Relevant bibliographies by topics / Propagation Path Loss Model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Propagation Path Loss Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Propagation Path Loss Model"

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Elechi, Promise, and Paul Osaretin Otasowie. "Comparison of Empirical Path Loss Propagation Models with Building Penetration Path Loss Model." International Journal on Communications Antenna and Propagation (IRECAP) 6, no. 2 (April 30, 2016): 116. http://dx.doi.org/10.15866/irecap.v6i2.8013.

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Yang, Yi Huai. "Visual Simulation of Mobile Channel Model." Applied Mechanics and Materials 246-247 (December 2012): 1209–13. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.1209.

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Simulink is the integrated environment of system modelling and simulation, which is being widespread used. This paper describes the MATLAB visual simulation of the propagation path loss model for telecommunication systems. We simulated the whole process of COST231-Walfisch-Ikegami model with high accuracy, built a visual simulation frame and the path loss curves are given. This method can be used in studying other propagation path loss models in propagation environments.
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Kosz, Paweł. "An Empirical Propagation Model for Corridors in Office Buildings." International Journal of Electronics and Telecommunications 63, no. 1 (March 1, 2017): 5–10. http://dx.doi.org/10.1515/eletel-2017-0001.

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Abstract This paper presents an empirical propagation path loss model for corridors in office buildings. The proposed model estimates changeable character of radio signal attenuation, based on a special approach as a combination of the simple free-space model with the author’s model. The measurement stand and measurement scenario are described. The propagation path loss research have been made in corridor for different frequencies in range 30 MHz to 290 MHz. A significant number of measurement results were allowed an analysis of the radio wave propagation conditions in the environment. In general, the propagation path loss increases for each measurement frequencies with length of propagation route. Based on measurement data, the new empirical propagation path loss model was developed. For this purpose, the regression analysis was made. The novelty of this model is that it could be used for estimate propagation path loss in measured environment for different radio wave frequencies. At the end, in order to justification the practical usefulness of described method for estimate a radio wave attenuation, the statistical evaluation was made. Thus, the results of the statistical analysis (ME, SEE and R2 values) are satisfactory for each measured radio wave frequency.
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A.Mawjoud, Sami. "Path Loss Propagation Model Prediction for GSM Network Planning." International Journal of Computer Applications 84, no. 7 (December 18, 2013): 30–33. http://dx.doi.org/10.5120/14592-2830.

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Bhuvaneshwari, A., R. Hemalatha, and T. SatyaSavithri. "Path Loss Model Optimization Using Stochastic Hybrid Genetic Algorithm." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 464. http://dx.doi.org/10.14419/ijet.v7i4.10.21041.

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In the context of modeling the propagation of mobile radio signals, optimizing the existing path loss model is largely required to precisely represent the actual propagation medium. In this paper, a hybrid tuning approach is proposed by merging the stochastic Weighted Least Square method and Genetic algorithm. The proposed hybrid optimization is employed to optimize the parameters of Cost 231 Hata propagation model and is validated by cellular field strength measurements at 900 MHz in the sub urban region. The hybrid optimization is compared with optimized results of Weighted Least Square method and Genetic algorithm. The least values of Mean Square error (0.2702), RMSE (0.4798) and percentage Relative error (3.96) justify the tuning precision of the hybrid method. The proposed optimization approach could be used by network service providers to improve the quality of service and in mobile radio network planning of 900 MHz band for 4G LTE services.
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Tabakcioglu, Mehmet Baris, and Ahmet Cansiz. "Application of S-UTD-CH Model into Multiple Diffraction Scenarios." International Journal of Antennas and Propagation 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/285304.

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Propagation prediction models based on ray tracing in coverage estimation for digital broadcasting systems are compared. Geometrical Theory of Diffraction (GTD), Slope Uniform Theory of Diffraction (S-UTD), and Slope UTD with Convex Hull (S-UTD-CH) models are compared for computation time and propagation path loss. S-UTD-CH model is optimum model with respect to computation time and relative path loss.
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Juang, Rong-Terng. "Explainable Deep-Learning-Based Path Loss Prediction from Path Profiles in Urban Environments." Applied Sciences 11, no. 15 (July 21, 2021): 6690. http://dx.doi.org/10.3390/app11156690.

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This paper applies a deep learning approach to model the mechanism of path loss based on the path profile in urban propagation environments for 5G cellular communication systems. The proposed method combines the log-distance path loss model for line-of-sight propagation scenarios and a deep-learning-based model for non-line-of-sight cases. Simulation results show that the proposed path loss model outperforms the conventional models when operating in the 3.5 GHz frequency band. The standard deviation of prediction error was reduced by 34% when compared to the conventional models. To explain the internal behavior of the proposed deep-learning-based model, which is a black box in nature, eight relevant features were selected to model the path loss based on a linear regression approach. Simulation results show that the accuracy of the explanatory model reached 72% when it was used to explain the proposed deep learning model. Furthermore, the proposed deep learning model was also evaluated in a non-standalone 5G New Radio network in the urban environment of Taipei City. The real-world measurements show that the standard deviation of prediction error can be reduced by 30–43% when compared to the conventional models. In addition, the transparency of the proposed deep learning model reached 63% in the realistic 5G network.
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Noh, Sun-Kuk. "A Study on Path-loss Wave Propagation Model for V2X." Journal of the Institute of Electronics and Information Engineers 55, no. 5 (May 31, 2018): 116–20. http://dx.doi.org/10.5573/ieie.2018.55.5.116.

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Yu, Junyi, Wei Chen, Kun Yang, Changzhen Li, Fang Li, and Yishui Shui. "Path Loss Channel Model for Inland River Radio Propagation at 1.4 GHz." International Journal of Antennas and Propagation 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/5853724.

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In this paper, a propagation path loss model for inland river is proposed by three improvements compared with the Round Earth Loss (REL) model for open-sea environment. Specifically, parameters optimization uses Okumura-Hata model in dB scale to replace the equation transformed from the free space loss in REL model; secondly, diffraction loss caused by the obstacles (e.g., large buildings, bridges, or some other facilities near the river bank) is also taken into account; mixed-path methodology as another improvement is used for Inland River (IR) model because the actual propagation environment between transmitter (TX) antenna and receiver (RX) antenna contains both land part and water part. The paper presents a set of 1.4 GHz measurements conducted along the Yangtze River in Wuhan. According to the comparison between path loss models and experimental results, IR model shows a good matching degree. After that, Root Mean Square Error (RMSE), Grey Relation Grade and Mean Absolute Percentage Error (GRG-MAPE), Pearson Correlation Coefficient, and Mean Absolute Percentage Error (PCC-MAPE) are employed to implement quantitative analysis. The results prove that IR model with consideration of mixed path and deterministic information is more accurate than other classic empirical propagation models for these scenarios.
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Faruk, Nasir, N. T. Surajudeen-Bakinde, Abubakar Abdulkarim, Segun I. Popoola, A. Abdulkarim, Lukman A. Olawoyin, and Aderemi A. Atayero. "ANFIS Model for Path Loss Prediction in the GSM and WCDMA Bands in Urban Area." ELEKTRIKA- Journal of Electrical Engineering 18, no. 1 (April 24, 2019): 1–10. http://dx.doi.org/10.11113/elektrika.v18n1.140.

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Path loss propagation is a vital concern when designing and planning networks in mobile communication systems. Propagation models such as the empirical, deterministic and theoretical models, which possess complex, inconsistent, time-consuming and non-adaptable features, have proven to be inefficient in designing of wireless systems, thereby resulting in the need for a more reliable model. Artificial Intelligence methods seem to overcome the drawbacks of the propagation models for predicting path loss. In this paper, the ANFIS approach to path loss prediction in the GSM and WCDMA bands is presented for selected urban areas in Nigeria. Furthermore, the effects of the number of Membership Functions (MFs) are investigated. The prediction results indicated that the ANFIS model outperformed the Hata, Cost-231, Egli and ECC-33 models in both Kano and Abuja urban areas. In addition, an increase in the number of MFs conceded an improved RMSE result for the generalized bell-shaped MF. The general performance and outcome of this research work show the efficiency and usefulness of the ANFIS model in improving prediction accuracy over propagation models
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Dissertations / Theses on the topic "Propagation Path Loss Model"

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Liechty, Lorne Christopher. "Path Loss Measurements and Model Analysis of a 2.4 GHz Wireless Network in an Outdoor Environment." Thesis, Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/16308.

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Careful network planning has become increasingly critical with the rising deployment, coverage, and congestion of wireless local area networks (WLANs). This thesis outlines the achieved prediction accuracy of a direct-ray, single path loss exponent, adapted Seidel-Rappaport propagation model as determined through measurements and analysis of the established 2.4 GHz, 802.11g outdoor WiFi network deployed on the campus of the Georgia Institute of Technology. Additionally, the viability of using the obtained model parameters as a means for planning future network deployment is discussed. Analysis of measured data shows that accurate predictive planning for network coverage is possible without the need for overly complicated modeling techniques such as ray tracing. The proposed model performs with accuracy comparable to other commonly accepted, more complicated models and is offered as a simple, yet strong predictive model for network planning having both speed and accuracy. Results show, that for the area under study, the standard deviation of the prediction error for the proposed model is below 6.8dB in all analyzed environments, and is approximately 5.5dB on average. Further, the accuracy of model predictions in new environments is shown to be satisfactory for network planning.
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Rowe, Christopher D. "Channel Propagation Model for Train to Vehicle Alert System at 5.9 GHz using Dedicated Short Range Communication." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/73178.

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The most common railroad accidents today involve collisions between trains and passenger vehicles at railroad grade crossings [1][2]. Due to the size and speed of a train, these collisions generally result in significant damage and serious injury. Despite recent efforts by projects such as Operation Lifesaver to install safety features at grade crossings, up to 80% of the United States railroad grade crossings are classified as 'unprotected' with no lights, warnings, or crossing gates [2]. Further, from January to September 2012, nearly 10% of all reported vehicle accidents were a result of train-to-vehicle collisions. These collisions also accounted for nearly 95% of all reported fatalities from vehicular accidents [2]. To help provide a more rapidly deployable safety system, advanced dedicated short range communication (DSRC) systems are being developed. DSRC is an emerging technology that is currently being explored by the automotive safety industry for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications to provide intelligent transportation services (ITS). DSRC uses WAVE protocols and the IEEE 1609 standards. Among the many features of DSRC systems is the ability to sense and then provide an early warning of a potential collision [6]. One potential adaption for this technology is for use as a train-to-vehicle collision warning system for unprotected grade crossings. These new protocols pose an interesting opportunity for enhancing cybersecurity since terrorists will undoubtedly eventually identify these types of mass disasters as targets of opportunity. To provide a thorough channel model of the train to vehicle communication environment that is proposed above, large-scale path loss and small scale fading will both be analyzed to characterize the propagation environment. Measurements were collected at TTCI in Pueblo Colorado to measure the received signal strength in a train to vehicle communication environment. From the received signal strength, different channel models can be developed to characterize the communication environment. Documented metrics include large scale path loss, Rician small scale fading, Delay spread, and Doppler spread. An analysis of the DSRC performance based on Packet Error Rate is also included.
Master of Science
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Almalki, Faris Abdullah E. "Optimisation of a propagation model for last mile connectivity with low altitude platforms using machine learning." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16177.

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Our related research review on propagation models reveals six factors that are significant in last mile connectivity via LAP: path loss, elevation angle, LAP altitude, coverage area, power consumption, operation frequency, interference, and antenna type. These factors can help with monitoring system performance, network planning, coverage footprint, receivers' line-of-sight, quality of service requirements, and data rates which may all vary in response to geomorphology characteristics. Several competing propagation models have been proposed over the years but whilst they collectively raise many shortcomings such as limited altitude up to few tens of meters, lack of cover across different environments, low perdition accuracy they also exhibit several advantages. Four propagation models, which are representatives of their types, have been selected since they exhibit advantages in relation to high altitude, wide coverage range, adaption across different terrains. In addition, all four have been extensively deployed in the past and as a result their correction factors have evolved over the years to yield extremely accurate results which makes the development and evaluation aspects of this research very precise. The four models are: ITU-R P.529-3, Okumura, Hata-Davidson, and ATG. The aim of this doctoral research is to design a new propagation model for last-mile connectivity using LAPs technology as an alternative to aerial base station that includes all six factors but does not exhibit any of the shortcomings of existing models. The new propagation model evolves from existing models using machine learning. The four models are first adapted to include the elevation angle alongside the multiple-input multiple-output diversity gain, our first novelty in propagation modelling. The four adapted models are then used as input in a Neural Network framework and their parameters are clustered in a Self-Organizing-Map using a minimax technique. The framework evolves an optimal propagation model that represents the main research contribution of this research. The optimal propagation model is deployed in two proof-of-concept applications, a wireless sensor network, and a cellular structure. The performance of the optimal model is evaluated and then validated against that of the four adapted models first in relation to predictions reported in the literature and then in the context of the two proof-of-concept applications. The predictions of the optimised model are significantly improved in comparison to those of the four adapted propagation models. Each of the two proof-of-concept applications also represent a research novelty.
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Sarwar, Muhammad. "Effects of terrain features on wave propagation: high-frequency techniques." Thesis, University of Kalmar, Department of Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hik:diva-2281.

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This Master thesis deals with wave propagation and starts with wave propagation basics. It briefly presents the theory for the diffraction over terrain obstacles and describes two different path loss models, the Hata model and a FFT-based model. The significance of this paper is that it gives the simulation results for the models mentioned above and presents a comparison between the results obtained from an empirical formula and the FFT-model. The comparison shows that the approach based on Fast Fourier Transform is good enough for prediction of the path loss and that it is a time efficient method.

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Ha, Sean Anthony. "3.5 GHz Indoor Propagation Modeling and Channel Characterization." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/53949.

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In the push for spectrum sharing and open spectrum access, the 3.5 GHz frequency band is under consideration for small cells and general Wireless Local Area Networks (WLAN) in the United States. The same band is beginning to see deployment in China, Japan, and South Korea, for the 4G Long Term Evolution (LTE) cellular standard to increase coverage and capacity in urban areas through small cell deployment. However, since the adoption of this band is new, there is a distinct shortage of propagation data and accurate channel modeling at 3.5 GHz in indoor environments. These models are necessary for cellular coverage planning and evaluating the performance and feasibility of wireless systems. This report presents the results of a fixed wireless channel measurement campaign at 3.5 GHz. Measurements were taken in environments typical of indoor wireless deployment: traditional urban indoor office, hallway, classroom, computer laboratory, and atrium areas, as well as within a hospital. Primarily Non Line of Sight (NLOS) experiments were carried out in areas with a controllable amount of partitions separating the transmitter and receiver in order to document material-based attenuation values. Indoor-to-outdoor measurements were carried out, focusing on attenuation due to common exterior building materials such as concrete, brick, wood, and reinforced glass. Documented metrics include large scale path loss, log-normal shadowing, and channel power delay profiles combined with delay spread characteristics for multipath analysis. The statistical multi-antenna diversity gain was evaluated to gauge the benefit of using multi-antenna systems in an indoor environment, which has much greater spatial diversity than an outdoor environment. Measurements were compared to indoor path loss models used for WLAN planning in the low GHz range to investigate the applicability of extending these models to 3.5 GHz.
Master of Science
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Blakaj, Valon, and Gent Gashi. "Implementation of a 3D terrain-dependent Wave Propagation Model in WRAP." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-36774.

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The radio wave propagation prediction is one of the key elements for designing an efficient radio network system. WRAP International has developed a software for spectrum management and radio network planning.This software includes some wave propagation models which are used to predict path loss. Current propagation models in WRAP perform the calculation in a vertical 2D plane, the plane between the transmitter and the receiver. The goal of this thesis is to investigate and implement a 3D wave propagation model, in a way that reflections and diffractions from the sides are taken into account.The implemented 3D wave propagation model should be both fast and accurate. A full 3D model which uses high resolution geographical data may be accurate, but it is inefficient in terms of memory usage and computational time. Based on the fact that in urban areas the strongest path between the receiver and the transmitter exists with no joint between vertical and horizontal diffractions [10], the radio wave propagation can be divided into two parts, the vertical and horizontal part. Calculations along the horizontal and vertical parts are performed independently, and after that, the results are combined. This approach leads to less computational complexity, faster calculation time, less memory usage, and still maintaining a good accuracy.The proposed model is implemented in C++ and speeded up using parallel programming techniques. Using the provided Stockholm high resolution geographical data, simulations are performed and results are compared with real measurements and other wave propagation models. In addition to the path loss calculation, the proposed model can also be used to estimate the channel power delay profile and the delay spread.
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Lu, Yao. "Propagation Modeling and Performance Evaluation in an Atrium Building." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177375.

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In this thesis electromagnetic wave propagation is investigated in an indoor environment. The indoor environment is a furnished office building with corridors, corners and rooms. Particularly, there is an atrium through the building in the center. For the study there were measurements available from real building in the 2.1 GHz frequency band. One objective is to design a propagation model that should be simple but reflect the trend of the propagation measurements. Furthermore, a system performance evaluation is carried out based on the implemented model. The proposed 3D model is a combination of the Free Space Path Loss model, the Keenan-Motley model and the recursive diffraction model. The channel predictions from the 2D Keenan-Motley algorithm are quite different from the measurements. Therefore, the 3D Keenan-Motley algorithm is designed to depict the atrium effect and speed up the simulation at the same time. Besides a buttery radiation diagram is created to mimic Kathrein 80010709 antenna installed in the building. Finally, a diffracted path is added to improve the received signal strength for the users around the atrium areas. With all the above procedures, the final results from the model are in good quantitative agreement with the measurement data. With the implemented propagation model, a further analysis of the system performance on the Distributed Antenna System (DAS) is performed. A comparison for the system capacity between the closed building and the atrium building is conducted, showing that the former one benefits more when the number of the cells increases. The reason is the atrium cells suffer severe interference from neighbor cells during high traffic demand scenarios. Then some further cell configurations show that the number of the cells, the geometry performance and the balance of the user fraction should be considered to improve the system capacity.
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Saeed, Asad, Habib Ur Rehman, and Muhammad Hassan Masood. "Performance Analysis and Comparison of Radio Propagation Models for Outdoor Environment in 4G LTE Network." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3241.

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The dissertation concerns about the path loss calculation of Radio Frequency (RF) propagation models for 4G Long Term Evolution (LTE) Network to prefer the best Radio Frequency propagation model. The radio propagation models are very significant while planning of any wireless communication system. A comparative analysis between radio propagation models e.g. SUI model, Okumura model, Cost 231 Hata Model, Cost 231-Walfisch Ikegami and Ericsson 9999 model that would be used for outdoor propagation in LTE. The comparison and performance analysis has been made by using different geological environments e.g. urban, sub-urban and rural areas. The simulation scenario is made to calculate the lowest path loss in above defined environments by using selected frequency and height of base station antennas while keeping a constant distance between the transmitter and receiver antennas.
Asad Saeed C/O Muhammad Awais Hovslagargatan 47 LGH 1004 19431 Stockholm Sweden Mob: 0046723333734
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Vyčítal, Jaroslav. "Šíření signálů bezdrátových komunikačních systémů IEEE 802.11." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-377156.

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This paper deals with the propagation of waves. Here is the wavelength distribution according to the wavelength. It focuses on the UHF and SHF band in which IEEE802.11n operates. Contains model breakdown by cell type. Describes which propagation methods are dominant in the cell type. Several propagation patterns are presented, which are then modeled in Matlab environment.The models are then compared to experimental measurements.
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Manan, Waqas. "Propagation channel models for 5G mobile networks. Simulation and measurements of 5G propagation channel models for indoor and outdoor environments covering both LOS and NLOS Scenarios." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17219.

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At present, the current 4G systems provide a universal platform for broadband mobile services; however, mobile traffic is still growing at an unprecedented rate and the need for more sophisticated broadband services is pushing the limits on current standards to provide even tighter integration between wireless technologies and higher speeds. This has led to the need for a new generation of mobile communications: the so-called 5G. Although 5G systems are not expected to penetrate the market until 2020, the evolution towards 5G is widely accepted to be the logical convergence of internet services with existing mobile networking standards leading to the commonly used term “mobile internet” over heterogeneous networks, with several Gbits/s data rate and very high connectivity speeds. Therefore, to support highly increasing traffic capacity and high data rates, the next generation mobile network (5G) should extend the range of frequency spectrum for mobile communication that is yet to be identified by the ITU-R. The mm-wave spectrum is the key enabling feature of the next-generation cellular system, for which the propagation channel models need to be predicted to enhance the design guidance and the practicality of the whole design transceiver system. The present work addresses the main concepts of the propagation channel behaviour using ray tracing software package for simulation and then results were tested and compared against practical analysis in a real-time environment. The characteristics of Indoor-Indoor (LOS and NLOS), and indoor-outdoor (NLOS) propagations channels are intensively investigated at four different frequencies; 5.8 GHz, 26GHz, 28GHz and 60GHz for vertical polarized directional, omnidirectional and isotropic antennas patterns. The computed data achieved from the 3-D Shooting and Bouncing Ray (SBR) Wireless Insite based on the effect of frequency dependent electrical properties of building materials. Ray tracing technique has been utilized to predict multipath propagation characteristics in mm-wave bands at different propagation environments. Finally, the received signal power and delay spread were computed for outdoor-outdoor complex propagation channel model at 26 GHz, 28 GHz and 60GHz frequencies and results were compared to the theoretical models.
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Books on the topic "Propagation Path Loss Model"

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J, Liebe H., and United States. National Telecommunications and Information Administration, eds. Millimeter-wave propagation in moist air: Model versus path data. [Washington, DC]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1985.

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J, Liebe H., and United States. National Telecommunications and Information Administration, eds. Millimeter-wave propagation in moist air: Model versus path data. [Washington, DC]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1985.

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J, Liebe H., and United States. National Telecommunications and Information Administration., eds. Millimeter-wave propagation in moist air: Model versus path data. [Washington, DC]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1985.

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Millimeter-wave propagation in moist air: Model versus path data. [Washington, DC]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1985.

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J, Liebe H., and United States. National Telecommunications and Information Administration., eds. Millimeter-wave propagation in moist air: Model versus path data. [Washington, DC]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1985.

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J, Liebe H., and United States. National Telecommunications and Information Administration., eds. Millimeter-wave propagation in moist air: Model versus path data. [Washington, DC]: U.S. Dept. of Commerce, National Telecommunications and Information Administration, 1985.

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A 3D Parabolic Equation (PE) Based Technique for Predicting Propagation Path Loss in an Urban Area. Storming Media, 2001.

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Book chapters on the topic "Propagation Path Loss Model"

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Zhu, Qiuming, Chenghua Wang, Xueqiang Chen, Chao Chen, Xinyi Wang, and Chenbeixi Zhang. "Path Loss Prediction Model of Radio Propagation over Lunar Surface." In Communications in Computer and Information Science, 556–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25002-6_77.

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Popoola, Segun I., Aderemi A. Atayero, Nasir Faruk, Carlos T. Calafate, Lukman A. Olawoyin, and Victor O. Matthews. "Standard Propagation Model Tuning for Path Loss Predictions in Built-Up Environments." In Computational Science and Its Applications – ICCSA 2017, 363–75. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62407-5_26.

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Torres-Tovio, Juan M., Nelson A. Pérez-García, Angel D. Pinto-Mangones, Mario R. Macea-Anaya, Samir O. Castaño-Rivera, and Enrique I. Delgado Cuadro. "Novel Lee Model for Prediction of Propagation Path Loss in Digital Terrestrial Television Systems in Montevideo City, Uruguay." In Advances in Intelligent Systems and Computing, 542–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32022-5_50.

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Akinyemi, P., J. S. Ojo, C. I. Abiodun, O. L. Ojo, and O. A. Abiodun. "Path Loss Propagation Prediction and Optimization Using Walfisch-Bertoni Model at 900 and 1800 MHz Over Macro-Cellular Western Regions of Nigeria." In Proceedings of the Future Technologies Conference (FTC) 2018, 623–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02683-7_44.

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Bilgehan, Bülent. "Fuzzy Based Wireless Channel Path Loss Prediction Model." In Advances in Intelligent Systems and Computing, 515–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64058-3_64.

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Ko, Kwangsoob. "The Consideration of GPS Jamming Signal Due to Propagation Path Loss." In Lecture Notes in Electrical Engineering, 915–20. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6516-0_100.

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Li, Yaning, Baoguo Yu, and Xingli Gan. "Research on Path Loss and Multipath Propagation of Indoor Pseudolite Signal." In China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume I, 441–51. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4588-2_38.

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Kim, Ryangsoo, Wooyeol Choi, Hyuk Lim, and Jae-Hyung Jang. "POSTER Zero-Configuration Path Loss Model-Based Indoor Localization." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 219–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30973-1_20.

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Cheerla, Sreevardhan, D. Venkata Ratnam, and J. R. K. Kumar Dabbakuti. "An Optimized Path Loss Model for Urban Wireless Channels." In Lecture Notes in Electrical Engineering, 293–301. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3828-5_31.

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Choi, Dong You. "Measurement of Radio Propagation Path Loss over the Sea for Wireless Multimedia." In NETWORKING 2006. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems, 525–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11753810_44.

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Conference papers on the topic "Propagation Path Loss Model"

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Oda, Y. "Advanced LOS path loss model in microwave mobile communications." In Tenth International Conference on Antennas and Propagation (ICAP). IEE, 1997. http://dx.doi.org/10.1049/cp:19970356.

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Bolli, Sridhar. "Propagation Path loss model based on Environmental Variables." In 2020 12th International Conference on Information Technology and Electrical Engineering (ICITEE). IEEE, 2020. http://dx.doi.org/10.1109/icitee49829.2020.9271731.

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Wu, Liyun, Xiaomei Liu, Yingjian Qi, and Zhengpeng Wu. "Propagation Path Loss Prediction Based-on Grey Verhulst Model." In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS). IEEE, 2018. http://dx.doi.org/10.1109/icis.2018.8466461.

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Klozar, Lukas, and Jan Prokopec. "Propagation path loss models for mobile communication." In 2011 21st International Conference Radioelektronika (RADIOELEKTRONIKA 2011). IEEE, 2011. http://dx.doi.org/10.1109/radioelek.2011.5936478.

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Joseph, W., L. Roelens, and L. Martens. "Path Loss Model for Wireless Applications at 3500 MHz." In 2006 IEEE Antennas and Propagation Society International Symposium. IEEE, 2006. http://dx.doi.org/10.1109/aps.2006.1711702.

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Goulianos, Angelos A., Tim W. C. Brown, and Stavros Stavrou. "A Novel Path-Loss Model for UWB Off-Body Propagation." In 2008 IEEE Vehicular Technology Conference (VTC 2008-Spring). IEEE, 2008. http://dx.doi.org/10.1109/vetecs.2008.105.

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Conway, G. A., W. G. Scanlon, S. L. Cotton, and M. J. Bentum. "An analytical path-loss model for on-body radio propagation." In 2010 URSI International Symposium on Electromagnetic Theory (EMTS 2010). IEEE, 2010. http://dx.doi.org/10.1109/ursi-emts.2010.5637009.

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Maaz, Issam, Jean-Marc Conrat, and Jean-Christophe Cousin. "Path loss models in LOS conditions for relay mobile channels." In 2014 8th European Conference on Antennas and Propagation (EuCAP). IEEE, 2014. http://dx.doi.org/10.1109/eucap.2014.6902577.

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Hu, Luoquan, Hongbo Zhu, and Yifan Chen. "Generalized Path Loss Model for Wireless Channels in Homogenous Propagation Environments." In 2007 Asia-Pacific Microwave Conference - (APMC 2007). IEEE, 2007. http://dx.doi.org/10.1109/apmc.2007.4554575.

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Lee, William C. Y. "A New Propagation Path-Loss Prediction Model for Military Mobile Access." In MILCOM 1985 - IEEE Military Communications Conference. IEEE, 1985. http://dx.doi.org/10.1109/milcom.1985.4795052.

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Reports on the topic "Propagation Path Loss Model"

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Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41034.

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We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.
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Hart, Carl R., D. Keith Wilson, Chris L. Pettit, and Edward T. Nykaza. Machine-Learning of Long-Range Sound Propagation Through Simulated Atmospheric Turbulence. U.S. Army Engineer Research and Development Center, July 2021. http://dx.doi.org/10.21079/11681/41182.

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Conventional numerical methods can capture the inherent variability of long-range outdoor sound propagation. However, computational memory and time requirements are high. In contrast, machine-learning models provide very fast predictions. This comes by learning from experimental observations or surrogate data. Yet, it is unknown what type of surrogate data is most suitable for machine-learning. This study used a Crank-Nicholson parabolic equation (CNPE) for generating the surrogate data. The CNPE input data were sampled by the Latin hypercube technique. Two separate datasets comprised 5000 samples of model input. The first dataset consisted of transmission loss (TL) fields for single realizations of turbulence. The second dataset consisted of average TL fields for 64 realizations of turbulence. Three machine-learning algorithms were applied to each dataset, namely, ensemble decision trees, neural networks, and cluster-weighted models. Observational data come from a long-range (out to 8 km) sound propagation experiment. In comparison to the experimental observations, regression predictions have 5–7 dB in median absolute error. Surrogate data quality depends on an accurate characterization of refractive and scattering conditions. Predictions obtained through a single realization of turbulence agree better with the experimental observations.
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