Relevant bibliographies by topics / Path Loss Prediction Models

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  2. Dissertations / Theses
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Academic literature on the topic 'Path Loss Prediction Models'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Path Loss Prediction Models"

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Naik, Udaykumar, and Vishram N. Bapat. "Adaptive Empirical Path Loss Prediction Models for Indoor WLAN." Wireless Personal Communications 79, no. 2 (July 9, 2014): 1003–16. http://dx.doi.org/10.1007/s11277-014-1914-9.

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Nguyen, Chi, and Adnan Ahmad Cheema. "A Deep Neural Network-Based Multi-Frequency Path Loss Prediction Model from 0.8 GHz to 70 GHz." Sensors 21, no. 15 (July 28, 2021): 5100. http://dx.doi.org/10.3390/s21155100.

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Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.
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Yamamoto, A., K. Ogawa, T. Horimatsu, A. Kato, and M. Fujise. "Path-Loss Prediction Models for Intervehicle Communication at 60 GHz." IEEE Transactions on Vehicular Technology 57, no. 1 (January 2008): 65–78. http://dx.doi.org/10.1109/tvt.2007.901890.

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Garah, Messaoud, Houcine Oudira, Lotfi Djouane, and Nazih Hamdiken. "Particle Swarm Optimization for the Path Loss Reduction in Suburban and Rural Area." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 2125. http://dx.doi.org/10.11591/ijece.v7i4.pp2125-2131.

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In the present work, a precise optimization method is proposed for tuning the parameters of the COST231 model to improve its accuracy in the path loss propagation prediction. The Particle Swarm Optimization is used to tune the model parameters. The predictions of the tuned model are compared with the most popular models. The performance criteria selected for the comparison of various empirical path loss models is the Root Mean Square Error (RMSE). The RMSE between the actual and predicted data are calculated for various path loss models. It turned out that the tuned COST 231 model outperforms the other studied models.
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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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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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Phillips, Caleb, Douglas Sicker, and Dirk Grunwald. "Bounding the Practical Error of Path Loss Models." International Journal of Antennas and Propagation 2012 (2012): 1–21. http://dx.doi.org/10.1155/2012/754158.

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We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios wherea prioripredictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible.
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Nossire, Zyad, Navarun Gupta, Laiali Almazaydeh, and Xingguo Xiong. "New Empirical Path Loss Model for 28 GHz and 38 GHz Millimeter Wave in Indoor Urban under Various Conditions." Applied Sciences 8, no. 11 (November 1, 2018): 2122. http://dx.doi.org/10.3390/app8112122.

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Due to rapid development in mobile communication technology in recent years, the demand for high quality and high capacity networks with thorough coverage has become a major necessity. Several models have been developed for predicting wireless signal coverage in urban areas, but these models suffer from inadequately calculating certain conditions, such as weather and building materials, especially window size. In this paper, we propose a new path loss prediction model based on the measurement of new indicators, such as window size, temperature, and humidity conditions, after which an extensive statistical analysis using a linear regression technique was implemented in order to validate the new indicators. As the new indicators were incorporated into the Okumura model to derive a new path loss model, the results showed that the proposed model provides an accurate prediction of the received signal strength in a given propagation environment. Our model enhanced the prediction of path loss by 10% when compared to the Okumura and by 15% when compared to the COST-Hata.
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O., Ogbeide K., and Eko Mwenrenren E. J. "Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach." APTIKOM Journal on Computer Science and Information Technologies 1, no. 2 (June 1, 2016): 77–84. http://dx.doi.org/10.34306/csit.v1i2.52.

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The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.
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O., Ogbeide K., and Eko Mwenrenren E. J. "Path-Loss Prediction for UHF/VHF Signal Propagation in Edo State: Neural Network Approach." APTIKOM Journal on Computer Science and Information Technologies 1, no. 2 (July 1, 2016): 77–84. http://dx.doi.org/10.11591/aptikom.j.csit.113.

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The aim of this paper is to present and evaluate artificial neural network model used for path loss prediction of signal propagation in the VHF/UHF spectrum in Edo state.Measurement data obtained from three television broadcasting stations in Edo state, operating at 189.25MHz, 479.25MHz, and 743.25MHz, is used to train and evaluate the artificial neural network. A two layer neural network with one hidden and one output layer is evaluated regarding prediction accuracy and generalization properties. The path loss prediction results obtained by using the artificial neural network model are evaluated against the Hata and Walfisch-Ikegami empirical path loss models .Result analysis shows that the artificial neural network performs well as regards to prediction accuracy and generalization ability. The ANN performed better across all performance measures in comparison to the Hata and Walfisch-Ikegami and Line of Sight models in estimating path loss in vhf/uhf spectrum in Edo state.
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Dissertations / Theses on the topic "Path Loss Prediction Models"

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Akkasli, Cem. "Methods for Path loss Prediction." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-6127.

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Large scale path loss modeling plays a fundamental role in designing both fixed and mobile radio systems. Predicting the radio coverage area of a system is not done in a standard manner. Wireless systems are expensive systems. Therefore, before setting up a system one has to choose a proper method depending on the channel environment, frequency band and the desired radio coverage range. Path loss prediction plays a crucial role in link budget analysis and in the cell coverage prediction of mobile radio systems. Especially in urban areas, increasing numbers of subscribers brings forth the need for more base stations and channels. To obtain high efficiency from the frequency reuse concept in modern cellular systems one has to eliminate the interference at the cell boundaries. Determining the cell size properly is done by using an accurate path loss prediction method. Starting from the radio propagation phenomena and basic path loss models this thesis aims at describing various accurate path loss prediction methods used both in rural and urban environments. The Walfisch-Bertoni and Hata models, which are both used for UHF propagation in urban areas, were chosen for a detailed comparison. The comparison shows that the Walfisch-Bertoni model, which involves more parameters, agrees with the Hata model for the overall path loss.

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Cavalcante, Gustavo Ara?jo. "Otimiza??o de modelos de predi??o da perda de propaga??o aplic?veis em 3,5GHZ utilizando algoritmos gen?ticos." Universidade Federal do Rio Grande do Norte, 2010. http://repositorio.ufrn.br:8080/jspui/handle/123456789/15319.

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Made available in DSpace on 2014-12-17T14:55:43Z (GMT). No. of bitstreams: 1 GustavoAC__DISSERT.pdf: 2594771 bytes, checksum: b46a4c0b026ad559d9ed126acb92b471 (MD5) Previous issue date: 2010-07-09
Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
The telecommunications industry has experienced recent changes, due to increasing quest for access to digital services for data, video and multimedia, especially using the mobile phone networks. Recently in Brazil, mobile operators are upgrading their networks to third generations systems (3G) providing to users broadband services such as video conferencing, Internet, digital TV and more. These new networks that provides mobility and high data rates has allowed the development of new market concepts. Currently the market is focused on the expansion of WiMAX technology, which is gaining increasingly the market for mobile voice and data. In Brazil, the commercial interest for this technology appears to the first award of licenses in the 3.5 GHz band. In February 2003 ANATEL held the 003/2002/SPV-ANATEL bidding, where it offered blocks of frequencies in the range of 3.5 GHz. The enterprises who purchased blocks of frequency were: Embratel, Brazil Telecom (Vant), Grupo Sinos, Neovia and WKVE, each one with operations spread in some regions of Brazil. For this and other wireless communications systems are implemented effectively, many efforts have been invested in attempts to developing simulation methods for coverage prediction that is close to reality as much as possible so that they may become believers and indispensable tools to design wireless communications systems. In this work wasm developed a genetic algorithm (GA's) that is able to optimize the models for predicting propagation loss at applicable frequency range of 3.5 GHz, thus enabling an estimate of the signal closer to reality to avoid significant errors in planning and implementation a system of wireless communication
O setor de telecomunica??es vem passando por grandes transforma??es, devido ao aumento da busca por acesso a servi?os digitais de transmiss?o de dados, v?deo e multim?dia, especialmente, por meios das redes de telefonia m?vel. Recentemente, no Brasil, as operadoras de telefonia m?vel v?m atualizando suas redes para sistemas de terceira gera??o (3G) proporcionando aos usu?rios servi?os em banda larga como v?deo confer?ncia, Internet e TV digital, dentre outros. Essas novas redes que proporcionam mobilidade e elevadas taxas de transmiss?o t?m permitido o desenvolvimento de novos conceitos de mercado de servi?os. Atualmente o mercado est? voltado para a expans?o da tecnologia WiMAX, que v?m ganhando cada vez mais o mercado das comunica??es m?veis de voz e de dados. No Brasil o interesse comercial para esta tecnologia aparece com a primeira outorga de licen?as em 3,5 GHz. Em fevereiro de 2003, a ANATEL realizou a licita??o 003/2002/SPV-ANATEL, onde ofereceu blocos de frequ?ncia em 3,5 GHz. As operadoras que adquiriram os blocos de frequ?ncia foram: Embratel, Brasil Telecom (Vant), Grupo Sinos, Neovia e WKVE, cada uma com opera??es distribu?das em regi?es do Brasil. Para que esse e outros sistemas de comunica??es sem fio sejam implementados com efici?ncia, muitos esfor?os t?m sido investidos na tentativa de desenvolvimento de m?todos de simula??o, de predi??o e de cobertura que se aproximem da realidade o melhor poss?vel, de forma a que se possam tornar ferramentas fi?is e indispens?veis no planejamento dos sistemas de comunica??es sem fio. Neste trabalho, foi desenvolvido um algoritmo gen?tico (AG s) capaz de otimizar os modelos de predi??o de perda de propaga??o aplic?veis na frequ?ncia de 3,5 GHz, possibilitando dessa forma uma estimativa do sinal mais pr?xima da realidade, evitando erros significativos no planejamento e implementa??o de um sistema de comunica??o sem fio
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Matsunaga, Richard. "Path loss prediction and location variability for mobile radio." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1996. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq21124.pdf.

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Kidner, David B. "Digital terrain models for radio path loss calculations." Thesis, University of South Wales, 1991. https://pure.southwales.ac.uk/en/studentthesis/digital-terrain-models-for-radio-path-loss-calculations(6733f679-d3c0-4a25-916f-0464321ea520).html.

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This work addresses the problem of digital terrain modelling for estimating radio path propagation within a mobile communication system. The ideal requirements are for a data structure which is storage efficient and computationally efficient for calculating profiles, whilst elevation errors should be constrained and radio path loss errors should be minimised. For a digital terrain model (DTM) to be considered viable as an alternative to the regular grid, it should: (i) produce a storage saving of at least 75% over the regular grid; (ii) be error constrained to a maximum absolute error of 10 metres; (iii) produce only a small overall average elevation error; (iv) preserve critical terrain characteristics such as ridges, peaks and slopes; (v) produce 95% of profiles to within a radio path loss error of ± 6 decibels; and (vi) be as computationally efficient as the regular grid. This research focuses on the implementation of a number of prototype DTMs, including a regular grid, sub-sampled grids, variable density grids, elevation difference grids, polynomial models of fixed and variable degree, surface patch quadtrees, and triangulated irregular networks (TINs). Each of these DTMs are examined in terms of the criteria outlined above. No DTM fulfils all of these requirements. The user should identify the relative importance of each requirement before selecting a specific model. For this study, computational efficiency is identified as the criterion which can be considered the least important. With this in mind, two original DTMs are developed. These are optimised with respect to storage and error constraints. The proposed Huffman-encoded DTM represents the deviations of a regular grid of heights from linearly predicted values as variable-length codes, whilst the Implicit TIN is a storage-efficient triangulated irregular network which reconstructs the original topology of the triangulation at the application stage. Both methods produce storage savings approaching 90% over the regular grid for the data sets tested and are suitable for parallel implementations.
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Alfaro, Hidalgo Luis Adolfo. "Experimental path loss models for UWB multistatic radar systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14656/.

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The use of Ultra-Wideband (UWB) radio technology in a multistatic radar system has recently gained interest to implement Wireless Sensor Networks (WSN) capable of detecting and tracking targets in indoor environments. Due to the increasing attention towards multistatic UWB systems, it is important to perform the radio channel characterization. In this thesis we focus on the characterization of the path loss exponent (α). To perform the present work, the followed methodology was to collect experimental data from the UWB devices using a suitable target, this information was processed with a clutter removal algorithm using the Empty Room (ER) approach, then the contribution of the target was isolated to produce a graph of energy as a function of the product between the target-to-transmitter and the target-to-receiver distances in a bistatic configuration. Finally, using this plot it was properly obtained the value of the path loss exponent. As as additional experimental result, the main statistical parameters associated to the residual clutter were calculated, which are expected to allow having a better understanding and characterization of the radar system performance in the experimental environments.
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Jones, Margaret. "Point prediction in survival time models." Thesis, University of Newcastle Upon Tyne, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340616.

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Isnin, Ismail. "A study on wireless communication error performance and path loss prediction." Thesis, University of Plymouth, 2011. http://hdl.handle.net/10026.1/324.

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One channel model that characterises multipath fading effect of a wireless channel is called Flat Rayleigh Fading channel model. Given the properties of Flat Rayleigh Fading channel, an equation to find the capacity of a Flat Rayleigh fading channel with hard decision decoding is derived. The difference of power requirement to achieve the Additive White Gaussian Noise (AWGN) capacity over a Flat Rayleigh Fading channel fading is found to increase exponentially with Es /N0 . Upper and lower bounds of error performance of linear block codes over a Flat Rayleigh Fading channel are also studied. With the condition that the excess delay of a channel is known earlier, it is shown that a correlator with shorter length, according to excess delay of the channel, can be constructed for use in wireless channel response measurements. Therefore, a rule of construction of a shorter length correlator is defined, involving concatenation of parts of a Constant Amplitude Zero Auto-Correlation (CAZAC) sequence. Simulation of [136,68,24] Double Circulant Code with Dorsch List Decoding is also done in order to evaluate error performance of the channel coding scheme over one of the IEEE Wireless Metropolitan Area Network (WirelessMAN) channel models, the Stanford University Interim Channel Model No. 5 (SUI-5) channel. Performance of the channel cod- ing was severely degraded over the SUI-5 channel when it is compared to its performance over the AWGN channel. Indoor path losses within three multifloor office buildings were investigated at 433 MHz, 869 MHz and 1249 MHz. The work involved series of extensive received signal strength measurements within the buildings for all of the considered frequencies. Results have shown that indoor path loss is higher within a square footprint building than indoor path loss in a rectangular building. Parameters of Log-Distance Path Loss and Floor Attenuation Factor Path Loss models have been derived from the measurement data. In addition, a new indoor path loss prediction model was derived to cater for path loss pre- diction within multifloor buildings with indoor atriums. The model performs with better prediction accuracy when compared with Log-Distance Path Loss and Floor Attenuation Factor Path Loss models.
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Sanz, Solaesa Sergio. "Analytical prediction of turbocharger compressor performance: A comparison of loss models with numerical data." Thesis, KTH, Maskinkonstruktion (Inst.), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-201613.

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One-dimensional models predict the performance of centrifugal compressor in short time, being a helpful design tool in the early design stages. They assume uniform flow through the compressor. Conservation of mass, momentum and energy and some empirical loss correlations are applied to estimate the real outputs. In this thesis, this one-dimensional approach is applied to model a turbocharger compressor. Two different models are implemented. They consist of an impeller, a vaneless diffuser and a volute. The modelstage outputs, pressure ratio and efficiency, are compared with experimental data. Then, both models are further investigated by comparing their losses prediction with validated Reynolds-Averaged Navier-Stokes (RANS) data. The implemented models are taken from literature. They use the same vaneless diffuser and volute approach, but different impeller loss sets. The next impeller losses are studied: incidence, skin friction, choking, jet-wake mixing, blade loading, hub to shroud, tip clearance, shock and distortion losses. The vaneless diffuser outlet is calculated using a one-dimensional numerical solution to the underlying differential equations. For the volute, a set of empirical losses is used. The losses from the CFD are also measured by entropy rise calculations. Due to the complexity of this model, not all the losses can be independently extracted. Incidence, choking, skin friction, blade loading and jet-wake mixing losses are measured along the impeller. Besides, vaneless diffuser and volute losses are also obtained. Results show relative total pressure ratio errors less than 5% in 49 points in a total of 77 predicted operation conditions. 69 points are estimated with a relative error less than 10%. CFD still gives better predictions, especially at low tip speeds. However, at high tip speeds one-dimensional gives similar accuracy. The one-dimensional and CFD losses comparison shows largest differences in the vaneless diffuser and volute models. Some strengths and weaknesses of the impeller losses are revealed, being possible future improvements.
Endimensionella modeller för prestandaprediktering av radialkompressor har i regel en kort ledtid. Detta gör dessa verktyg mycket användbara under designfasen. I modellerna antas att flödet genom kompressorn är uniformt. Vidare används bevarande av massa, rörelsemängd och energi samt empiriska förlusttermer för uppskattning av kompressorns prestanda. I denna avhandling tillämpas en endimensionell metodik för turboladdarkompressorn. Två olika modeller är implementerade. Dessa består av: en rotor, en diffusor utan ledskenor, och en volut. Modellernas uppskattning av kompressorns tryckförhållande samt verkningsgrad jämförs med experimentella mätningar. Därefter jämförs modellernas förlustuppskattningar med validerat Reynolds Averaged Navier-Stokes (RANS) data. Modellerna har implementerats med hjälp av dokumentation från valda källor i litteraturen. Båda modellerna använder samma diffusor- och volutmodell, men har olika förlusttermer för rotorn. Följande rotorförluster undersöks: incidens, väggfriktion, strypning, jet-wake blandning, rotorbladens laddning, nav till hölje laddning, spelrum bladspets till hölje, och ojämnheter i inloppsflödet. Diffusorutloppet beräknas med hjälp av en numerisk lösning av de fundamentala differentialekvationerna. En samling empiriska förlusttermer används för voluten. Förlusterna från numeriska strömningsmekanikberäkningar (CFD) beräknas i form av entropiökningar. På grund av CFD modellens komplexitet kan inte alla förlusttermer extraheras oberoende av varandra. Incidens, strypning, väggfriktion, rotorbladens laddning och jet-wakeblandning mäts längs rotorn. Även diffusorförluster och volutförluster erhålls från CFD:n. Resultatet visar att relativa felet i tryckförhållandet är mindre än fem procent i 49 av 77 undersökta driftpunkter i kompressorn. I 69 punkter uppskattas det relativa fel till mindre än tio procent. CFD:nger bättre resultat, särskilt vid låga varvtal på rotorn. Vid höga varvtal är noggrannheten mellan CFD och endimensionella modeller likvärdigt. Uppskattningarna från CFD och endimensionella modellerskiljer sig mest i diffusorn och voluten. Avslutningsvis diskuteras styrkor, svagheter och möjliga framtida förbättringar i rotorförlustmodelleringen.
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Cahill, Joseph E. "Identification and Evaluation of Loss and Deviation Models for use in Transonic Compressor Stage Performance Prediction." Thesis, Virginia Tech, 1997. http://hdl.handle.net/10919/37041.

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The correlation of cascade experimental data is one method for obtaining compressor stage characteristics. These correlations specify pressure loss and flow turning caused by the blades. Current open literature correlations used in streamline curvature codes are inadequate for general application to high-speed transonic axial-flow compressors. The objective of this research was to investigate and evaluate the available correlations and ultimately discover sets of correlations which best fit the empirical data to be used in streamline curvature codes. Correlations were evaluated against experimental data from NASA Rotor 1-B and NASA Stage 35. It was found that no universal set of correlations was valid for minimum-loss point predictions. The Bloch shock loss model showed promising results in the stall regime for supersonic relative inlet Mach numbers. The Hearsey and Creveling off-minimum-loss deviation angle prediction performed consistently better than all other correlations tested.
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Van, Acker Rene C. "Multiple-weed species interference in broadleaved crops : evaluation of yield loss prediction and competition models." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308562.

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Books on the topic "Path Loss Prediction Models"

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Murray, Michael P. Middle-term loss prediction models for the Air Force's enlisted force management system: Information for updating. Santa Monica, CA: Rand, 1989.

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Kidner, David B. Digital terrain models for radio path loss calculations. 1991.

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M, Carter Grace, United States Air Force, Project Air Force (Rand Corporation), and Rand Corporation, eds. Middle-term loss prediction models for the Air Force's enlisted force management system: Specification and estimation. Santa Monica, CA: Rand, 1987.

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Book chapters on the topic "Path Loss Prediction Models"

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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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Abolade, Robert O., Dare J. Akintade, Segun I. Popoola, Folasade A. Semire, Aderemi A. Atayero, and Sanjay Misra. "Calibration of Empirical Models for Path Loss Prediction in Urban Environment." In Computational Science and Its Applications – ICCSA 2020, 301–10. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58817-5_23.

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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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Lihan, Marc, Takeshi Tsuchiya, and Keiichi Koyanagi. "Orientation-Aware Indoor Localization Path Loss Prediction Model for Wireless Sensor Networks." In Network-Based Information Systems, 169–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-85693-1_19.

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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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Hamid, Masoud, and Ivica Kostanic. "Path Loss Prediction in Relay Station Environment." In Transactions on Engineering Technologies, 403–16. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-017-9115-1_31.

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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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Popoola, Segun I., Nasir Faruk, N. T. Surajudeen-Bakinde, Aderemi A. Atayero, and Sanjay Misra. "Artificial Neural Network Model for Path Loss Predictions in the VHF Band." In Conference Proceedings of ICDLAIR2019, 161–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67187-7_18.

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Ogunnaike, Ruth M., and Dong Si. "Prediction of Insurance Claim Severity Loss Using Regression Models." In Machine Learning and Data Mining in Pattern Recognition, 233–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62416-7_17.

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Conference papers on the topic "Path Loss Prediction Models"

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Isnin, Ismail Fauzi, Martin Tomlinson, Mohammed Zaki Ahmed, and Marcel Ambroze. "1.25 GHz path loss prediction models for multifloored buildings." In SPIE Defense, Security, and Sensing, edited by Sohail A. Dianat and Michael D. Zoltowski. SPIE, 2009. http://dx.doi.org/10.1117/12.817738.

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Nadir, Z., and H. Al Lawati. "LTE path-loss prediction models' comparative study for outdoor wireless communications." In 7th Brunei International Conference on Engineering and Technology 2018 (BICET 2018). Institution of Engineering and Technology, 2018. http://dx.doi.org/10.1049/cp.2018.1499.

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Popescu, Ileana, Dimitris Nikitopoulos, Philip Constantinou, and Ioan Nafornita. "Comparison of ANN Based Models for Path Loss Prediction in Indoor Environment." In IEEE Vehicular Technology Conference. IEEE, 2006. http://dx.doi.org/10.1109/vtcf.2006.43.

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Corre, Yoann, Julien Stephan, and Yves Lostanlen. "Indoor-to-outdoor path-loss models for femtocell predictions." In 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2011). IEEE, 2011. http://dx.doi.org/10.1109/pimrc.2011.6140082.

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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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Lavanya, Undela, Sowjanya Mupparaju, Padmavathi Patnala, Prathyeka Reddy Anugu, and Simi Surendran. "Model Selection for Path Loss Prediction in Wireless Networks." In 2020 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2020. http://dx.doi.org/10.1109/iccsp48568.2020.9182186.

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Onipe, John Asuva, Caroline Omoanatse Alenoghena, Nathaniel Salawu, and Eberechukwu Numan Paulson. "Optimal Propagation Models for Path-loss Prediction in a Mountainous Environment at 2100MHz." In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS). IEEE, 2020. http://dx.doi.org/10.1109/icmcecs47690.2020.246994.

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Ekiz, Erdem, and Radosveta Sokullu. "Comparison of path loss prediction models and field measurements for cellular networks in Turkey." In Wireless Networking (iCOST). IEEE, 2011. http://dx.doi.org/10.1109/icost.2011.6085834.

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Phaiboon, S., and P. Phokharatkul. "Comparison between Xia and Walfisch-Ikegami models for low-rise building path loss prediction." In 2010 IEEE Region 10 Conference (TENCON 2010). IEEE, 2010. http://dx.doi.org/10.1109/tencon.2010.5686558.

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Vergos, George, Sotirios P. Sotiroudis, Georgia Athanasiadou, George V. Tsoulos, and Sotirios K. Goudos. "Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction." In 2021 10th International Conference on Modern Circuits and Systems Technologies (MOCAST). IEEE, 2021. http://dx.doi.org/10.1109/mocast52088.2021.9493374.

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