Propagation Profile and Signal Strength Variation of VHF Signal in Ekiti State Nigeria

Oluwafemi, Ilesanmi B.; Femi-Jemilohun, Oladunni Juliet

doi:10.5815/ijwmt.2017.03.02

Cited by 4 publications

(5 citation statements)

References 2 publications

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“…The high variations observed in all the measured field strength signals can be ascribed to stochastic multipath nature of the propagation channels and diverse radio propagation mechanisms that impact the signal strength at the receiver en-route the transmitter, and verse versa. Similar reports on varied propagated signals is also contained in [22,23], but for television signals at 75.25 MHz and 631.25MHz, respectively.…”

Section: Resultssupporting

confidence: 61%

Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band

Ojuh¹,

Isabona²

2021

IJISA

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Propagated electromagnetic signal over the cellular radio communication channels and interfaces are usually highly stochastic and complex with unequal noise variation pattern. This is due to multipath nature of the propagation channels and diverse radio propagation mechanisms that impact the signal strength at the receiver en-route the transmitter, and verse versa. This also makes measurement, predictive modeling and estimation based analysis of such signal very challenging and complex as well. One important and popular parametric modelling and estimation technique in mathematics and engineering science, especially for signal processing applications is the least square regression (LSR). The dominance use and popularity of the LSR approach may be attributed to its simplified supporting theory, relatively fast application procedure and ubiquitous application packages. However, LSR is known to be very sensitive to outliers and unusual stochastic signal data. In this work, we propose the application of weighted least square regression method for enhanced propagation practical field strength estimation modelling over cellular radio communication networks interface. The signal data was collected from a commercial LTE networks service provider. Also, we provide statistical computational analyses to compare the resultant estimation outcome of the weighted least square and the standard least approach. From the result, it is found that the WLSR approach is reliably better the most popular standard least square method. The significance and academic of value of this paper is that our proposed and implemented WLSR method can used as replacement of the standard LSR approach for robust mobile signal processing of future communication system networks.

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Section: Resultssupporting

confidence: 61%

Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band

Ojuh¹,

Isabona²

2021

IJISA

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“…To code and implement the Levenberg-Marquardt and Gauss-Newton algorithms for the propagation loss approximation modelling, we explore the MATLAB 2018a software platform. Similar to the works by [2,14,19,26], engaged different performance evaluation indicators such as RMSE, MSE, MAE and MABE to ascertain the level of the proposed algorithm. Shown in tables 1 are the Log-distance propagation path loss models and their parameters gotten using Levenberg-Marquardt algorithms and the bench-marking Gauss-Newton algorithms.…”

Section: Results and Solutionmentioning

confidence: 97%

Application of Levenberg-Marguardt Algorithm for Prime Radio Propagation Wave Attenuation Modelling in Typical Urban, Suburban and Rural Terrains

Isabona¹,

Ojuh²

2021

IJISA

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The desire to achieve an adaptive prognostics regression learning processes of physical and empirical phenomenon is a complex task and open problem in radio frequency telecommunication engineering. One key method to solving such complex task or problems is by means of numerical based optimisation algorithms. The Levenberg–Marquardt algorithm (LMA) is an efficient nonlinear parametric machine learning based modelling algorithm with optimal, fast, and accurate convergence speed. This paper proposes and demonstrates the real-time application of the LMA in developing a log-distance like propagation loss model based on received radio strength measurements conducted over deployed long term evolution (LTE) eNodeBs antennas in three different propagation areas. The LTE eNodeB signal propagation areas were selected to reflect typical urban, suburban and rural terrains which represent urban, suburban and rural terrains. The heights of the three eNodeBs are 30, 28 and 32m respectively and each operate at 2.6GHz carrier frequency with 10MHz channel bandwidths. The resultant outcome of the proposed propagation loss modelling using LMA indicates a high approximation efficacy over the popular Gauss-Newton algorithm (GNA) modelling method, which has been used to benchmark the process. Precisely, the developed propagation loss model using LMA method attained lower maximum absolute error (MABE) of 7.73, 14.57and 10.53 for urban, suburban and rural terrains compared to the ones developed by GNA which yielded 15.19, 16.59 and 13.05 MABE values. The improved approximation performance of the LMA over the GNA can be ascribed to its capacity handle multiple free parameters and attain optimum solution irrespective of the selected values of initial guess parameters.

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“…Coverage areas were employed to study the variability of electric field strength of FM radio [6], VHF/UHF television [7] transmitters in Niger State and MW radio transmitter in Kaduna State [8]. On the other hand, propagation profiles were utilized to describe the variation in the signal strength of UHF [1] in Ondo State and VHF [9] in Ekiti State.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Relationship between Coverage Area and Propagation Profile

Adewale¹

2019

IJWMT

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This study investigates the relationship between coverage area and propagation profile. Most studies on propagation profile do not link the profile with the coverage area of the transmitter and vice versa. Using both coverage area and propagation profile to describe the variations of electric field intensity of propagating wave makes a vivid description of the transmitter performance. This study creates propagation profile from the contour of coverage area of Benue Radio transmitter. Data for the study were collected during a measurement campaign carried out around the transmitter. The data were interpolated to produce a contour of the coverage area. A cross sectional profile elicits from the contour of coverage area at equally spaced eight different vertical plains provides the propagation profiles of the transmitter. This was achieved by transforming the coordinates of the contour of coverage area into distance thus producing a two dimensional plain from a three dimensional contour. Values of coordinates and electric field intensity were taken from some points on the contour and were compared with the equivalent positions on the propagation profile yields the same value of electric field intensity and equivalent position in distance. Similar procedure was carried out on the propagation profile provides the same value of electric field intensity and equivalent positions in terms of coordinates. The study concludes that propagation profiles are cross sectional parts of coverage area.

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Propagation Profile and Signal Strength Variation of VHF Signal in Ekiti State Nigeria

Cited by 4 publications

References 2 publications

Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band

Field Electromagnetic Strength Variability Measurement and Adaptive Prognostic Approximation with Weighed Least Regression Approach in the Ultra-high Radio Frequency Band

Application of Levenberg-Marguardt Algorithm for Prime Radio Propagation Wave Attenuation Modelling in Typical Urban, Suburban and Rural Terrains

Investigating the Relationship between Coverage Area and Propagation Profile

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