiments, we presented new results in [2], where we concluded that the second-order KF-based estimator is more accurate in predicting local shadow power profiles than the first-order KF-based estimator, even in channels with imposed non-Gaussian measurement noise. In the present paper, we extend experiments to the outdoor environment to include higher speeds, larger distances, and distant large objects, such as tall buildings. Comparison was performed to see if the system is able to operate without a failure under a variety of conditions, which demonstrates model robustness and further investigates the effectiveness of this method in optimization of the received signals. Outdoor experimental results are provided. Findings demonstrate that the second-order Kalman filter outperforms the first-order Kalman filter.

Identification of peanut oil origins based on Raman spectroscopy combined with multivariate data analysis methods

This study aimed to use Raman spectroscopy to identify the producing areas of peanut oil and build a robust discriminant model to further screen out the characteristic spectra closely related to the origin. Raman spectra of 159 peanut oil samples from different provinces and different cities of the same province were collected. The obtained data were analyzed by stepwise linear discriminant analysis(SLDA), k-nearest neighbor analysis(k-NN),support vector machine(SVM) and multi-way analysis of variance. The results showed that the overall recognition rate of samples based on full spectra was higher than 90%. The producing origin, variety and their interaction influenced Raman spectra of peanut oil significantly, and 1 400–1 500 cmand 1 600– 1 700 cmwere selected as the characteristic spectra of origin and less affected by variety. The best classification model established by SLDA combined with characteristic spectra could rapidly and accurately identify peanut oil’s origin.

Channel Modeling for Air-to-Ground Wireless Communication

In this paper, we discuss several large-scale fading models for different enviromnents. The COST231-Hata model is adapted for air-to-ground modeling. We propose two criteria for air-to- ground channel modelling based on test data derived from field testing in Beijing. We develop a new propagation model that is more suitable for air-to-ground communication that previous models. We focus on improving this propagation model using the field test data.

VENTILATION FLOW IN A BAROCLINIC VORTEX RELATED TO TROPICAL CYCLONE MOTION

The tropical cyclone motion is numerically simulated with a quasi-geostrophic baroclinic model. The flow field of a tropical cyclone is decomposed into its axisymmetric and asymmetric components. The relation between the ventilation flow vector and the motion vector of the tropical cyclone is inves- tigated.The results of numerical experiments indicate:(1) There are both large-scale beta gyres and small-scale gyres in the asyrnmetric flow field.(2) The interaction between small-scale gyres and large-scale beta gyres leads to the oscillation of translation speed and translation direction for the tropi- cal cyclone.(3) There are the large deviations between the ventilation flow vector calculated by means of Fiorino and Elsberry's method and the motion vector of tropical cyclone.(4) The ventila- tion flow vector computed using the improved method closely correlates with the motion vector of the tropical cyclone.

User grouping and optimization-based pilot scheduling for mitigating the pilot contamination in massive multi cell MIMO systems

Massive multiple-input multiple-output(massive MIMO)is a promising approach in wireless communication systems for providing improved link reliability and spectral effi-ciency and it helps several users.The main aim is to solve pilot contamination issue in massive MIMO systems;this research paper utilizes two approaches for reducing the contamination.This paper presents the user grouping approach based on sparse fuzzy C-means clustering(sparse FCM),which groups user parameters based on parameters such as large-scale fading factor,SINR,and user distance.Here,same pilot sequences are assigned to center users in which the impact of pilot contamination is limited,while the algorithm assigns orthogonal pilot sequences to the edge users that suffer severely from pilot contamination.Therefore,the proposed user grouping keeps away from the inappropriate grouping of users,enabling effective grouping even under the worst situations of the channel.Secondly,pilot scheduling is done based on elephant spider monkey optimization(ESMO),which is designed by integrating elephant herding optimization(EHO)into spider monkey optimization(SMO).The performance of pilot scheduling based on grouping-based ESMO is evaluated based on achievable rate and SINR.The proposed method achieves maximal achievable rate of 41.29 bps/Hz and maximal SINR of 124.31 dB.