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RF
Coverage Validation and Prediction with GPS Technology
By Jin Yu, Berkeley Varitronics Systems, Inc.
It has taken many years for wireless engineers
to "tame" wireless communications and there are still many
complexities in planning wireless networks due to the unpredictable
and varying RF environment. There are mainly two techniques
to plan and deploy wireless networks. One uses theoretical
electromagnetic models to predict RF coverage. The theoretical
model requires information for the survey area, such as
the presence of trees, the location of buildings, the materials
that comprise the buildings, and so on. Inputting the information
into the prediction software and computing the coverage
takes a lot of time. In addition, the varying environment
increases the difficulty to accurately predict and maintain
the coverage with the theoretical model. For example, tree
foliage changes from winter to summer. The real signal in
the environment is also unknown with prediction software.
Another technology is to plan and deploy
wireless networks based on real measurement data. A dedicated
site survey is required to build stable wireless networks
in a varying RF environment. Berkeley Varitronics Systems
(BVS) has developed Hive software with YellowJacket hardware
for in-building site surveys. It asks the user to tap the
recording locations on the iPAQ during the measurement.
When an outdoor network with a large area is considered,
using Global Positioning System (GPS) technology to locate
the measurement point makes a quick automatic measurement
possible and gets site surveys done easier. The software
we will use for reference is Berkeley Varitronics Systems'
new product -- Drone Site Survey system. Since time is money,
Drone software combined with YellowJacket hardware can save
labor cost in deploying a wireless network and later in
maintaining that network.
Currently, GPS location accuracy can be controlled within
several meters and the GPS information can be updated every
second. The good accuracy and high update rate make it possible
to use GPS to record measurement locations in collecting
measurement data. The Drone Site Survey system is composed
of three parts; Drone Projector, Drone Collector, and Drone
Analyzer. Drone Projector and Collector are used with the
BVS YellowJacket Plus (which has a GPS module) and HP iPAQ;
Drone Analyzer is PC-based software. YellowJacket measures
all 14 OFDM/DSSS network channels, which operate on the
IEEE 802.11b/g standard. This allows the user to determine
the AP's MAC address, SSID, and RSSI signal levels for all
access points on or off any 802.11b/g WISP or Hotspot. A
YellowJacket with GPS module is shown in Figure
1. Based on the real measurement data collected
by Drone Collector, Drone Analyzer can perform a quick analysis
to estimate access points (AP) locations, validate RF coverage,
and predict coverage reliability.
Drone Projector
BVS Drone Projector is used to geo-code the user's map and
projects the geographical information (latitude and longitude)
into real distance information (meter/feet). The first step
is to load the map (Bitmap) into an iPAQ; the second step
is to take it outside and tap on the screen where the user
is. After having enough points, the user's map can be projected
into a geo-coded map. If the tapped points do not meet the
requirements to do the projection, Drone Projector will
ask the user to record more points. The customer can use
any image onhand or get the image from Google, Mapquest,
or other map websites. Drone Projector is shown in Figure
2, where the black dots represent the tapped points.
Drone Collector
Drone Collector collects and stores geographical information
and wireless network information during a walk or drive
study. The measurement points will be shown on the map as
the user moves. Therefore, Drone Collector can also be used
to navigate the user's drive. This stored information will
be used in Drone Analyzer to do coverage reliability analysis,
AP's location estimation, power or signal-to-noise ratio
contour analysis, and AP's overlap analysis. Drone Collector
is shown in Figure 3, where the yellow
and red dots represent the measurement points.
Drone Analyzer
The first step to use Drone Analyzer is to import the projection
file. However, a map is not necessary in Drone Analyzer.
If there is no projection file, Drone Analyzer can also
process the measurement data based on the stored latitude
and longitude information. There are two options for the
user to associate a post-processing plot to a map:
The user can save the plot and then put it on top of the
map from GoogleEarth or other maps, which must have latitude
and longitude information.
Drone Analyzer can automatically generate a KML file, which
can accurately overlap the RF coverage on top of the map
of GoogleEarth.
The second step is to import the data collection file. Drone
Analyzer provides the options to select multiple files and
to process those files jointly or separately. After files
are selected, the software begins to sort the access points
and estimate their locations. Cell coverage reliability
can be analyzed and predicted based on the estimated APs'
locations. Drone Analyzer graphically indicates RF coverage
with multicolor representation, showing:
• Location of APs
• Power, signal-to-noise ratio, number of APs in the
survey area
• Reliability of an AP or a group of APs with a certain
radius and power threshold
Coverage holes would show up in the resulting reports as
colorless (white areas). The network administrator can also
use the YellowJacket to find the reason for the network
hole. Through the plot of the number of APs, the network
administrator will know exactly how many APs are in a location,
the MAC addresses, and the SSID of these APs. Therefore,
the administrator will know which AP is an interferer and
which AP belongs to his network. The administrator can take
away some APs to reduce the overlap of the current network.
Drone can also automatically provide a network and coverage
report in HTML format. With the help of GPS technology,
Drone's capability, to quickly collect the measurement data
and efficiently analyze the data, will greatly help the
network service provider to build stable Wi-Fi networks
in the cities, outdoor areas, and university campuses and
later maintain them. An example of RF coverage with contours
is shown in Figure 4. In the following
section, we will present the technologies used in Drone.
Coverage Reliability Analysis with GPS technology
1. GPS Module
GPS is a navigational system that calculates position from
24 satellites orbiting the earth. BVS YellowJacket Plus
can have a Motorola 12-channel differential GPS receiver
(M12). This allows YellowJacket users to geo-time-stamp
every measurement taken for post processing. The 12-channel
platform was designed for a wide range of GPS positioning
and tracking applications and superior performance: split-second
reacquisition, maximum embedding flexibility, low power
consumption and enhanced urban canyon and foliage performance.
Some parameters of M12 performance are [1]:
Accuracy:
• Position 25 m SEP without Selective Availability
(SA)
• 100 m 2DRMS (95%) with SA
• 1 to 5 m typical in differential mode
• Altitude 156 m RMS (95%)
• Velocity 0.02 m/s without SA
• Time pulse UTC ± 500 ns with SA on
Dynamic limits:
• Velocity 515 m/s maximum at altitudes > 18000
m
• Altitude -1000 m minimum
• 18000 m maximum at velocities > 515 m/s
• Acceleration 4G maximum
• Jerk 5m/s3 maximum
2. Propagation Model
Based on the measured signal strength and locations, the
cell radius with a certain outage probability or the cell
reliability with a certain radius can be estimated through
a robust method [2], [3].
The verification method is particularly useful in planning
a wireless network since it effectively determines the geographic
extent of reliable RF coverage. Outage probability means
the possibility that signal strength is below a threshold.
Reliability represents the probability that signal strength
at a location or in an area is higher than a threshold.
The Hata model is used here to predict the radius and the
cell reliability. The received signal strength (in dBm)
is modeled as a function of distance, antenna height, path
loss exponent, and a normally distributed random variable
due to large-scale fading [4]. Based on
the measurement data, we are able to use a linear regression
algorithm to estimate the parameters, such as the path loss
exponent, the standard variation of the random variable,
and so on. Since the signal strength is a random variable,
an outage probability for any location in the survey area
can be estimated with a predetermined power threshold. At
the same time, a radius can also be estimated based on a
given outage probability. Since outage probability for any
location in the survey area can be estimated, the cell or
area reliability can be estimated by averaging the reliability
(1-OutageProbability) over the area. An example of RF coverage
reliability analysis is shown in Figure 5.
The power threshold is set to be -75 dBm and the radius
is 80 feet. The reliability at the boundary is calculated
as 0.9413 and the cell reliability is obtained as 0.9753.
3. AP Location Estimation
The distance from a location to an AP is necessary in coverage
validation and reliability analysis. Thus, the location
of the access point is essential to the analysis. However,
in most cases, APs' locations are unknown, which indicates
that the distance cannot be obtained. Based on a derived
minimum mean square error (MMSE) algorithm, the AP's location
can be estimated. Usually there are hundreds of measurement
points for one access point during a survey study, which
provide enough points to estimate AP's location accurately.
The security of the network can be improved by locating
unknown APs. An example is shown in Figure 5
to illustrate the accuracy of AP estimations.
4. Spatial Interpolation Technology
When the AP location is known, we can predict the power
from the Hata channel model. With the measurement results,
the signal strength can also be estimated by using spatial
interpolation algorithms, such as inverse distance weighting
(IDW) algorithm, Shepard weighting algorithm, IDW with anisotropy
correction, IDW with gradient correction, and so on [5].
In the Drone Site Survey system, a combination of the spatial
interpolation and RF power prediction (from the Hata model)
is used to show the RF coverage.
Conclusions
Drone software, along with YellowJacket Plus hardware, provides
a network administrator with an efficient tool to quickly
survey an outdoor network with a large area and constantly
monitor the rapidly changing RF environment. The use of
GPS technology helps reduce the amount of labor cost involved
with network setup and maintenance. The Drone's capability,
to quickly collect the measurement data and efficiently
analyze the data, will greatly help the network service
provider to build stable Wi-Fi networks in the cities, outdoor
areas, and university campuses.
References
[1] M12 oncore - User Guider Supplement,
Motorola, Inc.
[2] P. Bernardin and K. Manoj, "The postprocessing
resolution required for accurate RF coverage validation
and prediction, IEEE Transactions on Vehicular Technology,
vol. 49, no. 5, Sep. 2000, pp. 1516 - 1521.
[3] P. Bernardin, M. F. Yee, and K. Manoj,
" Cell radius inaccuracy: a new measure of coverage reliability,"
IEEE Transactions on Vehicular Technology, vol. 47, no.
4, Nov. 1998, pp. 1215 - 1226.
[4] R. Vaughan and J. B. Anderson, Channels,
Propagation, and Antennas for Mobile Communications, IEE
Electromagnetic Waves Series, 2003.
[5] P. Revesz and L. Li, "Constraint-based
visualization of spatial interpolation data," Sixth International
Conference on Information Visualization, 2002, pp. 563-569.
Berkeley Varitronics
Systems, Inc.
www.bvsystems.com
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