Performance Study of Path Loss Models at 14, 18, and 22 GHz in an Indoor Corridor Environment for Wireless Communications
Mohamed K. Elmezughi,Thomas J. Afullo,Nicholas O. Oyie +2 more
- Vol. 112, Iss: 1, pp 32-45
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TLDR
The LOS and NLOS results reveal that the CI and FI models can be trusted as good path loss models for corridor environments and exhibit stable behavior over measured distances and frequencies.Abstract:
The critical rule to achieve extremely high peaks of data transmission is the availability of a tremendous amount of bandwidth. The super high frequency (SHF) and the millimeter-wave (mmWave) frequency bands are the candidates for the deployment of the 5G cellular system and for satisfying future needs due to their massively available blocks of contiguous raw bandwidth that is capable of supporting additional data traffic for multimedia services. This research paper presents propagation measurements at three frequencies above 6 GHz, which are 14, 18, and 22 GHz frequency bands. The measurements were carried out or both line-of-sight (LOS) and non-line-of-sight (NLOS) communication scenarios in an indoor corridor environment to present frequency- and distance-dependent wireless channel models. Moreover, this study presents, investigates, and compares the performance of two well-known path loss prediction models; the single frequency close-in (CI) free space reference distance model and the single frequency floating intercept (FI) model. The LOS comparison study shows that the CI and FI models provide comparable and accurate estimates that fit the real measured data for the frequency bands selected. Furthermore, the study investigates the behavior of the path loss exponent (PLE) and the FI model parameters as a function of the reception angle of arrival (AoA) in the NLOS scenario. It is observed from this work that the path loss models' parameters exhibit symmetrical behavior around 180° AoA. The FI model provides the same standard deviation values as the CI model in the LOS scenario. In contrast, the FI model offers a notable reduction(up to 2.84 dB) compared to the CI model in the NLOS scenario. Finally, the LOS and NLOS results reveal that the CI and FI models can be trusted as good path loss models for corridor environments and exhibit stable behavior over measured distances and frequencies.read more
Citations
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Journal ArticleDOI
An Efficient Approach of Improving Path Loss Models for Future Mobile Networks in Enclosed Indoor Environments
TL;DR: In this article, the authors proposed an efficient improvement of the well-known close-in (CI) free space reference distance model and the floating-intercept (FI) model.
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Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
TL;DR: This paper presents and evaluates the performance of several well-known machine learning methods, including multiple linear regression (MLR), polynomial regression (PR), support vector regression (SVR), as well as the methods using decision trees (DT), random forests (RF), K-nearest neighbors (KNN), artificial Neural networks (ANN), and artificial recurrent neural networks (RNN).
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Path Loss Measurements and Model Analysis in an Indoor Corridor Environment at 28 GHz and 38 GHz
TL;DR: In this article , the authors examined the large-scale path loss models for an indoor corridor environment at frequencies of 28 and 38 GHz and evaluated the performance of the models in terms of the standard deviation, path loss exponent, accuracy, simplicity, and stability.
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Attenuation of millimetre wave radio signal at worst hour rainfall rate in a tropical region: A Case Study, Nigeria
EF Nymphas,Osita Ibe +1 more
TL;DR: In this paper , the attenuation of millimeter wave radio signal at worst hour rainfall rate was investigated and the Lavergnat and Gole model was used for the conversion of the measured 5 minute to equivalent 1-minute values.
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Air-to-Ground Large-Scale Channel Characterization by Ray Tracing
TL;DR: In this article , the authors used ray tracing simulation on three-dimensional (3D) urban environments to characterize air-to-ground (A2G) channels for 5G and beyond wireless communications.
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