Standard Propagation Model Research Articles

Cascades of Parametric Instabilities in the Tokamak Plasma Edge during Electron Cyclotron Resonance Heating.

We report observations of nonlinear two-plasmon decay instabilities (TPDIs) of a high-power microwave beam, a process similar to half-harmonic generation in optics, during electron cyclotron resonance heating in a tokamak. TPDIs are found to occur regularly in the plasma edge due to wave trapping in density fluctuations for various confinement modes, and the frequencies of both observed daughter waves agree with modeling. Emissions from a cascade of subsequent decays, which indicate a generation of ion Bernstein waves, are correlated with fast-ion generation. This emphasizes the limitations of standard linear microwave propagation models and possibly paves the way for novel microwave applications in plasmas.

Comparative Analysis for Radio Channel Propagation Models in the City of Tripoli/ Libya for 4G/LTE Networks

The accurate prediction of radio channel propagation is one of the most important factors for the design and optimization of wireless communication systems. Path loss is the key element to design appropriate propagation model. There are different propagation models used to predict path loss, however, inaccurate propagation model may result in unsatisfactory services for the Global System for Mobile communications (GSM) network. These include low data rates, high co-channel interference, and wasted received power.This paper presents a comparative analysis of radio channel propagation models specifically for urban environment. The aim is to evaluate and compare the performance of various models and accurately predict path loss and signal behavior in urban settings. The research focuses on adapting the Okumura-Hata model which is used by the Libya Telecom and Technology Company (LTT) for Long Term Evolution (LTE) technology in the GSM Band. This study is based on real time measurements collected in downtown Tripoli in Libya. It suggests considering other models, such as Standard Propagation Model (SPM), Cost-231, and Ericsson. Using MATLAB for simulation, the findings indicate that SPM model is the best fit especially when distance is over ~22 Km.

A risk-based approach to precinct planning

As urban populations increase more people being affected by noise. Industrial precincts are a key component of the economy, but they are placed near existing resident. Planning is an important tool to manage the noise exposure from these areas. This paper describes the method we developed to help with deciding where noisy activities, and the amount of mitigation required, to reduce the risk of annoyance from the precinct. Our method is based on modeling the impact from a grid of sources overlayed on the precinct boundary. The transfer functions for each source to all sensitive receiver is calculated using a standard noise propagation model. An optimization algorithm is used to fairly calculate the maximum sound power for each grid point without exceeding the noise management levels. Risk maps are created using contours based on standard industrial sound power densities to define 3 zones: the central, intermediate, and interface. The risk maps are a useful tool for planners for deciding where to locate projects and the amount of mitigation required.

Using spectral characterization to identify healthcare-associated infection (HAI) patients for clinical contact precaution

Healthcare-associated infections (HAIs) are a major problem in hospital infection control. Although HAIs can be suppressed using contact precautions, such precautions are expensive, and we can only apply them to a small fraction of patients (i.e., a limited budget). In this work, we focus on two clinical problems arising from the limited budget: (a) choosing the best patients to be placed under precaution given a limited budget to minimize the spread (the isolation problem), and (b) choosing the best patients to release when limited budget requires some of the patients to be cleared from precaution (the clearance problem). A critical challenge in addressing them is that HAIs have multiple transmission pathways such that locations can also accumulate ‘load’ and spread the disease. One of the most common practices when placing patients under contact precautions is the regular clearance of pathogen loads. However, standard propagation models like independent cascade (IC)/susceptible-infectious-susceptible (SIS) cannot capture such mechanisms directly. Hence to account for this challenge, using non-linear system theory, we develop a novel spectral characterization of a recently proposed pathogen load based model, 2-Mode-SIS model, on people/location networks to capture spread dynamics of HAIs. We formulate the two clinical problems using this spectral characterization and develop effective and efficient algorithms for them. Our experiments show that our methods outperform several natural structural and clinical approaches on real-world hospital testbeds and pick meaningful solutions.

An Artificial Intelligence Radio Propagation Model Based on Geographical Information

In this article, a new wireless network planning propagation model is proposed, the datasets are continuous wave data from different cities, and the model is constructed based on the artificial neural network in machine learning and uses geographical information to do feature engineering. Six geographical information features are used as the input feature vectors of the model. The terrain type near the receiving point is a corrective factor affecting the propagation field intensity. To make the physical implications more explicit, two multilayer perceptron networks were designed, and the one-hot encoding of terrain types of five grids near the receiving point on the line between the receiving and transmitting points is taken as one of the input features of the second network. Through the model training and validation, and tested in actual scene, the model on the datasets of different cities has achieved good results. Compared with the traditional propagation model, such as the standard propagation model (SPM), this artificial intelligence model is more suitable for correction of data containing random disturbance and has a better simulation accuracy; the random disturbance includes external random interference sources, global positioning system offsets, and inaccurate maps.

Capacity Dimensioning of HSDPA Urban Network

To launch a cellular network, prelaunch capacity dimensioning is performed which includes coverage estimation and throughput prediction. Cellular companies in developing countries like Pakistan are only providing 2G services, while 3G services are yet to be launched. Although a lot of research has been done on 3G services in developed countries but there is very little knowledge regarding practical aspects of planning and optimization of 3G networks in third world countries like Pakistan. This research paper includes a thorough analysis of factors that affect capacity of 3G networks, including radio propagation models. Various propagation models are studied and propagation constants of Standard Propagation Model are tuned according to topography of Islamabad. The performance analysis of these propagation models is done using Matlab and results are verified through planning tool Atoll and field measurements. Based on analysis of these results capacity dimensioning, in terms of number of sites, is carried out for an urban network of Islamabad.

Correction of Radio Wave Propagation Prediction Model Based on Improved Seagull Algorithm in Tunnel Environment

In the urban rail transit (URT) environment, the radio wave propagation prediction model and communication system planning are very important. However, due to the complexity of the tunnel propagation environment, the current prediction model can not fully cover the radio wave propagation process in the tunnel. In this paper, the propagation mechanism area is divided based on the segmentation approach. Different propagation models are used for different propagation mechanism areas to predict path loss more quickly and accurately. To improve the accuracy of the prediction model, this paper proposes an improved seagull optimization algorithm (ISOA). First, to address the shortcomings of the seagull optimization algorithm (SOA) such as easy premature convergence and slow convergence speeds, two improved methods of random search and periodic disturbance are proposed. Then, in order to verify the effectiveness and feasibility of the improved algorithm, the benchmark function is used to test the optimization performance of the ISOA and gray wolf optimization, the SOA, and particle swarm optimization. The results show that the optimization performance of ISOA is the most significant. Finally, the ISOA is used to fit and correct the continuous wave test data for a rectangular tunnel and an arch tunnel. The results show that the corrected propagation model has a higher degree of fit with the measured data than the single standard propagation model (SPM) model. The modified propagation model thus has guiding significance for the deployment of time-division long-term (TD-LTE) evolution networks in the tunnel environment.

Calibrating the Standard Propagation Model (SPM) for Suburban Environments Using 4G LTE Field Measurement Study Case in Indonesia

Effective and efficient network planning for appropriate area coverage can use the standard propagation model (SPM) calibrating or tuning method. SPM functions to increase the accuracy of predictions from the appropriate coverage planning results in field implementation. This study uses a calibration method resulting from field measurements with 4G LTE 1800 MHz technology. The results of the simulation found that the percentage of SPM propagation model for Reference Signal Received Power (RSRP) is above -80 dBm, the difference is 4.30%, RSRP values range from -80 dBm to -95 dBm at 21.52%, -95 to -110 dBm at 25.45%, and RSRP is smaller than -110 dBm at 0.37% by measuring field signal distribution. Coverage prediction of Calibrated SPM propagation model has the higher accuracy than COST 231, it represents the accuracy of SPM about 87% and COST 231 about 79%. The improvement of RSRP in the simulation is caused by changes in the correction values K1 to K7 which are in accordance with the criteria of the Purwokerto City area after calibrating.

Information Content of Ship Noise on a Drifting Volumetric Array for Passive Environmental Sensing

This study investigates the information content of ship noise received on a drifting volumetric array of hydrophones in shallow water marine environments for the purposes of conducting acoustic thermometry or other environmental inversions. Passive inversions for physical oceanographic parameters are conducted using travel time differences, determined by cross-correlating ship noise received on hydrophones suspended beneath drifting buoys. Ships are tracked using the automatic identification system. Information content gained from the inversion is assessed using traditional a posteriori error analysis. Numerical simulations using a standard normal mode propagation model are used to test limitations of the proposed approach with respect to frequency band, drifting receiver configuration, precision, and accuracy of the inversion results, along with sensitivity to environmental and position mismatch. Performance predictions using this model are compared with results from a field experiment using at-sea data collected off the coast of New London, CT, USA, in Long Island Sound. Information gathered using passive acoustic inversion methods on drifting arrays can be used to constrain general circulation models, in coastal environments, where ship noise is ubiquitous, environmental data is sparse, and the oceanography is dynamic and important for understanding large-scale ocean processes.

Measurements and path loss models for a TD-LTE network at 3.7 GHz in rural areas

This paper presents an extensive path loss measurement campaign carried out in rural areas at 3.7 GHz, including line-of-sight (LOS) and non-LOS conditions. For this purpose, a commercially established fixed wireless access (FWA) network is exploited, operating with time-division long term evolution configuration. Furthermore, various models are examined and validated regarding their ability to predict accurately the path loss. The results reveal that the standard propagation model (SPM) achieves the best performance, thus being an attractive option for planning rural FWA links. The WINNER II and 3GPP/ITU-R models exhibit very good performance, as well. From the statistical assessment, the shadow fading follows the Lognormal distribution with a standard between 4.6 and 5.4 dB. An almost excellent fit is obtained regardless of the diverse propagation conditions in the specific area. Finally, from the model evaluation was concluded that SPM is highly recommended as the best option for a precise network dimensioning and planning.

Comparison of Artificial Intelligence and Semi-Empirical Methodologies for Estimation of Coverage in Mobile Networks

To help telecommunication operators in their network planning, namely coverage estimation and optimisation tasks, this article presents a comparison between a semi-empirical propagation model and a propagation model generated using Artificial Intelligence (AI). These two types of propagation models are quite different in their design. The semi-empiric Automatically Calibrated Standard Propagation Model (ACSPM) is specific for an operating antenna, being calibrated every time a use case application is used and the Artificial Intelligence Propagation Model (AIPM) can be applied in different scenarios, once trained, allowing to estimate coverage for a new antenna location, using information from neighboring antennas. These models have quite different features and applicability. The ACSPM should be applied in network optimisation, when using data from the current state of the antennas. The AIPM can be used in the deployment of new antennas, as it uses data from a certain geographical area. For a better comparison of the models studied, extensive Drive Tests (DT) collection campaigns conducted by operators are used, since coverage estimations are more realistic when DTs are considered. Both models are generated using very different methodologies, but their resulting performance is very similar. The AIPM achieves a Mean Absolute Error (MAE) up to 6.1 dB with a standard deviation of 4 dB. When compared to the ACSPM we have an improvement of 0.5 dB, since this only achieves a MAE up to 6.6 dB. AIPM achieves better results and is the characterised for being completely agnostic and definition-free, when compared with known propagation models.

Landslide Material Control on Tsunami Genesis—The Storegga Slide and Tsunami (8,100 Years BP)

AbstractTsunami generation from subaqueous landslides is controlled by landslide kinematics, which in turn is governed by the material properties of the slide mass. Yet the effect of the material properties on tsunami genesis is poorly understood. Geomorphological observations of landslide runout put constraints on the landslide dynamics. In addition, observations of tsunami runup heights can improve our understanding of how the landslide material transforms from initiation to final runout. The giant prehistoric Storegga Slide off the mid‐Norwegian coast caused a well‐documented ocean‐wide tsunami that offers a unique setting for coupling landslide material models to tsunami generation models. In this study we simulate the dynamics of the Storegga Slide and tsunami using the depth‐averaged landslide model BingClaw, which implements visco‐plastic rheology and remolding, and couple it to a standard tsunami propagation model. A broad sensitivity study varying the landslide material strength parameters in BingClaw shows that the initial soil yield strength and remolding rate are most important for the tsunami genesis but that the residual strength determined the final runout distance. BingClaw parameters were further optimized to obtain the observed runout distance and to minimize the relative error of the tsunami runup heights. As detailed time‐dependent three‐dimensional representations of landslide parameters cannot be determined through a field investigation of the landslide itself, these simulations of the Storegga Slide and tsunami can help in the selection of plausible parameter ranges for prognostic modeling in quantitative hazard assessments.

ANALISIS KUALITAS SINYAL PADA JARINGAN LONG TERM EVOLUTION (LTE) MENGGUNAKAN DATA DRIVE TEST CLUSTER

The rapid development of cellular communication technology is inseparable from various problems especially on signal quality. In outdoor areas, the performance of eNodeB that is not optimal may cause communication failure. This research was conducted with the drive test on the L_BUNDARANRENON_PL, L_AKABA_PL and L_MYAMIN_CR to obtain signal quality parameters at Renon cluster area. The results of the drive test obtained compared to the simulation using the Atoll radio planning software with the Hatta Cost-231 propagation model and the Standard Propagation Model. From the comparison results, the margin value is used as a correction factor. The comparison results obtained based on the quality of SINR, The result of using the Standard Propagation Model is closer to the drive test measurement results than using Cost-231 Hatta. However, based on the quality of RSRQ, both propagation models yield almost the same results. Here are found that the correction factor for SINR obtained the Cost-231 Hatta propagation model of 6.15 dB and the Standard Propagation Model model of 6.11 dB. While for RSRQ correction factor the margin for both propagation models is -2 dB

Positron flux and γ-ray emission from Geminga pulsar and pulsar wind nebula

Nearby pulsars have been suggested as sources of $\sim$TeV $e^+/e^-$ Cosmic Ray (CR) excess on Earth. The High-Altitude Water Cherenkov Observatory (HAWC) detected extended TeV emission regions in the direction of two nearby middle-aged pulsars, Geminga and PSR B0656+14. By modeling the TeV emission as inverse Compton emission from electron-positron pairs diffusing in the interstellar medium (ISM), the HAWC collaboration derives a diffusion coefficient much smaller than the standard value in the vicinity of the two pulsars, which make them unlikely the origin of the positron excess. We propose that the observed $\gamma$-ray emission originate from the relic pulsar wind nebula. A two zone diffusion model with a slow diffusion in the nebula and a fast diffusion in the ISM can explain the HAWC surface brightness profile and the positron excess simultaneously. Inefficient diffusion in the $\gamma$-ray emission region surrounding a middle-aged pulsar maybe a common phenomenon that can be tested by future observation. The implied diffusion coefficient in the ISM is smaller than the one suggested by the standard CR propagation model, but it is fully consistent with the predictions of the spiral arm model.

Information content of ship noise on a drifting volumetric array for passive environmental sensing

This study investigates the information content of ship noise received on a drifting volumetric array of hydrophones in shallow water marine environments for the purposes of conducting acoustic thermometry or other environmental inversions. Passive inversions for physical oceanographic parameters are conducted using travel time differences, determined by cross-correlating ship noise received on hydrophones suspended beneath drifting buoys. Ships are tracked using the Automatic Identification System (AIS). Information content gained from the inversion is assessed using traditional a-posteriori error analysis. Numerical simulations using a standard normal mode propagation model are used to test limitations of the proposed approach with respect to frequency band, drifting receiver configuration, precision and accuracy of the inversion results, along with sensitivity to environmental and position mismatch. Performance predictions using this model are compared with results from a field experiment using at-sea data collected off the coast of New London, CT in Long Island Sound. Information gathered using passive acoustic inversion methods on drifting arrays can be used to constrain general circulation models (GCMs), in coastal environments, where ship noise is ubiquitous, environmental data are sparse, and the oceanography is dynamic and important for understanding large-scale ocean processes.

Multi-phase outflows as probes of AGN accretion history

Powerful outflows with a broad range of properties (such as velocity, ionization, radial scale and mass loss rate) represent a key feature of active galactic nuclei (AGN), even more so since they have been simultaneously revealed also in individual objects. Here we revisit in a simple analytical framework the recent remarkable cases of two ultraluminous infrared quasars, IRAS F11119+3257 and Mrk 231, which allow us to investigate the physical connection between multi-phase AGN outflows across the ladder of distance from the central supermassive black hole (SMBH). We argue that any major deviations from the standard outflow propagation models might encode unique information on the past SMBH accretion history, and briefly discuss how this could help address some controversial aspects of the current picture of AGN feedback.

Extending standard urban outdoor noise propagation models to complex geometries

A hybrid method that combines a noise engineering method and the 2.5D boundary element method approximates outdoor sound propagation in large domains with complex objects more accurately than noise engineering methods alone and more efficiently than reference methods alone. Noise engineering methods (e.g., ISO 9613-2 or CNOSSOS-EU) efficiently approximate sound levels from roads, railways, and industrial sources in cities for simple, box-shaped geometries by first finding the propagation paths between the source and receiver, then applying attenuations (e.g., geometrical divergence and atmospheric absorption) to each path, and finally incoherently summing all of the path contributions. Standard engineering methods cannot model more complicated geometries but introducing an additional attenuation term quantifies the influence of complex objects. Calculating this extra attenuation term requires reference calculations but performing reference computations for each path is too computationally expensive. Thus, the extra attenuation term is linearly interpolated from a data table containing the corrections for many source/receiver positions and frequencies. The 2.5D boundary element method produces the levels for the real and simplified geometries and subtracting them yields a table of corrections. For a T-shaped barrier with two buildings, this approach reduces the mean error by approximately 2 dBA compared to a standard engineering method.

The effect of remote and internal crack stresses on mixed-mode brittle fracture propagation of open cracks under compressive loading

The standard mixed-mode fracture propagation models, based on near-tip stress approximations, have limited application for fractures under far-field compressive loading. Most studies in engineering mechanics have focused on extending or modifying the standard models to solve mixed-mode fracture propagation under tensile loading. However, for geomechanics and geologic applications, the ambient stress is compressive, and internal fluid pressure or frictional stress can play an important role in the near-tip stress field altering the predicted propagation path. In this study, we modify the maximum tangential principal stress (MTPS) criterion to improve fracture propagation path predictions relative to published experimental results under both tensile and compressive external loadings. In addition, new experimental results on open cracks under compressive loading were consistent with our proposed model. Accurate propagation path predictions are independent on whether small scale yielding conditions are met in the experiments. Overall, the modified MTPS-criterion proved capable for predicting propagation paths for open cracks under compressive far-field loading.

A UHF Path Loss Model Using Learning Machine for Heterogeneous Networks

In this paper, we present and evaluate a new propagation model for heterogeneous networks. The designed model is multiband, multienvironment, and is usable for short and long distance. For this research, a measurement campaign was conducted in Tunis (Tunisia) using continuous wave analog technology. It concerns the most used bands (450, 850, 1800, 2100, and 2600 MHz) in rural, suburban, and urban environments. Measurements are split into two independent and random sets. The first one is used for model training, whereas the second is used for model validation. The new model is based on neural networks, uses back propagation algorithm, and obtains its inputs from Standard Propagation Model, to which we have added more parameters such as frequency, environment type, land use distribution, and diffraction loss. Model variables are computed from accurate Digital Terrain Model and Land Used maps with 2-m resolution. The statistical analysis has shown that the developed model is accurate as we obtained the following metrics: 0.235-dB absolute mean error, 6.850-dB standard deviation, and 85% correlation factor. The obtained simulation results are then compared to SPM and ITU-R P.1812-4 prediction, which are taken as reference to highlight the benefit of the new model.

Earth’s magnetic field effect on MUF calculation and consequences for hmF2 trend estimates

Knowledge of the state of the upper atmosphere, and in particular of the ionosphere, is essential in several applications such as systems used in radio frequency communications, satellite positioning and navigation. In general, these systems depend on the state and evolution of the ionosphere. In all applications involving the ionosphere an essential task is to determine the path and modifications of ray propagation through the ionospheric plasma. The ionospheric refractive index and the maximum usable frequency (MUF) that can be received over a given distance are some key parameters that are crucial for such technological applications. However, currently the representation of these parameters are in general simplified, neglecting the effects of Earth’s magnetic field. The value of M(3000)F2, related to the MUF that can be received over 3000km is routinely scaled from ionograms using a technique which also neglects the geomagnetic field effects assuming a standard simplified propagation model. M(3000)F2 is expected to be affected by a systematic trend linked to the secular variations of Earth’s magnetic field. On the other hand, among the upper atmospheric effects expected from increasing greenhouse gases concentration is the lowering of the F2-layer peak density height, hmF2. This ionospheric parameter is usually estimated using the M(3000)F2 factor, so it would also carry this “systematic trend”. In this study, the geomagnetic field effect on MUF estimations is analyzed as well as its impact on hmF2 long-term trend estimations. We find that M(3000)F2 increases when the geomagnetic field is included in its calculation, and hence hmF2, estimated using existing methods involving no magnetic field for M(3000)F2 scaling, would present a weak but steady trend linked to these variations which would increase or compensate the few kilometers decrease (~2km per decade) expected from greenhouse gases effect.