An Empirical Propagation Model for Path Loss Prediction at 2100MHz in a Dense Urban Environment

Ibhaze, Augustus E.; Imoize, Agbotiname Lucky; Ajose, S. O.; John, Samuel; Ndujiuba, Charles Uzoanya; Idachaba, F.E.

doi:10.17485/ijst/2017/v10i5/90654

Cited by 25 publications

(19 citation statements)

References 8 publications

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“…31 For shadowing effects, correction factor of 10.2 dB was applied, considering the nature of the environment investigated and the studies that were earlier carried out in related environments. 32,33 In order to smoothen the data, an unweighed sliding average smoothening algorithm was employed. This helped to preserve the path loss and other shadowing effect characteristics.…”

Section: Measurements Campaignmentioning

confidence: 99%

“…In order to ease the applicability of the models developed from the measured data in related wireless environments, correction factors were appropriately applied 31 . For shadowing effects, correction factor of 10.2 dB was applied, considering the nature of the environment investigated and the studies that were earlier carried out in related environments 32,33 …”

Section: Measurements Campaignmentioning

confidence: 99%

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Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Ojo

Imoize

Alienyi³

2020

Int J Communication

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Summary Path loss prediction models occupy a central role in wireless signal propagation because of the continuous need to achieve reliable and high quality of service for subscribers satisfaction. However, the adoption of deterministic and empirical models for pathloss characterization presents a highly contending trade‐off between simplicity and accuracy. On the one hand, empirical models are relatively simple to apply but are mostly inaccurate and inconsistent. Deterministic models are more accurate but quite complex to develop, time‐consuming, and possess nonadaptable characteristics. Toward this end, this paper proposes to address the problems associated with the existing models (empirical and deterministic) through the introduction of machine learning algorithms to path loss predictions. The contribution of this paper is in threefold. First, experimental data were collected in multitransmitter scenarios via drive test in six base transceiver stations, and the pathloss of the received signal level was derived and analyzed. Two machine learning‐based path loss prediction models were then developed using the measured data as input variables. The developed path loss prediction models are the radial basis function neural network (RBFNN) and the multilayer perception neural network (MLPNN). Further to this, the MLPNN and the RBFNN models were compared with the measured path loss, and the RBFNN appears to be more accurate with lower values of root mean squared errors (RMSEs) in comparison with the MLPNN. Finally, the proposed machine language‐based path loss prediction models (MLPNN and RBFNN) were compared against five existing empirical models, and again, the RBFNN shows the most accurate results.

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Section: Measurements Campaignmentioning

confidence: 99%

Section: Measurements Campaignmentioning

confidence: 99%

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Ojo

Imoize

Alienyi³

2020

Int J Communication

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“…The effortlessness and the obtainability of modified factors has improved usefulness of signal loss prediction at the path. This paper relies on frequency band of 700 and 900 MHz based on the basic equation for propagation prediction in dB [1], [8], [19] ( ) = ( 46.3 + 33.9 10 ( ) − 13.82 10 (ℎ ) − (ℎ ) + (44.9 − 6.55 10 (ℎ )) 10 ( ) + ) (1) The parameter A(hm) is defined for urban environments as. hb: the antenna height for the access point in meters.…”

Section: ) Cost 231 Modelmentioning

confidence: 99%

“…SUI is an extension model of Hata, which was proposed as standards for high frequency bands below 11 GHz cover the channel representations established by Stanford University, specifically stated the SUI models given as; [1], [8], [19] ( ) = + 10…”

Section: ) Stanford University Interim (Sui) Modelmentioning

confidence: 99%

Propagation Prediction of Mobile Networks at Buraidah Region via Different Frequencies and Terrain Profile

Ibrahim¹

2020

International Journal of Engineering Research and Technology

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Received Signal Strength Indicators (RSSI) is one of the main concerns in any wireless communication system deployment. Most of wireless infrastructure providing best connection for costumers as well as minimizing the wasted energy. This paper concentrated on major issues affect the signal disturbers in the area of study using well known empirical models to predict signal behaviours over different trainset profile in suburban area to obtain the best predicted model based on criteria caused by (RSSI). Stanford University Interim (SUI) and extended COST 231-Hata propagation models with actual measured data on different carrier frequencies has been considered for best prediction. The results show that the SUI propagation model gives the best performance according to the collected data in the area of study compared to the extended COST 231-Hata. The significant investigation in this paper confirmed by presenting the average signal in the path loss prediction.

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“…While efforts have been made to solve this problem, available reports (Ajose and Imoize, 2013;Ibhaze et al, 2017;Imoize and Dosunmu, 2018) show that focus is mainly on terrestrial radio waves propagation. It is very difficult to find detailed reports on pathloss measurements and modeling in Lagoon environments.…”

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confidence: 99%

Propagation measurements of a 4G LTE network in Lagoon environment

Imoize¹,

Ogunfuwa²

2019

Nig. J. Technol. Dev.

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Analysis of predicted and measured path loss over a Lagoon environment is presented. Propagation measurements were carried out at 1800 MHz, within a quarter of a year (May to August 2017) using Huawei Technologies drive test equipment. Measured data comprising of the received signal strength was taken for the initial measurements, measurements after first month, measurements after second month, and measurements after the third month. Measured path loss was compared with predictions made by free-space, log-distance, two-ray, COST 231-Hata, and Stanford University Interim (SUI) models. The COST-231 Hata model showed the most accurate performance with root mean square errors (RMSEs) of 10.03 dB, 12.38 dB, 17.59 dB, and 7.67 dB for the initial measurements, measurements after first month, measurements after second month, and measurements after third month, respectively. In order to achieve a more accurate prediction, the COST 231 Hata model was optimized using the least square algorithm. The optimized model showed improved signal prediction with RMSEs of 7.90 dB, 9.28 dB, 14.82 dB and 5.28 dB, respectively. The average RMSEs of the optimized COST 231 Hata model showed 9.32 dB compared with 11.92 dB predicted by the actual COST 231 Hata model. This accounts for about 21.81% improvements over the existing COST 231 Hata model. Therefore, the optimized COST 231-Hata model could be used to characterize radio channels in the investigated environments.

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An Empirical Propagation Model for Path Loss Prediction at 2100MHz in a Dense Urban Environment

Cited by 25 publications

References 8 publications

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Radial basis function neural network path loss prediction model for LTE networks in multitransmitter signal propagation environments

Propagation Prediction of Mobile Networks at Buraidah Region via Different Frequencies and Terrain Profile

Propagation measurements of a 4G LTE network in Lagoon environment

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