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![presents the average signal loss measurement for radio path obstructed by different building materials [10]](profile/Francis-Idachaba/publication/316787365/figure/tbl1/AS:614173167874059@1523441702396/presents-the-average-signal-loss-measurement-for-radio-path-obstructed-by-different.png)
presents the average signal loss measurement for radio path obstructed by different building materials [10]
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The advancement in wireless technology has followed diverse evolutionary path all aiming at achieving a better performance and efficiency in mobile environment such as voice, data, file sharing, video and much more. The deployment of wireless network over the years has been on the increase due to continuous improvement in IEEE 802.11 standards. Thi...
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... the introduction of wall partitions will introduce additional path loss to the signal shown in Tables 1 and 2. From the diagram in figure 2, this path loss will result in a sharper decline in the signal strength such that the same receiver at the location after wall 3 will not be able to receive the signal which it was able to receive with the free space transmission. ...
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Citations
... One typical approach to estimate the losses between the transmitter and the receiver, i.e. the attenuation that the power of the transmitted electromagnetic waves is to undergo in different propagation scenarios is through the establishment of propagation models, or path loss models, which in most cases have an experimental basis [18][19][20]. Although propagation models are aimed to simplify the computation of the losses in certain conditions, it is to be remarked that such models are of limited application as they should only be used in scenarios that are similar to the ones from where they were derived and tested. ...
... In this sense, there are plenty of specific path loss models adapted to as much propagation scenarios as it can be imagined. For instance, [18] assess typical models in both urban and rural environments; [20] adapts some models to indoor propagation environments; [25] reviews propagation models adapted to high altitude mountainous areas for 2.6 GHz propagation; in [26,27] they adapt propagation models to forestry environments; [28] study propagation models in coastal environments and; [29] studies path loss in UAV air-terrestrial links for farming purposes. Moreover, there has been much research in the effects of many direct and indirect phenomena in propagation models. ...
Cellular wireless networks have taken a preponderant role in modern society. With the emergence of 5G and 6G connections, the potential that they may unleash could transform the face in which mankind and machines work together. However, current 5G links are still scarce compared with the total amount of cellular users worldwide, and 6G is still in development phase. In this sense, 2G–4G links still dominate the market, with large physical infrastructures bearing transmissions ranging from 800 to 2,000 MHz. Thus, it is still important to provide reliable link budgets within such a frequency range in order to guarantee stability and quality of service. Despite there are many software-based calculators that provide a tool for link budgeting of cellular connections, they may be cumbersome to use, they could be of payment, they do not necessarily pose the used models as well as their range of validity, among other issues. The present work consists of the design and implementation of a calculation software tool for the construction of the link budget based on radio communications. The tool aims to offer ease of use, flexibility, accuracy, and accessibility in the area of communication systems, to obtain reliable and adequate link budget parameters, prior to the construction and commissioning of the real communications system. The software contains calculation options such as: conversion and display of basic measurement units for radio frequency links, Link Budget calculation, free space loss calculation applied to open environments, simulation and calculation of parameters for the design of communication systems, simulation of statistical models of wave propagation, among others. The software has a web-based friendly-user interface which can be used in any device and under any operating system, is modular and use generic processes, so it does not depend on specific transmission equipment.
... Since WiFi technology represents the typical choice for indoor applications [6], we see adequate research in the literature focusing on the PL problem. For instance, [6][7][8][9] proposed different mathematical models to approximate the signal's power loss as a function of the propagation distance of that signal under various parameters. Recently, attention has been drawn toward WiFi-based long-range systems to treat the issues mentioned above in the introduction. ...
Fixed Wireless Access (FWA) networks made substantial progress in providing
reliable wireless services for users everywhere. To the best of our knowledge, this is the first attempt in Libya to study and assess the performance of a realistic local FWA network operating on an unlicensed 5 GHz band despite the widespread networks of this type throughout the country. This paper provides a detailed analysis of several factors and measurements that affect network performance. The study depends on measurements extracted from a real dataset of an active FWA network. The central performance metric we consider is the capacity offered by the network to the subscribers. Using modern and powerful Python libraries, we present statistical summaries for those factors to estimate the impact of each one individually on the performance. Finally, we utilized the well-known free space Path Loss (PL) model behavior to demonstrate and validate the accuracy of our measurements.
... Radio signal behaviour in the indoor environment is difficult to determine due to the complexity of the case study represented with building structure and the distribution of the presented furniture in the environment [14]. One of these models is the deterministic models that employ Maxwell's equations to compute the characteristics of radio signal propagation [15,16]. It is adopted to predict the coverage area in the adopted indoor environment based on Ray Tracing (RT) method. ...
... while equation (16) will guarantee that APs number will equal to M: AP M AP NN (15) According to the equations above, it can be deduced that each TP in the optimum network receives the highest SIR from j-th AP with signal strength higher or equal to Pth. Moreover, the APs overlapping can be obtained from the relation between the variance of the received power from the neighbor APs and the maximum received power from the paranted AP as calculating by equations (16) and (17). ...
Indoor networks became the focus of attention of many researchers due to its important role to connect to the wide networks. Many algorithms have been applied or proposed to maximize the coverage of indoor networks. In this paper, a multi-objective algorithm has been introduced to optimize the coverage and maximize the Signal-to-Interference Ratio (SIR) based on Binary Particle Swarm Optimization (BPSO) using Matlab software. It has been applied to the installed network which is consist of four AP with a heterogeneous distribution. It has been evaluated the optimized network and proves its reliability. The results obtained show the flexibility and efficiency of the proposed algorithm which produce an optimal network maximizes the coverage area and enhances the SIR by 9.03 dB.
... El-Keyi et al. [20] updated the log-distance PL model for indoor Wi-Fi to take wall penetration, reflection, scattering, and diffraction effects into account. Oni and Idachaba [21] reviewed PL models and their adaptation to Wi-Fi indoor propagation environments. Rademacher et al. [22] conducted outdoor experiments to measure the PL for Wi-Fi links at distances of up to 10.3 km. ...
Great progress has been made in providing convenient wireless communications with easy connectivity for users everywhere. Many empirical path loss (PL) models have been developed to assess the performance of new radio networks. This article first studies the state-of-the-art of empirical PL models, along with vegetation effects on radio signal propagation. Next, an accurate empirical PL model is proposed for fixed wireless networks under challenging rural propagation conditions. The proposed model is based on a Canadian dataset from a wireless internet service provider, using the Wireless-To-The-Home technology in the unlicensed 900 MHz, 2.4 and 5.8 GHz ISM bands and in the licensed 3.65 GHz band. The proposed model considers several parameters, such as line-of-sight obstructions, frequency bands and dynamic link distance splitting, in addition to seasonal variations in PL attenuation. It outperforms other models in terms of accuracy when tested on a dataset from a different Canadian region, and it provides excellent and steady accuracy when tested on a largely different open-access dataset for mobile communication technology from seven different regions in England.
... Where frequency (f) is measured in MHz and distance (d) is measured in km [ [7]. ...
... The value of n depends on the accurate propagation environment. However, reducing the value of n lowers the signal loss, ranging from 1.2 to 8 [ [7]. The average path loss PL(d) for transmitter and receiver separated at distance d is given as ...
... Their results showed that path loss increases with increase in distance. Oni and Idachaba (2017) reviewed different path loss prediction models to investigate the effect of path loss with the goal of minimizing interference. Their results suggested adoption of signal boosters at specific locations in buildings to counter the effect of large path loss introduced by walls. ...
Path loss analysis of Wi-Fi (IEEE 802.11x) signal propagation plays significant role in the plan and function of wireless local area networks (WLAN) applications. Path loss models are crucial in the planning of wireless network as they facilitate survey and mapping of a site for RF installation via simulation; thus, avoiding tedious physical measurements and serve as a guide to a network designer in determining the best location for network devices. Among others, path loss depends upon signal frequency, distance, antenna height and other environmental (medium) characteristics. Frequency and distance are the most important parameters for path loss calculation. This paper develops a modified Wiener II path loss model suitable for assessing Wi-Fi signal propagation through concrete wall. It analyses the effect of distance and frequency on the received signal quality. Empirically, a computer system running the inSSIDer software (a Wi-Fi network scanner application) was used to measure the received signal strength from a Wi-Fi signal source (access point); the measured results were recorded at every 10 m interval between the source (Wi-Fi Access point) and the destination (a personal computer). For the theoretical results, Wiener II model was simulated in Matlab and the result was also recorded. A comparison of both the empirical and theoretical results revealed that the original Wiener II model consistently deviates from the measured results by an average of 1.11%. The Wiener II model was modified by tuning its parameters based on the medium characteristics. The improved Wiener II model estimates the measured path loss with an average deviation of only 0.38% from the measured results.
... Computational efficiency of these models is mostly satisfying but it has problem with accuracy. On the other hand, deterministic models are established on the basis of the principle of physics and can be implemented without affecting their accuracy [4]. However, they require large environmental data which are mostly impractical to obtain, thus its algorithms are mainly complex and lack computational efficiency [5]. ...
In a bid to predict the propagation loss of
electromagnetic signals, different models based on empirical
and deterministic formulas have been used. In this study,
different artificial neural network models which are very
effective for prediction were used for the prediction of signal
power loss in a microcell environment, Obio-Akpor, Port
Harcourt, Nigeria. The signal power loss of the area is studied
based on three artificial neural network algorithms with nine
training functions. For the training of the artificial neural
network, the input data were the distance from the
transmitter and the signal power loss. Training of neural
network is a demanding task in the field of supervised
learning research. This is because the main difficulty in
adopting artificial neural network is in finding the most
suitable combination of learning and training functions for the
prediction task. We compared the performance of three
training algorithms in feedforward back propagation multi
layer perceptron neural network. Nine training functions
under three training algorithms were selected: the Gradient
descent based algorithms, the Conjugate gradient based
algorithms and the Quasi-Newton based algorithms. The work
compared the training algorithms on the basis of mean square
error, mean absolute error, standard deviation, correlation
coefficient, regression on training and validation and the rate
of convergence. The general performance of the training
functions demonstrates their effectiveness to yield accurate
results in short time. The conclusion on the training functions
is based on the simulation results using measurement data
from the micro environment.
