Prediction of radiowave propagation for wireless cellular networks in Jordan

Salameh, M. S. H. Al; Al-Zu'bi, Muneer M.

doi:10.1109/kst.2015.7051452

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…In the case of the LOS, the ground-reflected ray in this environment is negligible because the forest ground is covered with full grass and/or weed that absorbs the radio waves. However, in the case of the NLOS, the maximum attenuation is calculated and combined with the LOS, as shown in Equation (33) [ 31 ]:

where

is the maximum excess attenuation in dB ; R is the initial slope of the attenuation curve in dB /m; d is the distance between the transmitter and the receiver in km; and f is the frequency carrier in MHz.…”

Section: Resultsmentioning

confidence: 99%

Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression

Phaiboon

Phokharatkul

2023

Sensors

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Path loss models are essential tools for estimating expected large-scale signal fading in a specific propagation environment during wireless sensor network (WSN) design and optimization. However, variations in the environment may result in prediction errors due to uncertainty caused by vegetation growth, random obstruction or climate change. This study explores the capability of multi-boundary fuzzy linear regression (MBFLR) to establish uncertainty relationships between related variables for path loss predictions of WSN in agricultural farming. Measurement campaigns along various routes in an agricultural area are conducted to obtain terrain profile data and path losses of radio signals transmitted at 433 MHz. Proposed models are fitted using measured data with “initial membership level” (μAI). The boundaries are extended to cover the uncertainty of the received signal strength indicator (RSSI) and distance relationship. The uncertainty not captured in normal measurement datasets between transmitter and receiving nodes (e.g., tall grass, weed, and moving humans and/or animals) may cause low-quality signal or disconnectivity. The results show the possibility of RSSI data in MBFLR supported at an μAI of 0.4 with root mean square error (RMSE) of 0.8, 1.2, and 2.6 for short grass, tall grass, and people motion, respectively. Breakpoint optimization helps provide prediction accuracy when uncertainty occurs. The proposed model determines the suitable coverage for acceptable signal quality in all environmental situations.

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Section: Resultsmentioning

confidence: 99%

Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression

Phaiboon

Phokharatkul

2023

Sensors

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“…In order to analyze the accuracy of Lee propagation model, and its usability in design, planning and management of wireless network in urban environments, especially for the city of Zrenjanin, the MSE (Mean Square Error) and RMSE (Root Mean Square Error) are calculated (Al Salameh, 2015;Chrysikos, Georgopoulos, Kotsopoulos, & Zevgolis, 2010). The MSE and RMSE are given in …”

Section: Results Analysesmentioning

confidence: 99%

Analyses and optimization of Lee propagation model for LoRa 868 MHz network deployments in urban areas

Dobrilovic¹,

Malić²,

Malic³

et al. 2017

JEMC

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“…Assuming that BS-A transmits a radio signal to BS-B that is separated by distance d, the received signal strength (RSS) in (dBm) at the receiving antenna of BS-B can be expressed as [20],…”

Section: A Path Loss and Link Budgetmentioning

confidence: 99%

End-to-End Modelling and Simulation of NLOS Sub-6 GHz Backhaul via Diffraction for Internet Connectivity of Rural Areas

Al-Zubi,

Alouini

2023

IEEE Open J. Commun. Soc.

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Many rural and remote areas around the world still lack access to the Internet due to the high deployment and maintenance costs, the lack of infrastructure, and the low income compared to urban regions. Therefore, cost-effective wireless backhaul links are required to connect these areas to the Internet core network. A wireless non-line-of-sight (NLOS) backhaul via diffraction in rural areas is a promising low-cost solution alternative to the traditional LOS backhaul. A reliable and cost-effective NLOS backhaul can be achieved if accurate propagation modelling and performance evaluation are performed before deployment. In this work, we first examine the accuracy of various terrain-based propagation models in predicting diffraction in different rural sites at unlicensed 5.8 GHz band. Although the classic irregular terrain model (ITM) is widely implemented in many radio frequency (RF) design software, we find that it overestimates the actual diffraction loss over small diffraction angles with an average error of -24 dB and RMSE value of 23 dB. However, a less complex rounded-obstacle diffraction model shows very high prediction accuracy compared to measured data with a negligible average error of -0.01 dB and RMSE value of 2.7 dB. Further, we evaluate the feasibility of an end-to-end NLOS backhaul link via simulation based on the IEEE 802.11ac standard using the practical characteristics of commercial RF equipment. The link performance is evaluated via endto-end simulation in terms of the achieved throughput and the packet error rate (PER) over various antenna heights, modulation, and coding (MCS) schemes, and channel bandwidths. The results show that an NLOS backhaul at a distance of 11 km is viable with a throughput of 100-175 Mbps and PER of 0-0.1.

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Prediction of radiowave propagation for wireless cellular networks in Jordan

Cited by 9 publications

References 17 publications

Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression

Multi-Boundary Empirical Path Loss Model for 433 MHz WSN in Agriculture Areas Using Fuzzy Linear Regression

Analyses and optimization of Lee propagation model for LoRa 868 MHz network deployments in urban areas

End-to-End Modelling and Simulation of NLOS Sub-6 GHz Backhaul via Diffraction for Internet Connectivity of Rural Areas

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