Smart cities around the world are supported by
high-capacity wireless communication networks, which are based
on millimeter-waves links. The propagating waves are sensitive
to hydrometeors, and their signal level is attenuated by rain.
However, most of the links in such networks are shorter than
1 km, imposing large errors on the rain estimation results. In
this paper we demonstrate, using actual measurements from the
city of Rehovot, Israel, how high-resolution rain maps can be
generated from the received signal level measurements collected
by these links. We first propose a method for reducing the errors
in converting signal attenuation to rainfall estimates in short, incity
links. The proposed method requires calibration of model
parameters using side information from either a rain gauge or a
long link in the vicinity of the network. We empirically analyze
the results of the calibrating method using either auxiliary
measurements and show that the performance is satisfactory
for both. Then, we apply a spatial interpolation method on the
rainfall resulting estimates, and demonstrate the construction of
an high-resolution 2-D map of the accumulated rain in a city, a
product with great potential for improving well-being of life in
urban areas.
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Parameter Estimation of In-City Frontal Rainfall Propagation
Modern infrastructures support smart-city operations, which
are based on short millimeter-waves wireless links connected
by a dense network. These links are sensitive to hydrometeors, and their signals attenuated by rain. In this study,
we demonstrate that standard signal-level measurements
being collected by the network can be used to estimate the
movement of an ongoing storm. Parameters characterizing
the movements of the frontal rain cell, as its velocity and
direction, can be accurately estimated. We first estimate the
differential time of arrival of the attenuated signals between
pairs of links, from which we extract the parameters of interest. We demonstrate our results using actual measurements
from an operating system in the city of Rehovot, Israel
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- Award ID(s):
- 1910757
- NSF-PAR ID:
- 10218480
- Date Published:
- Journal Name:
- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Page Range / eLocation ID:
- 4910 to 4914
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP40776.2020.9053572
