Path-loss prediction model for microcells

Har, Dongsoo; Xia, H.H.; Bertoni, H.L.

doi:10.1109/25.790520

Cited by 68 publications

(42 citation statements)

References 16 publications

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“…The suggested two-slope LOS expression agrees well with other two-slope models: ITUR LOS mean between the upper and lower limits, WINNER II LOS, ETSI Berg, and [8].…”

Section: A Los (Single-and Two-slope) and Nlos Proposed Modelssupporting

confidence: 83%

“…This dependence, however, exists only for d > x b . At short distances (d < x b , n = n 1 ) n ∈ (0.865, 1.08) matching the high transmitter height slopecoefficient of [8] and close to the first slope of the two-slope regression fit of [4]. The mean path loss slope coefficient is expected to assume a value that is less than 2, according to the wave-guiding property that characterises this type of environment [15].…”

Section: A Los (Single-and Two-slope) and Nlos Proposed Modelsmentioning

confidence: 95%

“…Dependence of the coefficient f on the mobile node height h M can be found in [14]. [2], [6], [8], [9], [11], [13].…”

Section: A Los (Single-and Two-slope) and Nlos Proposed Modelsmentioning

confidence: 99%

“…Aware of the importance in studying the low-height communication-link propagation-characteristics, research has produced several empirical models [1]- [9]. Single-slope [1]- [3], [5], [6], [9], [10], two-slope [8], [11], and even three-slope [4] models have been proposed, which consider heights compatible with the lowheight channel of this study. In [12], measurements were conducted, at a very near to the ground level (receiver height at 3 to 28cm), at 880MHz, in an urban environment.…”

Section: Introductionmentioning

confidence: 99%

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Measurement and modelling of the propagation channel between low-height terminals

Κωνσταντίνου

Kang

Brown

et al. 2011

IET Microw. Antennas Propag.

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The evaluation of communication systems with low-height terminals requires path loss models that are applicable to low-height links. For the terminology low-height, the range 0.5 (mobile-) to 3m (fixed-node) above ground is considered. Herein, empirical non-time-dispersive propagation models for relaying systems with low-height terminals are proposed. The models consist of line-of-sight and non-line-of-sight branches. Single-and two-slope modelling approaches were examined. The models take into account the effect of frequency, transmitter and receiver height, and environment. They are complemented by shadowing and fast-fading distribution and correlation statistics. The performance of the models in producing accurate estimations is evaluated by comparison with sets of independent data.

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“…The suggested two-slope LOS expression agrees well with other two-slope models: ITUR LOS mean between the upper and lower limits, WINNER II LOS, ETSI Berg, and [8].…”

Section: A Los (Single-and Two-slope) and Nlos Proposed Modelssupporting

confidence: 83%

Section: A Los (Single-and Two-slope) and Nlos Proposed Modelsmentioning

confidence: 95%

“…Dependence of the coefficient f on the mobile node height h M can be found in [14]. [2], [6], [8], [9], [11], [13].…”

Section: A Los (Single-and Two-slope) and Nlos Proposed Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Measurement and modelling of the propagation channel between low-height terminals

Κωνσταντίνου

Kang

Brown

et al. 2011

IET Microw. Antennas Propag.

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“…Xia et al [1,2] proposed path loss formulas for micro-cells in low-rise and high-rise building environments. Additionally, COST 231 Walfisch-Ikegami model [3,4] is also a popular prediction tool for micro cell environments.…”

Section: Introductionmentioning

confidence: 99%

Path Loss Prediction for Low-Rise Buildings With Image Classification on 2-D Aerial Photographs

Phaiboon

Phokharatkul²

2009

PIER

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Abstract-This paper presents a radio wave propagation prediction method for low-rise buildings using 2-D aerial images taken from the actual areas. The prediction procedure was done in three steps. Firstly, the images were classified in order to identify the objects by Color Temperature Properties with Maximum Likelihood Algorithm (CTP MLA). The objects in the images consist of buildings, trees, roads, water and plain. These objects influence wave propagation highly. The MLA classification is a common supervised image segmentation technique in remote sensing domain. However it still needs human editing in case of classification errors. Secondly, the appropriate path loss models were selected to predict path loss. The original Xia path loss model was modified to include the effects of airy buildings and vegetation around the buildings. Finally, preliminary tests provide a better solution compared with measured path losses with the root mean square error (RMSE) and maximum relative error (MRE) of 3.47 and 0.31, respectively. Therefore, the positions for micro-cell base stations could be designed on a 2-D aerial map.

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Path Loss Prediction Models in Cellular Communication Channels

Bertoni

2003

Wiley Encyclopedia of Telecommunications

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The characteristics of the radio channel provide a challenge to the communications engineer in designing systems that are spectrally efficient so as to provide high speed services to a large numbers of subscribers. Path loss is the most basic of the channel characteristics since it influences coverage and interference predictions for any system. Because the radio links in modern wireless systems are between low base station antennas and the subscribers over relatively short paths within cities, or even inside buildings, the buildings have a major influence on the received signal. Modern systems operate at frequencies f in the UHF (300 MHz ‐ 3 GHz) and microwave bands, so that the radio waves have wavelength λ less than a meter. Since the buildings are large compared to wavelength, it is appropriate to think of the radio waves as traveling along rays between the base station and the subscriber. Ray concepts are used in this chapter to make quantitative predictions of the path loss. These predictions are to compare well with measurements, and measurement based prediction models.

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Path-loss prediction model for microcells

Cited by 68 publications

References 16 publications

Measurement and modelling of the propagation channel between low-height terminals

Measurement and modelling of the propagation channel between low-height terminals

Path Loss Prediction for Low-Rise Buildings With Image Classification on 2-D Aerial Photographs

Path Loss Prediction Models in Cellular Communication Channels

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