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Urban propagation modeling for wireless systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Urban propagation modeling for wireless systems
Author:	Smith, William Mark.
Description:	249 p.
Notes:	Adviser: Donald C. Cox.
Notes:	Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2022.
Contained By:	Dissertation Abstracts International65-04B.
Subject:	Engineering, Electronics and Electrical.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3128690
ISBN:	0496759523

Urban propagation modeling for wireless systems
Smith, William Mark.

Urban propagation modeling for wireless systems [electronic resource] - 249 p.

Adviser: Donald C. Cox.

Thesis (Ph.D.)--Stanford University, 2004.

Partitioning streets in statistical models according to their orientation, i.e., radial or cross streets, reduces path-loss prediction error by up to 6 dB. The average received signal power is lognormally distributed about exponential distance-dependent trends for radial and cross-street partitions.

ISBN: 0496759523Subjects--Topical Terms:

226981
Engineering, Electronics and Electrical.

Urban propagation modeling for wireless systems
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245 1 0 $a Urban propagation modeling for wireless systems $h [electronic resource]
300 $a 249 p.
500 $a Adviser: Donald C. Cox.
500 $a Source: Dissertation Abstracts International, Volume: 65-04, Section: B, page: 2022.
502 $a Thesis (Ph.D.)--Stanford University, 2004.
520 # $a Partitioning streets in statistical models according to their orientation, i.e., radial or cross streets, reduces path-loss prediction error by up to 6 dB. The average received signal power is lognormally distributed about exponential distance-dependent trends for radial and cross-street partitions.
520 # $a Signal strength is measured with an instrumented automobile every 2 mm for along streets up to 600 m from the transmitter in the San Francisco financial district, a dense urban environment with buildings up to 160 m tall situated along a grid-like pattern of streets on level terrain. The transmitter operates at 880 MHz with an antenna mounted 54.3 m above the ground.
520 # $a Signal strength is modeled by random processes representing small-scale fading, i.e., fluctuations &gsim; 10 dB over distances on the order of a wavelength, and large-scale fading, i.e., changes &gsim; 3 dB in the local average of the small-scale fading over tens of meters. Repeatability of signal strength measurements is quantified on the same night and at 10-day and 15-month intervals. For measurements in the same lane with less than ten minutes between measurements, the large-scale fading is modeled by the same process, and the cross correlation of the envelope of the small-scale fading processes between runs is as high as 0.6. After 15 months the small-scale fading processes are independent; a lognormal dissimilarity process with standard deviation as high as 4 dB accounts for differences in the large-scale fading. Spatial repeatability is quantified by comparing measurement profiles from adjacent lanes on the same night, in which case the large-scale dissimilarity process has magnitude greater than 4 dB; small-scale fading is independent for the two runs.
520 # $a To characterize abrupt changes in signal strength profile at street intersections, averaging windows of 1--5 wavelengths are used rather than windows of more than 20 wavelengths used elsewhere along the street. For rates of change in signal strength greater than 1 dB/m adjacent to the edges of street intersections, the extent of the change in signal strength is 8 +/- 4 dB, and the profile changes by less than 2.5 dB within the intersection. For rates less than 1 dB/m, the extent of change in signal strength is directly proportional to the rate to within 4 dB.
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791 $a Ph.D.
792 $a 2004
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