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Статті в журналах з теми "PATH-LOSS MODEL"

Автор: Grafiati

Опубліковано: 11 вересня 2023

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1

Rayar, Vijay. "A Novel Indoor Path Loss Model based on Path Loss Exponent (PLE) Computational Approach." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1170–78. http://dx.doi.org/10.5373/jardcs/v12sp7/20202217.

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2

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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3

Bilgehan, Bülent, and Stephen Ojo. "Multiplicative based path loss model." International Journal of Communication Systems 31, no. 17 (August 16, 2018): e3794. http://dx.doi.org/10.1002/dac.3794.

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4

Iwuji, P. C. "PERFORMANCE ANALYSIS AND DEVELOPMENT OF PATH LOSS MODEL FOR TELEVISION SIGNALS IN IMO STATE, NIGERIA." Eurasian Physical Technical Journal 20, no. 2 (44) (June 21, 2023): 87–98. http://dx.doi.org/10.31489/2023no2/87-98.

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Анотація:

It is impossible to overstate the importance of propagation models in wireless network planning, frequency assignment, and television parameter evaluation. The fact that no two locations are identical in terms of climatic conditions, building patterns, terrain, etc. makes using path loss predicting models for any area extremely challenging. Therefore, it is impossible to develop a single path loss model that applies to all environmental settings. The main aim of this study is to develop a path loss model for NTA channel 12 Owerri and evaluate its performance based on received signal strength values along five selected routes in Imo State, Nigeria.A suitable path loss model was developed by critically analyzing the measured path loss values of each base station, which were retrieved from the signal strength data received. The values of the developed path loss model were compared to those of other empirical path loss models developed by other researchers as well as the measured path loss values. The results show that the proposed path loss model is well suited for predicting the path loss of NTA channel 12 Owerri signals in the study environment, while the other conventional empirical models taken into consideration in this study overestimated the path loss of NTA channel 12 Owerri signals with Root Mean Square Error and Mean Error of 63.65 and above. Additionally, the findings indicate that NTA Owerri performs poorly at a distance of 18 kilometers from the base transmitting station. The overall findings are helpful for designing prospective television network channels in the study location and other similar environments.

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5

Lee, Jun-Hyun, Dong-Hyung Lee, Hong-Sik Keum, and Heung-Gyoon Ryu. "Path Loss Model with Multiple-Antenna." Journal of Korean Institute of Electromagnetic Engineering and Science 25, no. 7 (July 31, 2014): 747–56. http://dx.doi.org/10.5515/kjkiees.2014.25.7.747.

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6

Har, D., H. H. Xia, and H. L. Bertoni. "Path-loss prediction model for microcells." IEEE Transactions on Vehicular Technology 48, no. 5 (1999): 1453–62. http://dx.doi.org/10.1109/25.790520.

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7

Suman, Suraj, Sidharth Kumar, and Swades De. "Path Loss Model for UAV-Assisted RFET." IEEE Communications Letters 22, no. 10 (October 2018): 2048–51. http://dx.doi.org/10.1109/lcomm.2018.2863389.

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8

Karttunen, Aki, Mikko Valkama, and Jukka Talvitie. "Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning." Sensors 21, no. 3 (February 2, 2021): 987. http://dx.doi.org/10.3390/s21030987.

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Positioning is considered one of the key features in various novel industry verticals in future radio systems. Since path loss (PL) or received signal strength-based measurements are widely available in the majority of wireless standards, PL-based positioning has an important role among positioning technologies. Conventionally, PL-based positioning has two phases—fitting a PL model to training data and positioning based on the link distance estimates. However, in both phases, the maximum measurable PL is limited by measurement noise. Such immeasurable samples are called censored PL data and such noisy data are commonly neglected in both the model fitting and in the positioning phase. In the case of censored PL, the loss is known to be above a known threshold level and that information can be used in model fitting and in the positioning phase. In this paper, we examine and propose how to use censored PL data in PL model-based positioning. Additionally, we demonstrate with several simulations the potential of the proposed approach for considerable improvements in positioning accuracy (23–57%) and improved robustness against PL model fitting errors.

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9

Seong, Ju-Hyeon, Teak-Gu Gwun, Seung-Hee Lee, Jeong-Woo Kim, and Dong-hoan Seo. "Radio map fingerprint algorithm based on a log-distance path loss model using WiFi and BLE." Journal of the Korean Society of Marine Engineering 40, no. 1 (January 31, 2016): 62–68. http://dx.doi.org/10.5916/jkosme.2016.40.1.62.

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10

Chen, Jie, Dong Ya Shen, Na Yao, and Ren Zhang. "3-D Research about Walfisch-Bertoni Model." Applied Mechanics and Materials 385-386 (August 2013): 1527–30. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1527.

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Walfisch - Bertoni model is used to predict the average signal field intensity of the main street. The model considers the path loss of the free space, diffraction loss along the path, and the influence of the height of the building. There are six City parameters in Walfisch - Bertoni model influence communication quality. In this paper, the researches about path loss and its characteristics is under the case of considering two city parameters at the same time. Facts have proved that this case is more close to the actual that the wireless signal propagation environment. This paper mainly researched the path loss, probability density function (PDF) and cumulative distribution function (CDF) of the path loss.

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11

Nebuloni, Roberto, and Elizabeth Verdugo. "FSO Path Loss Model Based on the Visibility." IEEE Photonics Journal 14, no. 2 (April 2022): 1–9. http://dx.doi.org/10.1109/jphot.2022.3152728.

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12

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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13

Pandey, Suryashree, and Abhinandan Sarkar. "To Develop a Model for 4g LTE and Predictable 5G at 3500 Mhz that Would Predict the Path Loss for the Environment in Semi-Urban or Mixture of Urban and Rural Surroundings at Specific Geographical Locations." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 485–89. http://dx.doi.org/10.22214/ijraset.2022.42001.

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Abstract: In wireless communication, a planned network is given by the path loss model. Link budgeting, coverage prediction, and system performance optimization are indispensable in developing an accurate, simple, and general path loss model. To predict path loss in a particular environment each type of path loss propagation model is designed, it may be inaccurate in another different environment. In this paper, we are trying to predict a path loss model of Durgapur considering a particular place with a mixture of high-rise buildings, sub-urban, open, and foliage environments. For this proposal, the model area has been divided into twelve sectors taking 30° sectoring of radii 5 km and applying the path loss model for the calculation of path loss. Index Terms: Path loss, Predicted model, Outdoor propagation model consideration, Cell site selection, results, and discussions.

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14

Li, Qiang, Hongxin Zhang, Yang Lu, Tianyi Zheng, and Yinghua Lv. "A new method for path-loss modeling." International Journal of Microwave and Wireless Technologies 11, no. 08 (February 22, 2019): 739–46. http://dx.doi.org/10.1017/s1759078719000084.

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AbstractIn this paper, a new path-loss model for electromagnetic wave in an indoor multipath environment is proposed based on matching coefficient, polarization matching factor, and normalized field intensity direction function. This model is called the Friis-extension (Friis-EXT) model, because it operates as the Friis model under certain conditions. In addition, in the modeling process of the path-loss in an indoor environment, the reflective surfaces in the environment and form of the antenna are considered. Afterwards, the path-loss data in an indoor corridor environment are measured, and the maximum error between the theoretical value and the measured data is <7.5 dB. Finally, the Friis-EXT model is compared with some other traditional models, and the results show that the Friis-EXT model is the best one that matches the measurement data.

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15

Wang, Tai Rong, Lu Bai, and Zhen Sen Wu. "Path Loss of Non-Line-of-Sight Single-Scatter Model." Advanced Materials Research 571 (September 2012): 416–20. http://dx.doi.org/10.4028/www.scientific.net/amr.571.416.

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Based on the single-scatter model of the Non-Line-of-Sight(NLOS), we analyze the effect of the geometric parameter of the transceiver on path loss, such as the receiver field of view(FOV), the transmitter apex angle and so on. And path loss in different visibility and weather conditions are simulated. The simulation results show that increasing the receiver FOV, or reducing the transmitter apex angle or the receiver apex angle can reduce path loss. And visibility and weather have an important effect on path loss.

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16

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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17

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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18

Elechi, Promise. "Path Loss Prediction Model for GSM Fixed Wireless Access." European Journal of Engineering and Technology Research 1, no. 1 (July 27, 2018): 1–4. http://dx.doi.org/10.24018/ejeng.2016.1.1.68.

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This research investigated the effect of building materials on GSM signals quality. Measurements were conducted on MTN, Glo, Airtel and Etisalat networks using Radio Frequency Signal Tracker on six different building patterns. The results showed that building with alucoboard wall cladding had the highest signal loss while the sandcrete building/unrusted corrugated iron sheet roof had the least signal loss. Also, a model to predict signal penetration through building walls was developed. It was developed using the principles of Fresnel Refraction Coefficient and the knife-edge diffraction. The total losses from the transmitter to the receiver was modelled as a combination of three different effects; losses due to free-space propagation from transmitter to building; the penetration loss was modelled as a combination of the wall penetration loss and the diffraction loss. The results show that despite the condition of the building walls, movement of people in the environment/room also affected the wireless signal quality as well as the chairs and gadgets in the room. The indoor signal path loss in the rooms increased from when the walls were plastered and continued until when the walls were covered with curtains, both rooms reduced by 4dBm. The mean squared error ranged between 1.6dBm and 2.1dBm with a standard deviation between 11.1 and 11.5.

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19

Lee, Byeong-ho, YoungJoon Kim, Hyoungmin So, and Seong-Cheol Kim. "Efficient Indoor Localization Algorithm Using Multi Path Loss Model." Journal of Korean Institute of Communications and Information Sciences 42, no. 10 (October 31, 2017): 1982–90. http://dx.doi.org/10.7840/kics.2017.42.10.1982.

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20

Sari, Rouhollah, and Hadi Zayyani. "RSS Localization Using Unknown Statistical Path Loss Exponent Model." IEEE Communications Letters 22, no. 9 (September 2018): 1830–33. http://dx.doi.org/10.1109/lcomm.2018.2849963.

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21

Oda, Y., K. Tsunekawa, and M. Hata. "Advanced LOS path-loss model in microcellular mobile communications." IEEE Transactions on Vehicular Technology 49, no. 6 (2000): 2121–25. http://dx.doi.org/10.1109/25.901884.

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22

Tonguz, O. K., A. E. Xhafa, D. D. Stancil, A. G. Cepni, P. V. Nikitin, and D. Brodtkorb. "A Simple Path-Loss Prediction Model for HVAC Systems." IEEE Transactions on Vehicular Technology 53, no. 4 (July 2004): 1203–14. http://dx.doi.org/10.1109/tvt.2004.830143.

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23

Bhuvaneshwari, A., R. Hemalatha, and T. Satyasavithri. "Semi Deterministic Hybrid Model for Path Loss Prediction Improvement." Procedia Computer Science 92 (2016): 336–44. http://dx.doi.org/10.1016/j.procs.2016.07.388.

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24

Kurup, Divya, Wout Joseph, Gnter Vermeeren, and Luc Martens. "In-body Path Loss Model for Homogeneous Human Tissues." IEEE Transactions on Electromagnetic Compatibility 54, no. 3 (June 2012): 556–64. http://dx.doi.org/10.1109/temc.2011.2164803.

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25

Progri, Ilir F. "A Unified Geolocation Channel Model--Part I (Path Loss)." Journal of Geolocation, Geo-information and Geo-intelligence 2017, no. 1 (2017): 31. http://dx.doi.org/10.18610/jg3.2017.071604.

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26

Rahim, Mohammad Abdel, Mohamed Hadi Habaebi, Jalel Chebil, Aisha Hassan A. Hashim, Musse Mohamud Ahmed, Md Rafiqul Islam, and Alhareth Zyoud. "An indoor path loss model for wireless sensor networks." International Journal of Ultra Wideband Communications and Systems 3, no. 4 (2018): 192. http://dx.doi.org/10.1504/ijuwbcs.2018.092427.

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27

Zyoud, Alhareth, Md Rafiqul Islam, Musse Mohamud Ahmed, Aisha Hassan A. Hashim, Jalel Chebil, Mohamed Hadi Habaebi, and Mohammad Abdel Rahim. "An indoor path loss model for wireless sensor networks." International Journal of Ultra Wideband Communications and Systems 3, no. 4 (2018): 192. http://dx.doi.org/10.1504/ijuwbcs.2018.10013837.

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28

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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Анотація:

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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29

Kaya , Abdil, Brecht De Beelde, Wout Joseph, Maarten Weyn, and Rafael Berkvens. "Geodesic Path Model for Indoor Propagation Loss Prediction of Narrowband Channels." Sensors 22, no. 13 (June 29, 2022): 4903. http://dx.doi.org/10.3390/s22134903.

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Анотація:

Indoor path loss models characterize the attenuation of signals between a transmitting and receiving antenna for a certain frequency and type of environment. Their use ranges from network coverage planning to joint communication and sensing applications such as localization and crowd counting. The need for this proposed geodesic path model comes forth from attempts at path loss-based localization on ships, for which the traditional models do not yield satisfactory path loss predictions. In this work, we present a novel pathfinding-based path loss model, requiring only a simple binary floor map and transmitter locations as input. The approximated propagation path is determined using geodesics, which are constrained shortest distances within path-connected spaces. However, finding geodesic paths from one distinct path-connected space to another is done through a systematic process of choosing space connector points and concatenating parts of the geodesic path. We developed an accompanying tool and present its algorithm which automatically extracts model parameters such as the number of wall crossings on the direct path as well as on the geodesic path, path distance, and direction changes on the corners along the propagation path. Moreover, we validate our model against path loss measurements conducted in two distinct indoor environments using DASH-7 sensor networks operating at 868 MHz. The results are then compared to traditional floor-map-based models. Mean absolute errors as low as 4.79 dB and a standard deviation of the model error of 3.63 dB is achieved in a ship environment, almost half the values of the next best traditional model. Improvements in an office environment are more modest with a mean absolute error of 6.16 dB and a standard deviation of 4.55 dB.

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30

M Mohamed, Ibrahim M. "Accurate Path-Loss Estimation for Wireless Cellular Networks." Jurnal Kejuruteraan 33, no. 2 (May 30, 2021): 317–28. http://dx.doi.org/10.17576/jkukm-2021-33(2)-16.

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Анотація:

In addition to its rule in achieving acceptable system performance, accurate path-loss estimation leads to realize a simple and precise estimation of cellular networks financial feasibility. In other words, in cellular networks, while accurate path-loss estimation helps to achieve a reasonable cost, inaccurate path-loss estimation could either lead to reduce the cost but degrade the performance, or improve the performance but increase the cost. Thus accurate path loss estimation becomes a very crucial and desirable goal. To this end, different models were introduced in the literature for achieving the aforementioned goal; however, each model sought to be a valid choice in a specific environment. Therefore, our main objective in this paper was to provide an insight to the cellular network designers to simplify the decision-making process. A number of widely used path-loss estimation models that work in different environments, such as Free-space model, Two-ray model, Okumura/Hata model, COST 231 model, and Indoor model were reviewed and analyzed. This significantly would assist any cellular network designer to select an appropriate model according to its working environment, and thus realize the desirable accurate path-loss estimation. Matlab was used to perform this analysis. The analysis drove us to enter into implicit comparisons between the aforementioned models. Going through these comparisons, it is concluded that although an increase in the level of complexity is encountered when using models with large number of correction factors, more accurate path-loss estimation can be achieved.

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31

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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32

Jo, Han-Shin, Chanshin Park, Eunhyoung Lee, Haing Kun Choi, and Jaedon Park. "Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process." Sensors 20, no. 7 (March 30, 2020): 1927. http://dx.doi.org/10.3390/s20071927.

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Анотація:

Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model.

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33

Jimoh, AKAANNI, ISA Abdurrhaman Ademola, OGUNBIYI Olalekan, OLUFEAGBA Benjamin. Jimmy, and SANNI Tunde Abdulrahman. "AJ-Olu-1: An Innovative Path Loss Model for Typical Nigerian Urban Environments." KIU Journal of Science, Engineering and Technology 2, no. 1 (April 4, 2023): 17–23. http://dx.doi.org/10.59568/kjset-2023-2-1-03.

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Анотація:

The modeling of outdoor path loss propagation is critical in the planning and construction of the Global System for Mobile Communication (GSM) coverage area. For GSM signal prediction at any location inside its service region, a precise forecast based on critical characteristics and a mathematical model is required. Numerous research findings on path loss propagation model forecast for GSM mobile networks conducted in various cities in Nigeria revealed that the COST231-Hata model gives closer prediction to most of the practical measure path loss values. Based on the existing COST-23-Hata path loss model and outdoor measurements at 1800 MHz frequency range within Ilorin metropolis, this paper proposed a suitable path loss model. The developed model was used and validated in various locations throughout Ilorin city with the measured and COST-231 Hata models. The analysis of the results revealed that the developed model performed satisfactorily in terms of the closest path loss prediction to the practical measure path loss values at all study locations. It also has the lowest Square Root Means Error and Standard Deviation (SD) of any Base Station (BTS) tested in Ilorin, Nigeria. As a result, it is concluded that the newly developed AJ-Olu-1 model is more suitable for GSM 1800 network design and installation in Ilorin City, Nigeria, as well as other cities in Nigeria and other cities outside Nigeria with similar environments.

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34

Sirdeshpande, Nandakishor, and Vishwanath Udupi. "Characterization of path loss model for wireless communication channel modelling." Data Technologies and Applications 54, no. 3 (April 27, 2020): 343–64. http://dx.doi.org/10.1108/dta-03-2019-0052.

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PurposeWireless communication channel provides a wide area of applications in the field of communication, distributed sensor network and so on. The prominence of the wireless communication channel is because of its robust nature and the sustainability for the precise ranging and the localization. The precision and accuracy of the wireless communication channel largely depend on the localization. The development of the wireless communication channel with improved benefits needs the accurate channel model.Design/methodology/approachThis paper characterizes the tangential path loss model in the WINNER based wireless communication channel model. The measurements taken in the WINNER channel model are compared with the tangential path loss characterized WINNER Channel model.FindingsThe model operates well over the varying antenna orientations, measurement condition and the propagation condition. The proposed tangential path loss model is performing well over the various outdoor scenarios.Originality/valueThe proposed characterization shows change in the small-scale parameters (SSP), such as power, delay, angle of arrival and angle of departure as well as the large-scale parameters (LSP), such as RMS delay spread, shadowing, path loss and Ricean factor associated with the model.

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35

Samad, Md Abdus, Dong-You Choi, and Kwonhue Choi. "Path loss measurement and modeling of 5G network in emergency indoor stairwell at 3.7 and 28 GHz." PLOS ONE 18, no. 3 (March 28, 2023): e0282781. http://dx.doi.org/10.1371/journal.pone.0282781.

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Research on path loss in indoor stairwells for 5G networks is currently insufficient. However, the study of path loss in indoor staircases is essential for managing network traffic quality under typical and emergency conditions and for localization purpose. This study investigated radio propagation on a staircase where a wall separated the stairs from free space. A horn and an omnidirectional antenna were used to determine path loss. The measured path loss evaluated the close-in-free-space reference distance, alpha-beta model, close-in-free-space reference distance with frequency weighting, and alpha-beta-gamma model. These four models exhibited good compatibility with the measured average path loss. However, comparing the path loss distributions of the projected models revealed that the alpha-beta model exhibited 1.29 dB and 6.48 dB for respectively, at 3.7 GHz and 28 GHz bands. Furthermore, the path loss standard deviations obtained in this study were smaller than those reported in previous studies.

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36

Gareh, Messaoud, Lotfi Djouane, Houcine Oudira, and Nazih Hamdiken. "Path Loss Models Optimization for Mobile Communication in Different Areas." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 1 (June 4, 2016): 126. http://dx.doi.org/10.11591/ijeecs.v3.i1.pp126-135.

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<p class="NormalItalique">In mobile radio systems, path loss models are necessary for proper planning, interference estimations, frequencies assignments and cell parameters which are basic for network planning process. Empirical models are the most adjustable models that can be suited to different types of environments. In this paper, data collected in Batna, Algeria is used to calculate the path loss for GSM (908-957 MHz). The measured path loss is compared with theoretical path loss estimated by the most widely empirical models «Cost123», «Hata», «SUI» and «Egli». The best model to estimate the measured path loss is optimized using genetic algorithm to predict path loss for suburban and rural area. The RMSE and the other test criteria between the actual and predicted data are calculated for various path loss models. It turned out that the adjusted COST 231 model outperforms the other studied models. The investigated results can help telecommunication engineers improve their planning and design of microcellular system.</p>

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37

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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38

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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39

Jiang, Xiang-yuan, Huan-shui Zhang, and Wei Wang. "A Model Selection Algorithm for Path Loss from Incomplete Data." Journal of Electronics & Information Technology 34, no. 6 (August 22, 2012): 1438–44. http://dx.doi.org/10.3724/sp.j.1146.2011.01084.

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40

Myagmardulam, Bilguunmaa, Nakayama Tadachika, Kazuyoshi Takahashi, Ryu Miura, Fumie Ono, Toshinori Kagawa, Lin Shan, and Fumihide Kojima. "Path Loss Prediction Model Development in a Mountainous Forest Environment." IEEE Open Journal of the Communications Society 2 (2021): 2494–501. http://dx.doi.org/10.1109/ojcoms.2021.3122286.

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41

Han, Seon Yeong, Nael B. Abu-Ghazaleh, and Dongman Lee. "Efficient and Consistent Path Loss Model for Mobile Network Simulation." IEEE/ACM Transactions on Networking 24, no. 3 (June 2016): 1774–86. http://dx.doi.org/10.1109/tnet.2015.2431852.

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42

Ghassemzadeh, Saeed S., Larry J. Greenstein, Aleksandar Kavcic, Thorvardur Sveinsson, and Vahid Tarokh. "An empirical indoor path loss model for ultra-wideband channels." Journal of Communications and Networks 5, no. 4 (December 2003): 303–8. http://dx.doi.org/10.1109/jcn.2003.6596612.

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43

Roelens, L., S. Van den Bulcke, W. Joseph, G. Vermeeren, and L. Martens. "Path loss model for wireless narrowband communication above flat phantom." Electronics Letters 42, no. 1 (2006): 10. http://dx.doi.org/10.1049/el:20063062.

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44

El-Sallabi, Hassan. "Terrain Partial Obstruction LOS Path Loss Model for Rural Environments." IEEE Antennas and Wireless Propagation Letters 10 (2011): 151–54. http://dx.doi.org/10.1109/lawp.2011.2108254.

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45

Perez-Vega, Constantino, Jose Luis García G, and José Miguel López Higuera. "A simple and efficient model for indoor path-loss prediction." Measurement Science and Technology 8, no. 10 (October 1, 1997): 1166–73. http://dx.doi.org/10.1088/0957-0233/8/10/020.

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46

KWAK, D. Y. "Enhanced Urban Path Loss Prediction Model with New Correction Factors." IEICE Transactions on Communications E89-B, no. 4 (April 1, 2006): 1459–63. http://dx.doi.org/10.1093/ietcom/e89-b.4.1459.

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47

Lee, Sang-Geol, and Yunsick Sung. "Log-Distance Path Loss Model-Based Relative Distance Estimation Method." Advanced Science Letters 22, no. 9 (September 1, 2016): 2558–61. http://dx.doi.org/10.1166/asl.2016.7841.

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48

Fryziel, M., C. Loyez, L. Clavier, N. Rolland, and P. A. Rolland. "Path-loss model of the 60-GHz indoor radio channel." Microwave and Optical Technology Letters 34, no. 3 (June 20, 2002): 158–62. http://dx.doi.org/10.1002/mop.10402.

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49

Hou, Wenjun, Chuanhui Liu, Faping Lu, Jiafang Kang, Zhongyang Mao, and Bifeng Li. "Non-line-of-sight ultraviolet single-scatter path loss model." Photonic Network Communications 35, no. 2 (October 5, 2017): 251–57. http://dx.doi.org/10.1007/s11107-017-0737-5.

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50

Sudhamani, Chilakala, Mardeni Roslee, Lee Loo Chuan, Athar Waseem, Anwar Faizd Osman, and Mohamad Huzaimy Jusoh. "Performance Analysis of a Millimeter Wave Communication System in Urban Micro, Urban Macro, and Rural Macro Environments." Energies 16, no. 14 (July 14, 2023): 5358. http://dx.doi.org/10.3390/en16145358.

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Анотація:

The signal power in wireless communication systems is influenced by various factors, including the environment. These factors include path differences, operational frequency, and environmental conditions. Consequently, designing a communication system that generates a stronger signal is highly challenging. To address this, large-scale path-loss models are employed to estimate the path loss and signal power across different frequencies, distances, and environments. In this paper, we focused on the urban micro, urban macro, and rural macro environments to estimate path loss and signal power at millimeter wave frequencies. We compared the path loss and received power among different path-loss models developed by standard organizations. Simulation results indicate that the fifth-generation channel model provides enhanced path loss and signal power in urban micro environments, while the third-generation partnership project model performs well in urban macro and rural macro environments when compared to other path-loss models.

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