Improved coverage and interference predictions using line-of-sight detection and correction

Christensen, E.L.; Paulsen, S.E.

doi:10.1109/vetec.1996.504035

Cited by 4 publications

(3 citation statements)

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“…It is a zero-mean Gaussian variable and can thus be expressed as (4) where is a zero-mean Gaussian variable of unit standard deviation ; and , the standard deviation of , is itself a Gaussian variable over the population of macrocells within each terrain category. Thus, can be written as (5) where is the mean of ; is the standard deviation of ; is a zero-mean Gaussian variable of unit standard deviation ; and and are both data-derived constants for each terrain category. The numerical values of these constants are given in Table I.…”

Section: The Modelmentioning

confidence: 99%

“…F OR signal strength prediction and simulation in macrocellular environments, the Hata-Okumura [1], [2] model is widely used. This model is valid for the 500-1500 MHz frequency range, user distances greater than 1 km from the base station, and base antenna heights greater than 30 m. There are some reports in the open literature that elaborate on the Hata-Okumura model, e.g., [3]- [5], and also some that use terrain databases [6], [7]. None of these approaches, however, address the variety of new communication systems [e.g., personal communications services, multichannel, multipoint distribution services (MMDS), fixed wireless] that feature smaller cells, shorter base station antenna heights, and higher frequencies.…”

Section: Introductionmentioning

confidence: 99%

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An empirically based path loss model for wireless channels in suburban environments

Erceg

Greenstein

Tjandra

et al. 1999

IEEE J. Select. Areas Commun.

976

333

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We present a statistical path loss model derived from 1.9 GHz experimental data collected across the United States in 95 existing macrocells. The model is for suburban areas, and it distinguishes between different terrain categories. Moreover, it applies to distances and base antenna heights not well-covered by existing models. The characterization used is a linear curve fitting the decibel path loss to the decibel-distance, with a Gaussian random variation about that curve due to shadow fading. The slope of the linear curve (corresponding to the path loss exponent,) is shown to be a random variate from one macrocell to another, as is the standard deviation of the shadow fading. These two parameters are statistically modeled, with the dependencies on base antenna height and terrain category made explicit. The resulting path loss model applies to base antenna heights from 10 to 80 m, base-to-terminal distances from 0.1 to 8 km, and three distinct terrain categories.

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Section: The Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An empirically based path loss model for wireless channels in suburban environments

Erceg

Greenstein

Tjandra

et al. 1999

IEEE J. Select. Areas Commun.

976

333

View full text Add to dashboard Cite

show abstract

“…There have been several suggestions for improving the Okumura-Hata and COST231-Hata models to take more propagation environment data into account [7], [8] and [9]. However, these modifications require either digital maps with sufficient resolution or on-site measurements.…”

Section: ) Enhanced Okumura-hata and Cost231-hata Modelsmentioning

confidence: 99%

Signal level interpolation for coverage area prediction

Backman

IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004

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Predicting the received signal level is important in the design of cellular communication systems. In this paper signal level interpolation around measured signal levels is introduced as a means to predict coverage areas. For signal level interpolation surface fitting methods are required and three methods have been evaluated: Moving Least Squares (MLS), Delaunay Triangulation and Averaging for Interpolation. The first two methods interpolate signal level values over a wide area with samples from only a few measurement points, while Averaging for Interpolation requires closely spaced, evenly scattered points. MLS and Delaunay Triangulation are described in the literature while Averaging for Interpolation is a novel method developed by the author. The interpolated signal levels have been compared with measurements. The mean of the standard deviation of the error for seven test cases is 6.1 dB with the MLS method and 5.1 dB with Triangulation. A comparison between all three methods revealed that in cases of closely spaced, evenly scattered points Averaging for Interpolation gives the smallest mean value of the standard deviation (3.0 dB) for four cases.

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An empirically-based path loss model for wireless channels in suburban environments

Erceg

Greenstein

Tjandra³

et al.

IEEE GLOBECOM 1998 (Cat. NO. 98CH36250)

208

265

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Improved coverage and interference predictions using line-of-sight detection and correction

Cited by 4 publications

References 0 publications

An empirically based path loss model for wireless channels in suburban environments

An empirically based path loss model for wireless channels in suburban environments

Signal level interpolation for coverage area prediction

An empirically-based path loss model for wireless channels in suburban environments

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