Probability density of the magnitude of indoor fading

Pao Yue-kong Library Electronic Theses Database

Probability density of the magnitude of indoor fading


Author: Hu, Jinzhen
Title: Probability density of the magnitude of indoor fading
Degree: M.Sc.
Year: 2011
Subject: Hong Kong Polytechnic University -- Dissertations
Radio -- Transmitters and transmission -- Fading
Department: Faculty of Engineering
Pages: viii, 82 leaves : ill. (chiefly col.) ; 30 cm.
InnoPac Record:
Abstract: A transmitted impulse δ(t), after propagating through a multipath-channel, would arrive at a receiver as r(t) = IΣi=1ciδ(t+τi), where τi denotes the i-th time-delayed multipath's propagation-delay, Ci denotes the i-th time-delayed multipath's complex-valued fading amplitude. Coherent summation of these multipaths would produce an overall fading factor of C = √IΣi=1|ci|2 . The value of this fading factor depends on the fading channel. For indoor wireless propagation, C would be affected by 1. The indoor floor plan, the position / materials / surface-roughness / size of walls / furniture / decor. 2. The transmitting / receiving antennas' polarizations, antenna-type, horizontal position in the floor plan, vertical positions from the ceiling and floor. 3. The presence / absence of humans and other animals. This C is affected by macroscopic fading, shadowing, and microscopic fading. The last could vary very significantly within fractions of a wavelength (approximately a foot at 1 GHz). This microscopic variation in C could be modeled as a random variable. This dissertation aims to characterize this random variable's probability density. The CINDOOR ray-tracing software will be used to simulate an indoor environment. To simulate microscopic fading, the faded signal is sampled over a 16 x 16 x 6 grid, spanning over a volume of a wavelength times a wavelength times a wavelength. Implicitly assumed here is that macroscopic fading and shadowing effects remain constant in this volume. The above 1536 spatial samples are then gathered into a histogram. Normalization of the area of this histogram to unity -that would give an estimate of the probability density of C, under that one particular propagation environment. This probability-density estimate is then least-squares fit to various probability densities, to identify the best-fitting density as a mathematical model of the density of C. This best-fitting density turns out to be Weibull. Then, one factor is changed in the propagation environment (e.g. the receive-antenna's polarization) to test how the best-fitting density's parameters may change as a consequence.

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