Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model

Chimaobi, Nnadi Nathaniel; Nnadi, Charles Chukwuemeka; Nzegwu, Amaechi Justice

doi:10.11648/j.ajsea.20170603.11

Cited by 3 publications

(1 citation statement)

References 11 publications

(13 reference statements)

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“…The authors realized up to 40% root mean square error reduction in the targeted urban environment. Nathaniel [2][3][4][5], Castro et al [6], Castro-Hernandez et al [7] employed the ordinary least square method to estimate and tune the Hata, Erceg and COST-231 model offset parameters to fit in their acquired measured loss data using a representative urban environment.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementation of Quasi-Newton Method Based on BFGS Algorithm for Identification and Optimization of Signal Propagation Loss Model Parameters

Isabona,

Ituabhor,

Akinwumi

et al. 2023

JAIT

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Reliable and precise predictive modelling of signal losses along the communications paths and channels of propagated radio frequency waves is fundamental to the proper design, modelling, operation, and management of mobile broadband cellular networks. As such, the identification and tuning-based estimation of the signal propagation loss parameters has advanced into a recurrent task in the field of radio frequency and telecommunication engineering. Amongst the critical challenges known with identification and predictive estimation signal propagation loss parameters, the generic model-empirical data tuning approach is very vital, yet a most often disregarded and tough optimization problem. Here, a robust and fast computation capacity of Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm Quasi-Newton (QN) method based on the BFGS algorithm is presented for precise identification and optimization of generic log-distance propagation loss model parameters. The proposed QN based BFGS algorithm has been implemented for prognostic analysis of three sets of real-time signal propagation loss data obtained over a Long Term Evolution (LTE) mobile broadband network. When compared with the most popular Levenberg-Marquardt (LM), QN, and Gradient Descent (GD) methods, the proposed method achieved the 30-46% precision accuracies over other methods using three different statistical indicators, particularly in two study locations. The indicators are root mean square error, correlation coefficient and mean absolute error. The awesome precision performance of the proposed method can be explored to overcome premature convergence and poor predictive fitting issues often experienced in the identification and tuningbased estimation of the signal propagation loss parameters during or after cellular network planning processes.

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Section: Literature Reviewmentioning

confidence: 99%

Implementation of Quasi-Newton Method Based on BFGS Algorithm for Identification and Optimization of Signal Propagation Loss Model Parameters

Isabona,

Ituabhor,

Akinwumi

et al. 2023

JAIT

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Terrain-based adaption of propagation model loss parameters using non-linear square regression

Isabona

Imoize

2021

J. Eng. Appl. Sci.

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Reliable and real-time propagation loss modeling play a significant role in the efficient planning, development, and optimization of macrocellular communication networks in a given terrain. Thus, the need to adapt or tune an existing model to enhance its signal prediction accuracy in a specified terrain becomes imperative. In this paper, we proposed and applied a non-linear square regression method based on the Levenberg-Marquart (LM) algorithm to adapt and improve the empirical propagation loss estimation accuracy of the Egli model for two major cities in Nigeria. A comprehensive propagation loss measurement acquired over Long Term Evolution (LTE) mobile broadband networks operating at 2630 MHz for four different cities was collected using TEMS investigation tools to achieve the Egli model adaption. Results indicate that the adapted Egli model displays a high estimation accuracy over the Gauss-Newton (GN) algorithm leveraging the non-linear regression method employed to benchmark the propagation loss estimation. Using six standard statistical indicators, the adapted Egli model displayed lower estimation errors than the classical Egli model across the tested locations in the two cities investigated. Finally, the LM-adapted Egli model was compared with extensive measurements from another eNodeB in Port Harcourt different from the initial four eNodeBs investigated. The results indicate that the adapted model is suitable for deployment in related macrocellular environments.

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Adaptation of Propagation Model Parameters toward Efficient Cellular Network Planning using Robust LAD Algorithm

Isabona¹,

Ojuh²

2020

IJWMT

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All new mobile radio communication systems undergo a cautious cellular network planning and re-planning process in order to resourcefully utilize the allotted frequency band and also ensure that the geographical area of focus is adequately fortified with integrated base stations transmitters. To this end, efficient radio propagation model prediction and tuning is of huge importance, as it assists radio network engineers to effectively assess and plan the cellular network signal coverage area. In this research work, an adaptive least absolute deviation approach is proposed and verified to fine-tune the parameters of Ericsson propagation model. The adaptive tuning technique have been verified experimentally with field propagation loss data acquired over three different suburban locations of a recently deployed LTE radio cellular network in Waterlines area of Port Harcourt City. In terms of the mean absolute percentage error and coefficient of efficiency, the outcomes of the proposed adaptive tuning approach show a higher degree of prediction performance accuracy on the measured loss data compared to the commonly applied least squares regression tuning technique.

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Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model

Cited by 3 publications

References 11 publications

Implementation of Quasi-Newton Method Based on BFGS Algorithm for Identification and Optimization of Signal Propagation Loss Model Parameters

Implementation of Quasi-Newton Method Based on BFGS Algorithm for Identification and Optimization of Signal Propagation Loss Model Parameters

Terrain-based adaption of propagation model loss parameters using non-linear square regression

Adaptation of Propagation Model Parameters toward Efficient Cellular Network Planning using Robust LAD Algorithm

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