Path Loss Propagation Model Prediction for GSM Network Planning

Mawjoud, Sami A.

doi:10.5120/14592-2830

Cited by 13 publications

(12 citation statements)

References 8 publications

(3 reference statements)

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“…Fundamentally, radio signal reduction or path loss is usually being predicted using propagation models. As reported in [10], a number of radio wave propagation models are available to predict path loss over different types of terrains. These radio wave propagation models are broadly classified into three classes: deterministic, empirical, and stochastic.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Need for Specific Propagation Model for Specific Environment Based on Different Terrain Characteristics

Popoola

Ponnle

Olasoji

et al. 2018

IIUMEJ

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Owing to their speed of excution as well as their limited reliance on detailed knowledge of the terrain characteristics of the service environments, empirical propagation models have enjoyed general acceptability in the wireless communication research community. However, recent industrial observations show that no single propagation model can best fit all the radio service environments, which led to the hypothesis of specific models for specific environments. In order to scientifically verify this hypothesis, the study presented in this paper investigated the performance of the free space propagation loss (FSPL) model in two different radio environments characterised with different types of obstructions. The investigation was conducted through field strength distribution measurement of two broadcasting radio stations transmitting at 96.5 MHz and 102.3 MHz. The field strength measurement data obtained were analysed. The result of the analysis shows gross disparity between the measured path losses and calculated path losses using FSPL model. The disparity thus necessitates the modification of the FSPL model in order to develop each propagation model for each of the two radio stations employed and their environment. The developed models were then evaluated to ascertain their performances relative to the FSPL model. The performance evaluation results show that the predictions of the developed propagation models vary for each of the two environments. Furthermore, the comparative performance evaluation result of the developed models with similar studies in the literature shows that the developed models perform favourably. The overall result from the developed models confirms the hypothesis that each location requires a specific propagation model for proper radio wave design and quality of signal transmission and reception. ABSTRAK: Kelebihan yang ada pada kelajuan perlaksanaannya dan juga kurang pergantungannya pada butiran terperinci ciri-ciri khusus bentuk rupa bumi di persekitaran servisnya, model penyebaran empirik telah diterima umum dalam komuniti kajian komunikasi tanpa wayar. Walau bagaimanapun, pemerhatian industri terkini menunjukkan tidak ada sebarang model penyebaran yang sesuai bagi semua keadaan servis radio, ini menghala kepada hipotesis keperluan model tertentu pada keadaan servis tertentu. Bagi menentusahkan secara saintifik hipotesis ini, kajian yang dibentangkan dalam kertas ini mengkaji tentang prestasi model kehilangan penyebaran pada ruang bebas (FSPL) dalam dua persekitaran radio berlainan melalui beberapa jenis halangan berbeza. Kajian telah dijalankan ke atas dua stesen radio penyiaran pada frekuensi 96.5 MHz dan 102.3 MHz melalui ukuran sebaran ruang keupayaan. Data ukuran ruang keupayaan telah diperoleh dan dianalisa. Keputusan analisis menunjukkan keputusan tidak seragam yang melampau antara ukuran kehilangan laluan dan pada kiraan model FSPL. Ketidaksamaan ini memungkinkan keperluan mengubah model FSPL bagi membangunkan model penyebaran pada setiap dua radio stesen yang digunakan dan persekitarannya. Model yang dibangunkan ini kemudiannya dinilai bagi mengesahkan prestasinya dengan model FSPL. Keputusan penilaian menunjukkan perbezaan pada jangkaan model penyebaran bagi setiap dua keadaan. Tambahan, keputusan perbandingan model yang dibangunkan dalam karya ini adalah serupa seperti kajian lain yang berkaitan. Secara keseluruhannya model yang dibangunkan ini mengesahkan hipotesis bahawa setiap lokasi memerlukan model penyebaran bagi rekaan gelombang radio yang sesuai dan juga kualiti signal penyebaran dan penerimaan.

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Section: Introductionmentioning

confidence: 99%

Investigation on Need for Specific Propagation Model for Specific Environment Based on Different Terrain Characteristics

Popoola

Ponnle

Olasoji

et al. 2018

IIUMEJ

View full text Add to dashboard Cite

show abstract

“…The path loss equation for this model expressed in dB as given in [24] is shown below PL(dB) = 46.3 + 33.9 log(f) -13.82 log(h te ) -a(h a ) + (44.9-6.55 log(h te )) log(d) + C m (6) where is the frequency specified in , is the distance between the base station and mobile antennas given in km, h te is the base station antenna height above ground level in meters.h re is the mobile antenna height in meters, is defined as 0 dB for suburban or open environments and 3 dB for urban environments.…”

Section: Cost-231 Modelmentioning

confidence: 99%

“…power in dBm, EIRP = Effective isotropic radiated power in dBm. EIRP is given in(24) and(25) EIRP = Pt + G-L(24)Where G stands for Gains and L for lossesTypical gains considered are the antenna gains both at the transmitter and receiver end Typical losses are connector, body and combiner loss Expanding this yields, EIRP = Pt + Gt + Gr-Lco-Lcon-Lbo (Pt = Transmit power (dBm),Gt = Gain of Transmit Antenna (dBi),Gr = Gain of Receive antenna (dBi),Lcon = Connector loss (dB),Lbo = Body loss (dB),Lco = Combiner loss(dB). The Values of the stated parameters commonly applied in LTE Networks are given by S. A. Mawjoud [24] as; P bts = 40W = 46dBm , G bts = 18.15dBi, G ms = 0dBi ,Lbo = 3dB, Lcon = 4.7dB, Lco = 3dB These parameters are substituted into equation (25) EIRP = 53.5dBm…”

mentioning

confidence: 99%

Improved Propagation Models for LTE Path Loss Prediction in Urban & Suburban Ghana

Gadze¹,

Agyekum²,

Nuagah³

et al. 2019

IJWMN

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To maximize the benefits of LTE cellular networks, careful and proper planning is needed. This requires the use of accurate propagation models to quantify the path loss required for base station deployment. Deployed LTE networks in Ghana can barely meet the desired 100Mbps throughput leading to customer dissatisfaction. Network operators rely on transmission planning tools designed for generalized environments that come with already embedded propagation models suited to other environments. A challenge therefore to Ghanaian transmission Network planners will be choosing an accurate and precise propagation model that best suits the Ghanaian environment. Given this, extensive LTE path loss measurements at 800MHz and 2600MHz were taken in selected urban and suburban environments in Ghana and compared with 6 commonly used propagation models. Improved versions of the Ericson, SUI, and ECC-33 developed in this study predict more precisely the path loss in Ghanaian environments compared with commonly used propagation models.

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“…The ERICSSON model [19][20][21] is an extension of the HATA model and is applicable to frequencies up to 1900…”

Section: Ericsson Modelmentioning

confidence: 99%

Minimizing path loss prediction error using k-means clustering and fuzzy logic

Bhupuak¹,

Tooprakai²

2018

Turk J Elec Eng & Comp Sci

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This research proposes an algorithmic scheme based on k-means clustering and fuzzy logic to minimize path loss prediction error. The proposed k-means fuzzy scheme concurrently utilizes the area topographical variability and multiple path loss prediction models to mitigate the prediction error inherent in the independent use of a conventional path loss model. Vegetation density, manmade structures, and transmission-receiver distances are the fuzzy inputs and the conventional path loss models the output: the free space loss, Walfisch-Ikegami, HATA, ECC-33, Stanford University Interim, and ERICSSON models. The experimental results show that the path loss prediction error of the k-mean fuzzy scheme is only 2.67% compared to the the drive-test measurement, and this is the lowest relative to that of the conventional models. The k-mean fuzzy scheme offers a novel means to approximate path loss in localities with diverse topographical features and also efficiently mitigates the prediction error inherent in the independent use of the conventional prediction models.

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Path Loss Propagation Model Prediction for GSM Network Planning

Cited by 13 publications

References 8 publications

Investigation on Need for Specific Propagation Model for Specific Environment Based on Different Terrain Characteristics

Investigation on Need for Specific Propagation Model for Specific Environment Based on Different Terrain Characteristics

Improved Propagation Models for LTE Path Loss Prediction in Urban & Suburban Ghana

Minimizing path loss prediction error using k-means clustering and fuzzy logic

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