An Indoor Path Loss Prediction Model Using Wall Correction Factors for Wireless Local Area Network and 5G Indoor Networks

Obeidat, H. A.; Asif, Rameez; Ali, Nazar; Dama, Y. A. S.; Obeidat, Omar; Jones, S. M. R.; Shuaieb, Wafa; Al-Sadoon, Mohammed A. G.; Hameed, Khalid; Alabdullah, Ali A. S.; Abd-Alhameed, R. A.

doi:10.1002/2018rs006536

Cited by 32 publications

(15 citation statements)

References 29 publications

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“…at is to say, the results of the 23rd and 27th sessions are used to predict the results of the 28th session, the 2400th session to predict the results of the 29th session, and the 25th session of the 29th session to predict the results of the 30th session so as to form the rotation training and repeat it until the full precision requirement of prediction is fulfilled [34,35]. And the achievement that satisfies the accuracy, namely, the forecast result that wants to obtain for the forecast is shown in Figure 1.…”

Section: Information Forward Propagation Processmentioning

confidence: 99%

Research on Sports Performance Prediction Based on BP Neural Network

Yang

Luo

Tan

2021

Mobile Information Systems

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Artificial neural network has the advantages of self-training and fault tolerance, while BP neural network has simple learning algorithms and powerful learning capabilities. The BP neural network algorithm has been widely used in practice. This paper conducts research on sports performance prediction based on 5G and artificial neural network algorithms. This paper uses the BP neural network algorithm as a pretest modelling method to predict the results of the 30th Olympic Men’s 100m Track and Field Championships and is supported by the MATLAB neural network toolbox. According to the experimental results, the scheme proposed in this paper has better performance than the other prediction strategies. In order to explore the feasibility and application of the BP neural network in this kind of prediction, there is a lot of work to be done. The model has a high prediction accuracy and provides a new method for the prediction of sports performance. The results show that the BP neural network algorithm can be used to predict sports performance, with high prediction accuracy and strong generalization ability.

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Section: Information Forward Propagation Processmentioning

confidence: 99%

Research on Sports Performance Prediction Based on BP Neural Network

Yang

Luo

Tan

2021

Mobile Information Systems

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“…For example, a cement wall, which is the most popular type of wall, shows significant differences in transmission and reflection properties for the 2.4 and 5.2 GHz frequencies [ 27 ]. Considering the influence of walls, Obeidat et al [ 28 ] presented the following effective wall loss model, which is consistent with practical indoor environments:

where

is the path-loss exponent, and

is the attenuation caused by the v -th wall in the propagation path. Focusing on the fact that the wall attenuation differs for each frequency band, the NLOS mode can be estimated.…”

Section: Preliminarymentioning

confidence: 77%

“…In [ 27 ], the propagation loss for a common building material in the 2.4 and 5.2 GHz bands was analyzed. In [ 28 ], the effects of walls on the propagation loss in complex indoor environments were analyzed, and an effective wall loss model was proposed.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi

Lee

Park

Kim

et al. 2021

Sensors

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In this paper, we propose a hybrid localization algorithm to boost the accuracy of range-based localization by improving the ranging accuracy under indoor non-line-of-sight (NLOS) conditions. We replaced the ranging part of the rule-based localization method with a deep regression model that uses data-driven learning with dual-band received signal strength (RSS). The ranging error caused by the NLOS conditions was effectively reduced by using the deep regression method. As a consequence, the positioning error could be reduced under NLOS conditions. The performance of the proposed method was verified through a ray-tracing-based simulation for indoor spaces. The proposed scheme showed a reduction in the positioning error of at least 22.3% in terms of the median root mean square error compared to the existing methods. In addition, we verified that the proposed method was robust to changes in the indoor structure.

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“…The path loss between the receiver and the transmitter can be calculated using deterministic models such as Free Space Path Loss (FSPL) [14], the One Slope Model (OSM) [15] or empirical models [16]. Furthermore, the Motley Keenan Model [17] can be used to evaluate the effects of the walls on the attenuation of signal strength. In our study, we applied the FSPL model to construct the fitness function of the genetic algorithm and simulated annealing.…”

Section: Software Architecturementioning

confidence: 99%

Optimizasyon Teknikleri Kullanılarak Kapalı Alan 802.11ac Kablosuz WLAN Modelleme için Bir Karar Destek Aracı

Ersoy¹,

Yi̇ği̇t²,

Yüksel³

2020

El-Cezeri Fen Ve Mühendislik Dergisi

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Wireless Local Area Network (WLAN) is a technology that provides wireless broadband Internet access in indoor or outdoor environments. It is low cost, easy to use and widely deployed. However, there are many factors that affect its coverage due to the complex structure of indoor settings. For this reason, it is necessary to locate the Access Points (APs) in such a way as to provide the wireless network with the strongest and most consistent coverage. Since these factors directly affect WLAN performance, network specialists often encounter unexpected problems and tend to optimize network coverage by trial and error, taking measurements from access points after they're located. Several metrics such as antenna type, channels, wall structures, throughput can be used for WLAN optimization. Our decision support tool helps network specialists generate a network model by employing these metrics. Determining the coverage of APs in indoor settings is a nonlinear problem. Therefore, our tool is based on the heuristic approaches of the simulated annealing and genetic algorithm. Via this tool, the optimum number of APs to be placed in an indoor setting can be decided and the best AP placement for optimal network coverage can be easily obtained.

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An Indoor Path Loss Prediction Model Using Wall Correction Factors for Wireless Local Area Network and 5G Indoor Networks

Cited by 32 publications

References 29 publications

Research on Sports Performance Prediction Based on BP Neural Network

Research on Sports Performance Prediction Based on BP Neural Network

Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi

Optimizasyon Teknikleri Kullanılarak Kapalı Alan 802.11ac Kablosuz WLAN Modelleme için Bir Karar Destek Aracı

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