82 research outputs found

    Modelling Terrestrial Clear-Air Microwave Radio Fading

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    The technology of communication systems between population centres has undergone much change over the last century an a half, but radio links continue to be an important part of communication networks. A challenging part of their design is allowing for variations in received signal level, known as radio fading and enhancement, due to the atmosphere between transmitter and receiver. At high frequencies rain fading is the limiting factor, but below about 10 GHz, temperature and humidity gradients, in the absence of precipitation, may produce clear-air fading that becomes the limiting factor. As the refractive index of the air at radio frequencies depends on temperature and humidity, vertical gradients of these parameters cause bending of ray-paths. Multiple signals may arrive at the receiver over different paths, resulting in multipath fading. Sometimes almost no signal at all is able to find its way from transmitter to receiver, resulting in an impairment known as median depression; this may last for an hour or more, with median signal level up to 50 dB below normal. Recent long-term observations show this fading to be particularly severe in some parts of Australia, but not well predicted by pre-existing models. This thesis develops a new international model for clear-air fading. Weather forecasting has made significant progress in recent years due to numerical weather prediction (NWP) models, so radio propagation researchers have aimed to use these models to predict the state of the atmosphere, and Fourier split-step parabolic equation modelling (PEM) to predict radio propagation. Considering this, we begin this thesis by investigating Fourier split-step PEM, developing new techniques for dealing with finite conductivity lower boundaries, estimating the absorbing upper boundary height, and for dealing with irregular terrain, in both two and three dimensions. A brief description of the internationally adopted empirical model for diffraction over terrain (Rec. ITU-R P526-15, 2019), completes this chapter. We then examine radio refractivity gradient cumulative distributions derived from NWP data, comparing them with measurements from radiosondes, and data from sensors mounted on towers. We find the NWP prediction of anomalous gradients in the surface atmospheric layer to be poor, and develop a new parameter, surface refractivity anomaly, derived from surface weather station time-series data. We find this parameter useful in predicting vertical radio refractivity gradients in the atmospheric surface layer. Due to NWP surface gradient accuracy problems, we adopt the empirical regression model approach to fading severity prediction. This is not new, but we now have the benefit of more fading data from more regions of the world, and we have our new prediction parameters, generated from several years of data from thousands of worldwide weather stations. We make novel refinements to the modelling of clear-air fading, by first replacing ordinary least squares (OLS) regression with generalised least squares (GLS) regression, to take spatial correlation into account. We then employ the geostatistical technique of universal kriging, to further improve prediction accuracy. Our new fading model, as described in this thesis, is now the internationally approved terrestrial line-of-sight model for fading due to multipath and related mechanisms (Rec. ITU-R P.530-18, 2021).Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 202

    Radio wave propagation modeling under precipitation and clear-air at microwave and millimetric bands over wireless links in the horn of Africa.

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    Doctor of Philosophy in Electronic Engineering. University of KwaZulu-Natal, Durban 2017.Abstract available in PDF file

    Levantamiento del mapa de atenuaciones de señal electromagnética en las bandas de 2.4 GHz y 5 GHz para la red wireless PUCP

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    El trabajo desarrollado en la presente tesis consiste en el levantamiento de mapas de atenuación de la señal electromagnética en bandas 2.4 y 5GHz en pabellones de alta prioridad definidos por DIRINFO, que constituyen el 20% de los APs instalados en la red WiFiPUCP. La implementación de los mapas se realizará a través de métodos y algoritmos matemáticos de estimación de valores a partir de data real recolectada en los pabellones elegidos. En el primer capítulo se describe la situación actual de las redes inalámbricas, en especial los hotsposts y redes de área local inalámbricas. Además, se describe los fenómenos físicos que afectan la señal electromagnética, los tipos de desvanecimientos y algunos de los modelos predictivos más usados en trabajos similares. El segundo capítulo presenta una descripción más profunda de los tipos de desvanecimientos que merman a la señal electromagnética y su modelamiento a través de distribuciones de probabilidad. Además, se describe la teoría de estimadores probabilísticos que nos ayudarán a implementar nuestro método de geolocalización y estimar la potencia de la señal cuando se ve afectada por los tipos de desvanecimiento. El tercer capítulo está orientado a las técnicas de localización. Se explica la teoría de las distintas técnicas que se puede implementar y se realiza una comparativa con la que se utilizará en la presente tesis. Por último, se describe el algoritmo de localización a implementar en Matlab y que se utilizará a lo largo de las pruebas. En cuarto capítulo se detalla los pormenores de los instrumentos utilizados para la recolección de data y se puntualiza en cifras las recolecciones de mediciones del presente trabajo. Además, se determinan variables y parámetros que tendrán influencia tanto en la recolección como en los métodos de estimaciones de valores.Tesi

    Clear-air radioclimatological modeling for terrestrial line of sight links in Southern Africa.

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    Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2010.This thesis has investigated radioclimatological study in a clear-air environment as applicable to terrestrial line of sight link design problems. Radioclimatological phenomena are adequately reviewed both for the precipitation effect and clear-air effect. The research focuses more on the clear-air effect of radioclimatological studies. Two Southern African countries chosen for case study in the report are Botswana and South Africa. To this end, radiosonde data gathered in Maun, Botswana and Durban, South Africa are used for model formulation and verification. The data used in the thesis ranges from three years to ten years in these two stations. Three to ten years of refractivity data gathered in Botswana and South Africa is used for the model formulation. On the other hand, eight months signal level measurement data recorded from the terrestrial line of sight link set up between Howard College and Westville Campuses of the University of KwaZulu-Natal, Durban South Africa is used for model verification. Though various radioclimatic parameters could affect radio signal propagation in the clear-air environment, this report focuses on two of these parameters. These two parameters are the geoclimatic factor and effective earth radius factor (k-factor). The first parameter is useful for multipath fading determination while the second parameter is very important for diffraction fading, modeling and characterization. The two countries chosen have different terrain and topographical structures; thus further underlying the choice for these two parameters. While Maun in Botswana is a gentle flat terrain, Durban in South Africa is characterized by hilly and mountainous terrain structure, which thus affects radioclimatological modeling in the two countries. Two analytical models have been proposed to solve clear-air radioclimatic problems in Southern Africa in the thesis. The first model is the fourth order polynomial analytical expression while the second model is the parabolic equation. The fourth order polynomial model was proposed after an extensive analysis of the eight month signal level measurement data gathered in Durban, South Africa. This model is able to predict the fade exceedance probabilities as a function of fade depth level. The result from the fourth order polynomial model is found to be comparable with other established multipath propagation model reviewed in the thesis. Availability of more measurement data in more location will be necessary in future to further refine this model. The second model proposed to solve clear-air propagation problem in the thesis is the modified parabolic equation. We chose this technique because of its strength and its simplistic adaptation to terrestrial line of sight link design problem. This adaptation is possible because, the parabolic equation can be modified to incorporate clear-air parameters. Hence this modification of the parabolic equation allows the possibility of a hybrid technique that incorporates both the statistical and mathematical procedures perfectly into one single process. As a result of this, most of the very important phenomena in clear-air propagation such as duct occurrence probabilities, diffraction fading and multipath fading is captured by this technique. The standard parabolic equation (SPE) is the unmodified parabolic equation which only accounts for free space propagation, while the modified parabolic equation (MPE) is the modified version of the parabolic equation. The MPE is classified into two in the thesis: the first modified parabolic equation (MPE1) and second modified parabolic equation (MPE2). The MPE1 is designed to incorporate the geoclimatic factor which is intended to study the multipath fading effect in the location of study. On the other hand, MPE2 is the modified parabolic equation designed to incorporate the effective earth radius factor (k-factor) intended to study the diffraction fading in the location of study. The results and analysis of the results after these modifications confirm our expectation. This result shows that signal loss is due primarily to diffraction fading in Durban while in Botswana, signal loss is due primarily to multipath. This confirms our expectation since a flatter terrain attracts signal loss due to multipath while hilly terrain attracts signal loss due to diffraction fading

    Estimation of tropospheric wet delay from GNSS measurements

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    The determination of the zenith wet delay (ZWD) component can be a difficult task due to the dynamic nature of atmospheric water vapour. However, precise estimation of the ZWD is essential for high-precision Global Navigation Satellite System (GNSS) applications such as real-time positioning and Numerical Weather Prediction (NWP) modelling.The functional and stochastic models that can be used for the estimation of the tropospheric parameters from GNSS measurements are presented and discussed in this study. The focus is to determine the ZWD in an efficient manner in static mode. In GNSS, the estimation of the ZWD is directly impacted by the choice of stochastic model used in the estimation process. In this thesis, the rigorous Minimum Norm Quadratic Unbiased Estimation (MINQUE) method was investigated and compared with traditional models such as the equal-weighting model (EWM) and the elevationangle dependent model (EADM). A variation of the MINQUE method was also introduced. A simulation study of these models resulted in MINQUE outperforming the other stochastic models by at least 36% in resolving the height component. However, this superiority did not lead to better ZWD estimates. In fact, the EADM provided the most accurate set of ZWD estimates among all the models tested. The EADM also yielded the best ZWD estimates in the real data analyses for two independent baselines in Australia and in Europe, respectively.The study also assessed the validity of a baseline approach, with a reduced processing window size, to provide good ZWD estimates at Continuously Operating Reference Stations (CORS) in an efficient manner. Results show that if the a-priori station coordinates are accurately known, the baseline approach, along with a 2-hour processing window, can produce ZWD estimates that are statistically in good agreement with the estimates from external sources such as the radiosonde (RS), water vapour radiometer (WVR) and International GNSS Service (IGS) solutions. Resolving the ZWD from GNSS measurements in such a timely manner can aid NWP model in providing near real-time weather forecasts in the data assimilation process.In the real-time kinematic modelling of GNSS measurements, the first-order Gauss- Markov (GM) autocorrelation model is commonly used for the dynamic model in Kalman filtering. However, for the purpose of ZWD estimation, it was found that the GM model consistently underestimates the temporal correlations that exist among the ZWD measurements. Therefore, a new autocorrelation dynamic model is proposed in a form similar to that of a hyperbolic function. The proposed model initially requires a small number of autocorrelation estimates using the standard autocorrelation formulations. With these autocorrelation estimates, the least-squares method is then implemented to solve for the model’s parameter coefficients. Once solved, the model is then fully defined. The proposed model was shown to be able to follow the autocorrelation trend better than the GM model. Additionally, analysis of real data at an Australian IGS station has showed the proposed model performed better than the random-walk model, and just as well as the GM model. The proposed model was able to provide near real-time (i.e. 30 seconds interval) ZTD estimates to within 2 cm accuracy on average.The thesis also included an investigation into the several interpolation models for estimating missing ZWD observations that may take place during temporary breakdowns of GNSS stations, or malfunctions of RS and WVR equipments. Results indicated marginal differences between the polynomial regression models, linear interpolation, fast-Fourier transform and simple Kriging methods. However, the linear interpolation method, which is dependent on the two most recent data points, is preferable due to its simplicity. This result corresponded well with the autocorrelation analysis of the ZWD estimates where significant temporal correlations were observed for at most two hours.The study concluded with an evaluation of several trend and smoothing models to determine the best models for predicting ZWD estimates, which can help improve real-time kinematic (RTK) positioning by mitigating the tropospheric effect. The moving average (MA) and the single-exponential smoothing (SES) models were shown to be the best-performing prediction models overall. These two models were able to provide ZWD estimates with forecast errors of less 10% for up to 4 hours of prediction

    Machine-Learning-Based LOS Detection for 5G Signals with Applications in Airport Environments

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    The operational costs of the advanced Air Traffic Management (ATM) solutions are often prohibitive in low- and medium-sized airports. Therefore, new and complementary solutions are currently under research in order to take advantage of existing infrastructure and offer low-cost alternatives. The 5G signals are particularly attractive in an ATM context due to their promising potential in wireless positioning and sensing via Time-of-Arrival (ToA) and Angle-of-Arrival (AoA) algorithms. However, ToA and AoA methods are known to be highly sensitive to the presence of multipath and Non-Line-of-Sight (NLOS) scenarios. Yet, LOS detection in the context of 5G signals has been poorly addressed in the literature so far, to the best of the Authors’ knowledge. This paper focuses on LOS/NLOS detection methods for 5G signals by using both statistical/model-driven and data-driven/machine learning (ML) approaches and three challenging channel model classes widely used in 5G: namely Tapped Delay Line (TDL), Clustered Delay Line (CDL) and Winner II channel models. We show that, with simulated data, the ML-based detection can reach between 80% and 98% detection accuracy for TDL, CDL and Winner II channel models and that TDL is the most challenging in terms of LOS detection capabilities, as its richness of features is the lowest compared to CDL and Winner II channels. We also validate the findings through in-lab measurements with 5G signals and Yagi and 3D-vector antenna and show that measurement-based detection probabilities can reach 99–100% with a sufficient amount of training data and XGBoost or Random Forest classifiers.publishedVersionPeer reviewe

    Data Acquisition Applications

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    Data acquisition systems have numerous applications. This book has a total of 13 chapters and is divided into three sections: Industrial applications, Medical applications and Scientific experiments. The chapters are written by experts from around the world, while the targeted audience for this book includes professionals who are designers or researchers in the field of data acquisition systems. Faculty members and graduate students could also benefit from the book

    Research on Reliable Low-Power Wide-Area Communications Utilizing Multi-RAT LPWAN Technologies for IoT Applications

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    Předkládaná disertační práce je zaměřena na „Výzkum spolehlivé komunikace pro IoT aplikace v bezdrátových sítích využívajících technologie Multi-RAT LPWAN“. Navzdory značnému pokroku v oblasti vývoje LPWA technologií umožňující masivní komunikace mezi zařízeními (mMTC), nemusí tyto technologie výkonnostně dostačovat pro nově vznikající aplikace internetu věcí. Hlavním cílem této disertační práce je proto nalezení a vyhodnocení limitů současných LPWA technologií. Na základě těchto dat jsou nevrženy nové mechanismy umožňující snazší plánování a vyhodnocování síťového pokrytí. Navržené nástroje jsou vyladěny a validovány s využitím dat získaných z rozsáhlých měřících kampaních provedených v zákaznických LPWA sítích. Tato disertační práce dále obsahuje návrh LPWA zařízení vybavených více komunikačními rozhraními (multi-RAT) které mohou umožnit překonání výkonnostních limitů jednotlivých LPWA technologií. Současná implementace se zaměřuje zejména na snížení spotřeby zařízení s více rádiovými rozhraními, což je jejich největší nevýhodou. K tomuto účelu je využito algoritmů strojového učení, které jsou schopné dynamicky vybírat nejvhodnější rozhraní k přenosu.This doctoral thesis addresses the “Research on Reliable Low-Power Wide-Area Communications Utilizing Multi-RAT LPWAN Technologies for IoT Applications”. Despite the immense progress in massive Machine-Type Communication (mMTC) technology enablers such as Low-Power Wide-Area (LPWA) networks, their performance does not have to satisfy the requirements of novelty Internet of Things (IoT) applications. The main goal of this Ph.D. work is to explore and evaluate the limitations of current LPWA technologies and propose novel mechanisms facilitating coverage planning and assessment. Proposed frameworks are fine-tuned and cross-validated by the extensive measurement campaigns conducted in public LPWA networks. This doctoral thesis further introduces the novelty approach of multi-RAT LPWA devices to overcome the performance limitation of individual LPWA technologies. The current implementation primarily focuses on diminishing the greatest multi-RAT solutions disadvantage, i.e., increased power consumption by employing a machine learning approach to radio interface selection.
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