– Influence of Wind and Rain on a Foliage Radio-Wave Propagation Channel –

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In this thesis, electromagnetic (EM) wave measurements at microwave frequency (10.7 GHz) were conducted in a foliage environment under the influence of artificially created wind and rain in order to investigate their combined effects on a short-range foliage radio-wave propagation channel.

The obtained data was normalized to its mean to extract the temporal fading components due to the created weather-induced variations

. A well-developed software program (by Mathwave Technologies) called “EasyFit 5.5 professional” was then used to characterize and model these variations through the commonly known probability distribution functions associated with radio wave propagation channels.

After successful modeling and characterization, the temporal fading components were found to be Rician distributed, and the Rician K-factor decreases as the strength of wind or rain increases. This reduction in the K-factor indicates an increase in the variability of the channel when wind speed or rain intensity increases.


A foliage radio-wave propagation channel refers to the space between transmitter and receiver embedded with trees or shrubs which may or may not attenuate the propagating waves, depending on many factors such as water content and size of tree leaves, tree trunks diameters, frequency of the waves etc.

While microwave propagation refers to the technology of transmitting information or power by the use of radio waves whose wavelengths are conveniently measured in small numbers of centimeters. This part of radio spectrum ranges across frequency of roughly 1.0 GHz to 30 GHz. This corresponds to 30.0cm to 1.0cm (Natasha, 2011).

Near-ground foliage radio-wave propagations are of interest for the emerging military applications, such as battle field sensor networks and for wireless communication between dismounted soldiers.

In order to guarantee link availability and provide targeted quality-of- service to all the soldiers concerned, a thorough understanding of the temporal variations in the wireless channel is essential. Besides that, knowledge concerning channel time characteristics is also required to design effective fade mitigation schemes (Joshi, et al, 2005).


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