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Channel Characterization and Reliability of5.8 GHz DSRC Wireless CommunicationLinks in Vehicular Ad Hoc Networks in
Suburban Driving Environment
A thesis submitted in partial fulfillmentof the requirements for the degree of
Master of Sciencein
Electrical and Computer Engineeringat
Carnegie Mellon University
Jacob J. Meyers
Carnegie Mellon UniversityDepartment of Electrical and Computer Engineering
5000 Forbes AvenuePittsburgh, PA 15213, USA
09 May 2005(Revised 29 October 2005)
Key Words - VANET, vehicular ad hoc network, packet error rate, link reliability, suburban environment,
field test, channel characterization, path loss model., data distribution.
Abstract- A five node vehicular ad hoc network has been developed and used to model the
communications channel and estimate the packet error rate (PER) in real-world suburban driving
situations in varying traffic conditions. Measurements suggest that signal attenuation can be modeled
using the Log-Normal Path Loss Model with standard deviation of~ = 9.43 dB, but with an anomalously
low path loss exponent of n = 1.03. The reason for the low path loss exponent is not yet understood, but
data bias is suspected. Measurements also show a PER of 10% or less out to a distance of approximately
100 m, and indicate no strong correlation with relative or absolute velocities up to 48 KPH (30 MPH).
ACKNOWLEDGMENTS
I would like to offer special thanks to the following people for their involvement in this research effort.
Thanks to Dan Stancil (CMU) for serving as my academic advisor over the course of this project and
mentoring me as a student and a researcher. Thanks to Rahul Mangharam (CMU) and Dan Weller (CMU)
for the tremendous effort required to create and continually upgrade the software package used
throughout the research effort. Thanks to Jay Parikh (GM) for providing focus, guidance, support, and
encouragement on behalf of the research sponsors at General Motors.
Additional thanks to Suchit Mishra (CMU) and Kevin Borries (CMU) for driving during the many
collection efforts. Additional thanks to Raj Rajkumar (CMU), Christopher Kellum (GM), Hariharan
Krishnan (GM), Rajan Prasanna (GM), Priyantha Mudalige (GM), Vikas Kukshya (HRL), and Jijun
(HRL) for their suggestions and other guidance.
TABLE OF CONTENTS
Chapter One - Introduction ............................................................................................................................ l
Chapter Two - Description of Test Bed ........................................................................................................ 2
Description of Test Bed Hardware ................................................................................................................ 2
Description of Test Bed Software ................................................................................................................. 3
Chapter Three - Calibration of Radio Test Kits ......................................................................................... 6
Radio Test Kit Transmitter Calibration ........................................................................................................ 6
Radio Test Kit Receiver Calibration ............................................................................................................. 7
Chapter Four - Data Collection Techniques ................................................................................................ 8
Chapter Five - Empirical Results .................................................................................................................... 9
Basic Data Structuring and Calculations ........................................................................................................ 9
Data Analysis as a Function of Time and the Observation of Scenario Specific Data Features ............. 12
Channel Characterization .............................................................................................................................16
Data Analysis Techniques ........................................................................................................................17
Determination of Path Loss Exponent .................................................................................................... 19
Effects of Data Point Distribution "Clipping" ,on Data Analysis Results ............................................ 23
Determination of Standard Deviation of Gaussian Random Variable ................................................. 27
Link Reliability ..............................................................................................................................................29
Data Analysis Techniques ........................................................................................................................30
Determination of Link Reliability .......................................................................................................... 31
Chapter Six- Conclusions .............................................................................................................................33
Chapter Seven - Future Work ..................................................................................................................... 34
References ........................................................................................................................................................36
Appendix A- Transmitter Calibration Techniques .................................................................................. 38
Appendix B - Receiver Calibration Techniques ........................................................................................ 41
I. INTRODUCTION
The emergence and rapid commercialization of high-speed short-range wireless interfaces and low-cost
Global Positioning System (GPS) devices provides the opportunity to deploy a range of useful and
practical inter-vehicular communication applications. The integration of the Dedicated Short Range
Communications (DSRC) system in vehicles would enable the peer-to-peer communication required for
various proposed safety and emergency notifications and multimedia telematics applications [1,2].
Unfortunately, there has been very little realistic field testing of vehicular ad hoc networks (VANETs)
using the IEEE 802.1 lp protocol in dynamic mobile environments. Consequently, the large scale path
loss of the communications channel between nodes has not been accurately modeled using empirical data,
and the performance and reliability of such networks are not well understood in real-world driving
environments.
J. Maurer, et al. describe a set of real-world field experiments using narrow-band measurements at 5.2
GHz [3] similar to those presented in this thesis, however, they only use a single link between two
vehicles. Furthermore, link reliability with respect to packet error rates is not addressed. In addition, their
efforts to model the channel focus on fading statistics, Doppler analysis, and level crossing rate, but do
not address large scale path loss as function of distance. X. Zhao, et al. characterized wideband outdoor
mobile communications signal propagation at 5.3 GHz using techniques very similar to those described in
this thesis [4], however, they used a stationary transmitting node and a mobile receiving node rather than
an ad hoc vehicle-to-vehicle network and did so at a frequency below that of the new IEEE 802.1 l p band.
Likewise, T. Schwengler and M. Gilbert as well as G. Durgin, et al. conducted experiments at 5.8 GHz in
residential neighborhoods [5,6] very similar to those presented in this thesis; however, as was the case
with X. Zhao, et al., they used a stationary transmitting node and a mobile receiving node rather than an
ad hoc vehicle-to-vehicle network.
A. Visser, et al. developed a hierarchical method of modeling the reliability of DSRC links for electronic
toll collection applications [7]; however their model was specifically for stationary node to moving
vehicle links and does not apply to vehicle-to-vehicle communications networks. In addition, their results
were based solely on simulations, and were not validated using realistic field testing. Likewise, M.
Torrent-Moreno, et al. and J. Yin et al. have shown simulation results using a 5.8 GHz DSRC vehicular
ad hoc network with a reasonable estimate of link reliability [8,9]. However, the simulation utilized a
theoretical large scale path loss model and the results were not validated using realistic field testing.
The General Motors Collaborative Research Laboratory Ad Hoc Networking Project Team at Carnegie
Mellon University has developed a five node test-bed platform to collect the real-world data needed to
develop RF channel propagation models and VANET routing protocols for realistic driving situations [1 ].
This thesis discusses the composition of the platform, data collection and analysis techniques, and the
findings and conclusions about the channel characterization and the reliability of the individual links
between networked vehicles in suburban driving environments.
II. DESCRIPTION OF TEST BED
Each node in the ad hoc network presented in this thesis corresponds to one of five vehicles equipped
with a radio test kit.
A. Description of Test Bed Hardware
Unlike J. Maurer, et al. and G. Durgin, et al. who used signal generators and spectrum analyzers to make
their channel measurements, [3,6], the wireless vehicular networking test-bed used to collect the data
presented in this thesis was created using commercially available communications equipment.
Each radio test kit is composed of a CSI Wireless DGPS MAX differential GPS receiver with a
magnetically mounted antenna, an onboard IBM ThinkPad T23 2647-9LU laptop computer with a
modified Atheros wideband mini-PCI IEEE 802.11a based wireless interface [2,9], and a M/A-Com
ground-plane magnetically mounted 802.1 la radio antenna as shown in Fig. 1. The physical layer of the
IEEE 802.1 la wireless card has been modified to emulate the DSRC standard specifications [3] with a 10
MHz signal bandwidth that operates at a variable carrier frequency in 5.85 - 5.925 GHz spectrum.
Each radio test kit includes a Logitech audio headset and a generic video camera to facilitate multimedia
applications, and a Sierra Wireless AirCard 555 (CDMA lxRTT) cellular card and Digital Antenna, Inc.
3 watt dual band cellular amplifier to facilitate remote monitoring of test-bed. All devices are powered by
the vehicle’s DC power system via the cigarette lighter, utilizing DC-DC power converters as needed.
The equipment fits neatly in a plastic molded case and is easy to carry and quick to set up as shown in
Fig. 1. The transmission power of each IEEE 802.11 a wireless card was set to 20 dBm, and all test-kit
antennas were mounted on the roof of each vehicle.
Fig. 1 Radio Test Kit - Laptop Computer (A), GPS Antenna (B), Receiver (C), DC Power Cables with Car Adapter (D), 5.8
Antennas (E), and Audio / Video Accessories (F)
B. Description of Test Bed Software
Rahul Mangharam and Daniel Weller, both members of the General Motors Collaborative Research
Laboratory at Carnegie Mellon University, developed the GTK RoadMap software package used in this
research project. The GTK RoadMap software tracks vehicle locations on a virtual map, operates the
wireless communications hardware package, and records measured data. The onboard laptop computer in
each radio test kit runs Red Hat Linux Version 9 (Kernel 2.4.18-3) which provides a fertile platform for
network protocol and application development. There are primarily three layers of software, built from
open source libraries.
Runtime display capability l"or multiple vehicles was added to the original the GTK RoadMap software
tool [4] used by the test-bed so that the current location and movement of all vehicles in the network can
be visually tracked as they are driven. Communi[cations capability was added so that each vehicle’s
onboard computer can act as, a server and accept c,~nnections from other vehicles. Each computer runs a
User Datagram Protocol (UDP) client thread to connect to all other computers in the test-bed. The
connections occur at the socket level; therefore, the application manages the end-to-end data exchange
between each client and server. The client and server connections are displayed in a panel at the base of
the user interface. The underlying kernel-based networking software handles multi-hop routing along the
set of links between the client and server.
GPS location coordinates are computed five times per second and have an accuracy of < 2m. The
transmission of each data packet coincides with the computation of each set of location coordinates;
therefore, data packets are exchanged five times per second as well.
Using this client-server setup, each vehicle in the network exchanges data packets with headers
specifically designed for the efficient exchange of position and network information. Each transmitted
packet header contains the fbllowing data: packet number, packet size, transmitter IP address, time the
packet was transmitted, the longitude, latitude, and altitude of the transmitting vehicle, the speed and
heading of the transmitting vehicle, RF channel, data transmission rate [Mbps], and transmitted signal
strength as well as source and destination routing information and GPS receiver statistics. The packet
pa.yload is utilized to send both critical information such as emergency messages and non-critical
information such as voice and video, multimedia, and application data.
The onboard computer in each test kit logs the GPS and network data contained in the header of both
transmitted and received packets. The logged value transmitted signal strength field for each received
packet is replaced by the measured received signal strength or RSSI. In addition, the packet origin (local
or network) and the distance from the transmitting node to the node at which the data is logged is
included in the data log. This; distance value is zero for all logged transmitted packets and non-zero tbr all
logged received packets.
These data logs enable the playback of the route driven, at either actual speed or a user defined higher
speed, and the visualization of the vehicles on a vector-based rendering of the map traversed. The
mapping functions utilize TIGER/Line 2002 data files available from the U.S. Census Bureau [1 I]. In
addition, the raw data can also be used for external post-processing and analysis.
Within the data logs, transmitted packets are differentiated from received packets by the "packet origin"
field - "local" packets were transmitted by the node at which the packet was logged and "network"
packets were transmitted by some other node in the network. The identity of the transmitting node is
specified in the "transmitter [P address" field. The location, speed, and heading data of all packets is thai
of the node from which the packets were transmitted, therefore, when the data is processed, the location,
speed, and heading data of the received packets must be interpolated from the nearest transmitted packet
data entries in the log of that particular node.
Each node also has the calz, ability of using the AODV ad hoc networking protocol [1,10], but packet
relaying was not used for collecting the data reported here. Consequently, the results we report apply to
single hop links.
IlL CALIBRATION OF RADIO TEST KITS
The transmitted signal strength for all packets sent from any radio test kit can be specified by the user in
terms of a "ForcePower" index value, and the received signal strength of the all packets received by any
radio test kit are measured in terms of an "RSSI" index value. In order for these index values to have an5’
meaningful value in measuring signal attenuation, they had to be calibrated in terms of a physical power
value [dBm] for each radio test kit.
A. Radio Test Kit Transmitter Calibration
Each radio test kit’s transmitted power was calibrated using another radio test kit, a signal detector, an
attenuator, an oscilloscope, and a signal generator. The power transmitted at each "ForcePower" setting
was determined by measuring the amount of power required to create a square-wave-modulated signal of
the same frequency and magnitude using the signal generator. Five iterations of such measurements were
averaged at each "ForcePower" setting to generate the corresponding transmitted power value [dBm]. The
calibration table of "ForcePower" index values and the corresponding overall average power values
[dBm] is shown graphically in Fig. 2. Appendix A provides additional details about the transmitter
calibration process.
rewoP
d
t
snarT
24~ GM-2
i GM-3
22
20
18
16
14
10~ ¯
R ....20 25 3o~ ~5
ForcePower Index40 45 5O
Fig. 2 Measured Transmitted Power vs. ForcePower Index of the FiveRadio Test Kits (GM-I to GM-5) Used in the Research Project
B. Radio Test Kit Receiver Calibration
The received signal power measured by each radio test kit was calibrated using another radio test kit
whose transmission power was previously calibrated and a series of attenuators. The received power
corresponding to each "RSSI" value was determined by correlating the "RSSI" values logged by the
receiving test kit with known received signal strengths. Known received signal strengths were created by
attenuating the known transmitted power of the transmitting radio test kit using known amounts of
attenuation. All of the known received signal strength values corresponding to each measured "RSSI"
value were averaged, yielding a single received signal strength value for each possible "RSSI" value. The
calibration table for "RSSI" index values and the corresponding overall received signal strength [dBm]
for each radio test kit is shown graphically in Fig. 3. Appendix B provides additional details about the
receiver calibration process.
7
!
rew0P
devie¢
R
-20 ~
Model-30 GM-1
GM-2GM-3
-40 GM-4GM-5
-50
-60~
-1°°o lO 20
+
+
30 40 50 60 70 80RSSI Index
Fig. 3 Actual Received Power vs. RSSI Index lbr Each Radio TestKit (GM-1 to GM-5) Compared With the Model Given by Eq. (1)
For the most part, the calibrated received signal strength values were consistent with the model (1)
provided with the radio cards for "RSSI" values between 20 and the upper end of the range of values.
P~[dBm]= RSSI - 95 (1)
IV. DATA COLLECTION TECHNIQUES
The data presented in this thesis was collected in suburban driving environments, characterized by one or
two story buildings and residential streets. Each data set represents approximately one hour of driving
time. The radio test-kits were set up for broadcast transmission such that a single transmission can be
received by N-I receivers, where N was the number of nodes involved in the test run. The packet
transmission rate was five times per second and was equal to the rate at which the GPS receiver unit
updates location, speed, and heading data.
The data set presented in this. paper contains over 625,000 communications link measurements taken over
the course of multiple days. These measurements required approximately five hours of driving time, and a
combination of 38 individual node-to-node links while driving various numbers of vehicles in a convoy-
like formation through the Squirrel Hill, Shadyside, Oakland, and Bloomfield residential neighborhoods
in Pittsburgh, PA.
V. EMPIRICAL RESULTS
A. Basic Data Structuring and Calculations
As previously mentioned, the location, (absolute) speed, and heading data of all logged packets was that
of the node from which the packets were transmitted, therefore, the logged received packet data provided
no information about the point at which the packet was received. Once each data log was parsed, the
location, (absolute) speed, and heading data for each received packet was replaced with the logged
location, (absolute) speed, and heading data of the packet transmitted from the receiving node at the same
time that the incoming packet was received. The information about the point from which the packets were
transmitted is preserved in each data log. After the data replacement procedure was complete, the data
was divided into data sets for transmitted and received packets. The data set for received packets was then
subdivided by the transmitter’ from which they originated.
Before the transmitted and received signal strength values could be used for any data analysis, they had to
be converted from the "ForcePower" and "RSSI" index values into physical power values [dBm]. The
power value conversion was accomplished by using a "look-up" table approach. A search was conducted
for the "ForcePower" index value of each transmitted packet in the corresponding transmitter calibration
table. Once the "ForcePower" index value was located in the table, the corresponding transmitted power
[dBm] value was used to replace the "ForcePower" index value for that particular packet in the matrix of
transmitted packet data. Likewise, the "RSSI" index values were replaced with the received power [dBm]
values contained in the corresponding receiver calibration table.
At this point, the packets transmitted from Test-Kit A were matched up with the packets received by Test-
Kits B,C,D, and E which were known to have originaled from Test-Kit A using the packet number
information. The resulting matrix of transmitted packet - received packet pairs represented the "received
packets" data set for that particular link. All packets transmitted from one test-kit for which there was no
received packet at any one ol~the other test kits was considered a "dropped packet" for that particular link.
By definition, there were N-1 links for each transmitter - one to each of the other test kits. Each of these
links was evaluated individually, therefore the number of packets received by or dropped en route to any
other given test-kit was independent of that of all other test-kits.
The distance between any two given nodes was determined using the Haversine formula (2), given the
latitude and longitude of each node [11]:
ALat = Lat~_ - Lat~
ALong = Long~_ - Long~
a-- sin +cos(Latl)’cos(Lat2)" sinng
(2)
c = 2"atan2 (~-,~-)1
Distance = R ̄ c
where R = 6,373,000 [m] is the radius of the earth optimized for locations approximately 39° from the
equator.
10
If a packet was dropped, the location of the intended receiving unit, and thus the distance between the two
nodes, and other pertinent information could only be estimated. Since the packets containing the location,
speed, and heading data were transmitted five times per second, it was assumed that the validity of this
data for any given node would not be significantly degraded if fewer than five consecutive packets were
dropped if the nodes were moving at reasonable speeds. The location, speed, and heading of the intended
receiving unit for dropped packets were therefore assigned the values of the last successfully received
packet if there was less than one second time differential between the times at which the two packets were
transmitted. Unfortunately, the received signal strength of dropped packets cannot be accurately estimated
at this time due to the unknown behavior of fast-fading effects.
One goal of the project was to determine a large-scale fading model for the 5.8 GHz peer-to-peer channel.
To isolate the large-scale fading behavior, the effects of fast fading were removed using a sliding average
of the received signal strength measurements as a function of time. This sliding average was implemented
by sequentially assigning each transmitted and received data packet in the entire data set the average
value of itself and any packets transmitted and received within a one second margin centered about the
time value of the given packet. Due to the fact that the averaging of the signal strength data was done with
respect to a given time period, the transmitted and received power values had to be converted from the
logarithmic scale to the linear scale prior to being averaged, and then converted from the linear scale back
to the logarithmic scale to be consistent with the rest of the signal strength data analysis.
Distance and speed measurements do not exhibit fast fading effects; however, the accuracy of the GPS
location and speed measurements does vary as a fi~nction of the number of satellites available and other
such factors. The sliding aw,~rage technique used to filter out the fast-fading effects on received signal
strength measurements was equally useful for minimizing the effects of less than accurate location and
speed data measurements.
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The velocity of any two nodes relative to each other was determined by calculating the change in the
distance between those two nodes from one received packet to the next divided by the time elapsed
between transmission of the two packets. By definition, a positive relative velocity indicates that the
nodes were moving away from each other, while a :negative relative velocity indicates that the nodes were
moving towards each other.
In most cases, it was useful to separate the data collected while the vehicles were stationary from the data
collected while they were in motion. While the vehicles were stationary, but actively collecting data, the
data points corresponding to specific distance and signal attenuation values accumulated in large numbers
inconsistent with the typical distribution of data collected while vehicles were in motion. Separation of
these data points was accomplished by creating a temporary data matrix containing only the data
corresponding to packets in the matrix of transmitted and received packet pairs that were transmitted
while either the transmitting node or the receiving node (or both) had a speed value greater than zero.
B. Data Analysis as a Function of Time and the Observation of Scenario Specific Data Features
Although the objective of this research effort was to characterize the channel and evaluate the link
reliability on a large scale that encompasses all driving siluations in suburban environments, it was often
desirable to relate a particular data set to the specific route followed while collecting the data. The
simplest way to evaluate ’such effects was to simply play back the logged sequence of packet
transmissions. The GTK RoadMap program allows users replay past data logs, both at normal speed and
at several times the normal :speed, retracing the exact path of each vehicle on the GTK RoadMap user
interface map. There are four major data variables that, when plotted as a function of time, directly link
system performance metrics to individual scenarios shown on the GTK RoadMap data log replays.
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First, plots of the packet numbers of packets received and dropped over the course of time as well as the
cumulative number of each, as shown in Fig. 4, clearly indicate the points in time at which the pattern of
packets received and dropped was interrupted. For the most part, it was observed that the dropped packets
came in bursts that corresponded to situations such as vehicles getting temporarily separated from each
other or large obstructions between vehicles blocking line-of-sight signal paths. In addition, the
cumulative number of packets received and dropped plotted on the same time scale shows how the effects
of the lost packets adds up over time and provides an over all percentage of packets received and dropped
over the course of the entire data set. These overall performance statistics include only those packets
transmitted while the receiw:rs of each of the other radio test kits were enabled, so that packets sent to a
radio test kit which was turned off were not counted as dropped packets.
15000
rebrnuN 10000
t
caP 5000
I .... Totai Packets ReceivedPackets Received
°o~ ............ ~- ~o 4’0Time [rnin=Jtes]
15000~ : ~l-PacketsDro~oed
N 10000
50 60 0 10 20 30 40 50Time [minutes]
(A) (B)
Fig. 4 Packets Received By GM-3 From GM-4 and Cumulative Received (A)
(Packets Sent = 15662, Packets Received = 15074, Percent Received = 96.2457)and Packets Dropped From GM-4 and Cumulative Dropped (B) vs. Time
(Packets Sent = 15662, Packets Dropped = 588, Percent Dropped = 3.7543)in Squirrel Hill Neighborhood on 11 November 2004
Second, plots of the speed of each vehicle over the course of time, as shown in Fig. 5, clearly show the
fluctuations in speed along the route. The similarity in the speed of the two vehicles confirms that the
vehicles maintained a fairly consistent distance of separation regardless of speed, as can also be seen on
the GTK RoadMap user interface during the replay of the data log. The effects of vehicle speed on system
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performance are primarily related to Doppler effects and fast-fading, and can be filtered out of the
measured signal strength data for channel modeling purposes as described above; however, they cannot
be physically eliminated from the channel, and therefore contribute to the lost packet behavior. The
relevance of vehicle speed data analysis lies in the significant effects that such fast-fading behavior can
have on the packet error rate of the communications link as discussed later in this thesis.
40
35]
30Pm[ 25
de 2O
PS 15
GM-4 (Transmitter)GM-3 (Receiver)
10
030 35 40 45 50
Time [min]
Fig. 5 Speed of a Typical Radio Test Kit Transmitter (GM-4) And Receiver(GM-3) vs. Time in Squirrel Hill Neighborhood on 11 November 2004
Third, plots of the distance between transmitter and receiver over the course of time, as shown in Fig. 6,
clearly show that the vehicles were able to trans~rnit and receive data over a wide range of distances
throughout the entire duration of the data collection effort. The GTK RoadMap user interface also
provides an excellent view of the relative spacing of vehicles during the replay of the data log. The
transmission range of the radio test kits is limited by the minimum signal strength threshold of each
receiver, and the received signal strength is a function of the transmitted power level and attenuation of
the signals due to the distance between transmitter and receiver.
14
e 160II
0 10 20 30 40 50 60T~me [min]
Fig. 6 Distance Between Transmitter (GM-4) And Receiver (GM-3) vs. in Squirrel Hill Neighborhood on 11 November 2004
The plot of the distance between transmitter and receiver over the course of time provides a great deal of
insight when overlaid with tlhe plots of other system performance metrics which are a function of distance
on the same time scale.
Finally, plots of the signal attenuation over the course of time clearly show the effects of different
scenarios and changes in the environment on signal attenuation. The effects of distance between
transmitter and receiver on the received signal strength become apparent when the plot of the signal
attenuation over the course of time is overlaid with the plot of the distance between transmitter and
receiver over the same time period, a relationship that will be analyzed in much greater detail later in the
thesis. Figure 7 shows that in almost every case, large si~ikes in distance correspond to larger values of
signal attenuation.
Likewise, if this plot was overlaid with the plot.,; of packets transmitted, received, and dropped as a
function of time, the effects of signal attenuation on the loss of data packets would be seen. In that case,
large spikes in signal attenuation would correspond to bursts of dropped packets.
15
250120
110,
100!
9O
80
70
OL
~me [mini
2001n
[
e
150c
100i
Fig. 7 Signal Attenuation and Distance Between Transmitter(GM-2) and Receiver (GM-3) vs.
in Squirrel Hill Neighborhood on 11 November 2004
C, Channel Characterization
As suggested above, overlaying the various plots of system performance variables vs. time shows how the
different variables are closely related to each other. Another excellent way to observe these relationships
is to plot one variable as a function of the other. For example, a plot of signal attenuation vs. distance
between transmitter and receiver as shown in Fig.. 8 provides a different perspective on the same data
presented in Fig. 7. It also shows two distinct things about the nature of the relationship between signal
attenuation and distance - there is a general increase in signal attenuation as the distance increases, and
there is a distribution of signal attenuation data points at each distance - which provide the foundation for
the channel characterization model used in this thesis.
16
10~102
Distance [m]
Fig. 8 Signal Attenuation vs. Distance Between the Transmitter (GM-2) andthe Receiver (GM-4) in Squirrel Hill Neighborhood on 11 November 2004
C. 1 Data Analysis Techniques
The signal attenuation or pallh loss, PL(d), is defined as the difference in signal strength as measured at
the transmitter and the receiver (3) with antenna gains included.
The signal attenuation, PL(d), is commonly modeled using the Log-Distance Path Loss Model (4)
[12,13].
PL(d~_dB]~-fi-~(d,, )- lO’n’log~oZ (4)
In the presence of log-normal shadowing, signal attenuation, PL(d), can be modeled using the Log-
Normal Path Loss Model (5) where Xo is a zero-mean Gaussian distributed random variable (in dB)
standard deviation ~ (also in riB) [13].
X,, = )- lO’n.log,o( d ) + x,,
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If the signal attenuation is measured using logarilhmic units [dB] and the distance is converted from the
linear scale to the logarithmic scale, the modeled path loss will be a straight line with a slope of 10n dB
per decade of distance. The value of the path loss exponent, n, can be calculated using a linear regression
of the empirical data points over a wide range of distances between transmitter and receiver. The standard
deviation, ~, of the zero-me, an Gaussian random variable can be determined using the standard deviation
of the distribution of data points at each distance.
Due to the very large number of data points in each data set, the distribution of all measured signal
attenuation data points at each distance was represented by its mean value when calculating the path loss
exponent, n, for the Log-Distance Model. Since the mean signal attenuation values at each distance were
used in the linear regression to calculate the va]lue of the path loss exponent, n, then E(X~) = 0
definition, and, if d(, = 1, the Log-distance Path Loss Model can be rewritten as (6) where PLO) is a
constant offset value.
-fi-£(d )[dB ]= - l O " n " l°g,o (d )+ -~0 (6)
The channel characterization described above was determined for each transmitter-receiver link in the
following way. First, the range of possible distances between transmitters and receivers was divided up
into one meter segments corresponding with integer distance values, each with a 0.1 meter margin to
either side forming a "bin." Second, at each distance value, all data packets in the matrix of transmitted
and received packet pairs that were received while the nodes were at a distance within the given bin were
identified and counted. Third, the signal attenuation for each packet transmitted over the given distance
was calculated by subtracting the received signal strength from the transmitted signal strength (3). Fourth,
the sum of all signal attenuation values was divided by the total number of packets transmitted at the
given distance to determine the average signal attenuation. Finally, after calculating the average signal
attenuation value for each of’ the distances, the path loss exponent, n, and the constant offset value in the
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Log-Distance Path Loss Model (6) were calculated for each link in the individual data set using a l~rst
order polynomial (linear) regression of the form (7):
y = a0 +t’a~ (7)
in which
y = -fi~(d ldB ] (7.a"
a 0 = |Ogl0 ~0)) (7.b)
a1 = 10 ̄ log,0 (d) (7.c)
(7.d)
After determining the path loss exponent, n, and the constant offset value for each of the links in all of the
individual data sets, a matrix of the distance values., the sum total measured signal attenuation values, and
the sum total number of packets transmitted at each distance was created for each link in the individual
data set, and the data contained in all of these matrices was aggregated into a single data set. The overall
average signal attenuation at each distance was calculated by dividing the sum of the total signal
attenuation values by the sum of the total number of packets transmitted at each distance from all of the
links. The overall path loss exponent, n, and the constant offset value in the Log-distance Path Loss
Model (6) were calculated for all links using the same first order polynomial (linear) regression technique
(7).
C.2 Determination of Path Loss Exponent
The average signal attenuati.c)n [dB] and the number of time averaged measured signal attenuation data
points for vehicles in motion used to calculate that average at each distance are shown in Fig. 9 using the
same logarithmic distance (horizontal) axis. The average signal attenuation data points over the range
19
distances from 8 - 80 meters appear to be very linear as expected, however, the data points outside of this
range of distances appear to be very scattered.
u
tt
t
taD
fo
uN
100
110
120
100
15 O0
1000
500
~o’ (A) ~o’Number of Data Points Available vs. Distance
10t 102
Distance [m]
(B)
Fig. 9 Average Signal Attenuation (A) and Number Data Points Available (B) vs. Distance
A comparison of the two plots in Fig. 9 clearly indicates that the majority of the measured signal
attenuation data points were logged in the same range of distances over which the average signal
attenuation data points appear very linear - the range of 8 - 80 meters. This suggests that the average
signal attenuation data points outside of this range may be scattered due to an average value calculated
using an insufficient number of valid data points.
It was therefore concluded that the linear regression calculation used to calculate the path loss exponent
would be far more accurate if it was limited to the .average signal attenuation data points calculated using
no less than 700 measured signal attenuation data points and all other average signal attenuation values
calculated using a number of data points below this confidence threshold were excluded. In addition, it
was concluded that the linear regression would be more accurate if the average data points calculated
using greater numbers of measured values were weighted more heavily than those calculated using fewer
data values.
20
The weighting of average signal attenuation values was accomplished using the following technique.
First, the "weight" was defined as the total number of measured signal attenuation data points at each
distance divided by one fourth of the minimum number of data points specit]ed above and rounded up to
the nearest integer value. This definition of the "weight~’ given to each average signal attenuation data
point was arbitrarily derived from the need to reduce the number of total data points used in the linear
regression from several thousand to several hundred while providing enough resolution that the weighting
effect was significant. Second, the average signal attenuation value corresponding to each distance was
placed in the array of average signal attenuation values the "weight" number of times. Finally, the
distance value was placed in the array of distance values the "weight" number of times such that there
was one distance value in the distance array for each of the corresponding "weight" number of average
signal attenuation values int the average signal attenuation array. Having made these changes to the
average signal attenuation and distance arrays, the linear regression took into account a "weighted"
number of identical average signal attenuation data points at each distance value.
A plot of the average signal attenuation data and the Log-distance Path Loss Model as a function of
distance and the path loss exponent n = 1.0269, as determined using this weighting technique is shown in
Fig. 10. The model appears to be an excellent fit to the average signal attenuation data points over the
range of distances from 8 - 8.0 meters as was expected.
21
[
on80I
it 85 ~
u 90 ~
et 95~tA 100~
a 105
$ ~115~
120i ~ Average Signal AttenuationLog-Distance Model (~ = 1.0269)
100 10~ 102Distance Ira]
Fig. 10 Mean Signal Attenuation and Propagation Model(With Weighting and a Minimum of 700 Data Points) vs. Distance
In the absence of an "urban canyon" or other crude waveguide that would guide the signal along a certain
path, most typical applications of mobile wireless communications will result in a path loss exponent
greater than that of free space, n = 2. Table 1 gives examples of path loss exponents for six environments,
including free space [13], none of which are as low as the value calculated using the empirical data.
Environment Path Loss Exponent, n
Free Space 2
Urban Area Cellular Radio 2.7 - 3.5
Shadowed Urban Cellular Radio 3 - 5
In Building Line-Of-Sight 1.6 - 1.8
Obstructed in Building 4- 6
Obstructed in Factories 2- 3
Table 1" Typical Path Loss Exponents in ]Mobile Radio Environments (Rappaport [13])
22
X. Zhao, et al. used the same Log-Distance Path Loss Model (6) to characterize wideband outdoor mobile
communications signal propagation at 5.3 GHz and found that the path loss exponent was approximately
n = 2.5 in line-of-sight scenarios and n = 3.4 in non-line-of-sight scenarios in suburban environments [4].
Likewise, T. Schwengler and M. Gilbert found that he path loss exponent was approximately n = 2.0 in
line-of-sight scenarios and n = 3.5 in non-line-of-sight scenarios using 5.8 GHz in residential
neighborhoods [5].
In line-of-sight scenarios in urban environments, X. Zhao, et al. did find the path loss to be as low as n =
1.4 [4], however, as stated earlier, the data presented in this thesis was collected in residential suburban
environments characterized by one or two story buildings and residential streets, all of which are
relatively wide-open areas with very limited capability to "confine" the signals to the path taken by the
vehicles. Taking into account that fact that line-of-sight transmissions were at least partially blocked in
many situations, the path loss exponent ofn = 1.0269 is puzzling since all theoretical calculations indicate
that the path loss exponent should be at the very least n = 2 or more likely closer to n = 3.
C.3 Effects of Data Point Distribution "Clipping" on Data Analysis Results’
In an effort to understand the unusually low path loss exponent, the distribution of the signal attenuation
data points was evaluated at ’various distances between the transmitting and receiving nodes.
The distribution of signal attenuation data points for any given distance was determined using the
following technique. First, all packets transmitted from Radio Test Kit A and received by Radio Test Kit
B within a given data set were combined with all packets iransmitted from Radio Test Kit B and received
by Radio Test Kit A within the same data set, forming an aggregate data matrix accounting for two-way
packet transmission between the two radio test kits. Second, a distance "bin" was defined as the one meter
interval centered about the user specified distance with half meter spacing to either side. A search was
then conducted of all packets in the given data ~natrix, identifying all data packets transmitted over a
distance within the distance bin. Third, the maximum and minimum signal attenuation values were
determined and specified a’~; the limits of the range of signal attenuation values at which packets were
received. This range of signal attenuation values was then divided up into one dB segments corresponding
with integer signal attenuation values with a half dB margin to either side, forming a signal attenuation
bin. Fourth, a count was made of all data packets within the distance bin described above whose received
signal had experienced a signal attenuation whose value was within each of the given signal attenuation
bins. Fifth, the total number’ of packets received with each signal attenuation value at the given distance
for each transmitter-receiver pair were summed together one attenuation increment at a time, forming the
total distribution of signal attenuation data points at that given distance for the given individual data set.
Finally, the total distributions for all of the individual data sets at the given distance were added together
one attenuation increment at a time, forming the overall total distribution of signal attenuation data points
as is shown in Fig. 11.
ni 300 --o
a 250
D 20~
o
~50r
bIn 100uN
5O
60 70 80 90 100 110 120Signal Attenuation1 [dB]
Fig. l I Number of Data Points Available vs. Signal Attenuation at a Distanceof 20 Meters (A) (Mean = 87.3027 / Standard Deviation = 11.4055)
When all of the distributions were plotted using the same signal attenuation (horizontal) axis, it was
observed that the mean value of the measured signal attenuation data point distributions increased with
increasing distance, as was expected. It was also observed that the distributions became significantly
24
distorted as the distance and signal attenuation wLlues increased. It was concluded that the distortion of
the data point distributions was directly linked to the receiver sensitivity of the radio tests. To evaluate the
effects of receiver sensitivity on the data distributions, they were then regenerated using received signal
strength values rather than signal attenuation values as shown in Fig. 12. As was the case with the signal
attenuation data point distributions, at relatively small distances, the distribution of data points shown in
Fig. 12 (A) appears to be complete, however, at greater distances, it appears as if the data point
distribution shown in Fig. 12 (B) is truncated at the lower end of the range of received signal strength
values.
t
300 [oP
250 a 25ta
i ~20(] 20
f0
150 ~ 15re
~i b
100 ~m 10uN
0-40 -50 -60 -70 -80 -90 -40 -50 -60 -70 -80
Received Signat Strength [dE]m] Received Signal Strength [dBrn]
(A) (B)
-9O
Fig. 12 Number of Data Points Available vs. Received Signal Strength at a Distanceof 20 Meters (A) (Mean = -66.3072) and 70 meters (B) (Mean = -73.0225)
The fact that the distributions are distorted at low received signal strength values due to missing packets
indicates that packets arriving at the radio test kit with signal strengths below the minimum threshold of
the receiver have been dropped, effectively "clipping" off the lower end of the distribution. This idea is
validated by referring back to the receiver calibration plot of received signal strength [dBm] vs. "RSSI"
index value shown in Fig. 3. It can clearly be seen that the various radio test kits simply do not receive
packets received with signal strengths falling below a certain threshold, the lowest of which is about -90
dBm.
25
If the portion of the distribution corresponding to lower signal attenuation values is missing data points,
the mean value of the distribution will be artificially raised. Artificially high mean signal attenuation
values would cause the slope of the regression line to be artificially shallow, effectively decreasing the
calculated value of the path loss exponent.
To demonstrate this phenomenon, a Monte-Carlo style simulation was created using normal distributions
of randomly generated numbers. First, the mean values for the simulated signal attenuation data point
distributions at each distance from ten meters to one hundred meters in ten meter increments were
calculated using the Log-Distance Path Loss Model described above (6), given the theoretical free-space
path loss exponent of n = 2 and an offset value of 65. The offset value of 65 was chosen due to the fact
that the calculated path loss values are very comparable to those shown over the same range of distances
in the empirical data plots such as Fig. 10. Second, the standard deviations for the signal attenuation data
points were calculated using the empirical signal attenuation distribution data in order to make the
simulation as realistic as possible. Third, a distribution of 5000 data points was randomly generated for
each distance, given the mean and standard deviations. Fourth, a new data point distribution was created
in which all signal attenuation values in each randomly generated distribution above the arbitrary
threshold of 115 dB were thrown out. The arbitrary threshold of 115 dB was chosen due to the fact that
there does not appear to be any packets received with a signal attenuation in excess of 115 dB in the plot
of measured signal attenuation values as a function of distance between the transmitter and the receiver as
shown in Fig. 8, and there are no average signal attenuation values in excess of 115 dB as shown in Fig.
10. Finally, the mean of botl~t the randomly generated and "clipped" distributions was determined and the
same linear regression technique described above (7) was used to calculate the simulated path loss
exponent for each case. Figure 13 shows the results of this simulation and clearly indicates that the path
loss exponent was artificially lowered by 0.291 as a result of "clipping off" all received signal strength
data points below the given receiver sensitivity threshold.
26
Average ValueLinear Regr ( n = 2.0034)Clip Average ValueClip Linear Regr (n = 1.7124)
[
n 80 ~--~
oit
une 90 i
g 100
S
10~ 102
Distance [m]
Filg. 13 Average Values and Linear Regression Linevs. Distance for Monte-Carlo Simulation
Techniques such as the Expectation Maximization (EM) Algorithm [14,15] make it possible
mathematically fill in the missing portion of a data point distribution if the type of distribution is known.
Although this simulation demonstrates that incomplete data point distributions introduce significant error
into the simulated path loss exponent calculation, the amount of error determined by the simulation does
not account for the entirety of the error in the path loss exponent calculation of the empirical data.
C.4 Determination of Standard Deviation of Gaussian Random Variable
As previously stated, a plot of all signal attenuation data points vs. distance between transmitter and
receiver shows that there is a distribution of signal attenuation data points at each distance. The Log-
Normal Path Loss Model (5) accounts for these data point distributions by including the zero-mean
Gaussian distributed random variable, Xo, with standard deviation, ~.
The standard deviation, ~, of the zero-mean Gaussian distributed random variable, X~, was determined in
the following way. First, all of the fast-fading effects were removed from the data using the time
averaging technique described above. This is important due to the fact that only the large-scale fading
27
effects can be accurately modeled using a Gaussian distribution - the fast-fading effects yield different
types of distributions not included in the Log-Normal Path Loss Model. Second, all signal attenuation
data points in which both the transmitter and the receiver were stationary (zero velocity) were discarded.
This was necessary due to the fact that the distributions have large spikes at attenuation levels
corresponding to the distances of separation between stationary vehicles. The distribution of data points
shown in Fig. 14 was created using the same set of data points used to create the distribution of data
points shown in Fig. l I above, however, the time averaging technique was used to remove the last-fading
effects and all data points corresponding to stationary vehicles were removed. Note that the large spike at
about 82 dB of attenuation has been removed and the irregular bump at about 110 dB has been smoothed
out.
n
oi 180IP 160~
at ~4oL
fo 100~
b
N
050 60 70 80 90 100 110 120
S~gnal Attenuation [dB]
Fig. 14 Number of Time Averaged Data Points AvailableCorresponding to Vehicles in Motion vs. Signal Attenuation at a Distance
of 20 Meters (A) (Mean = 87.0275 / Standard Deviation = 9.5449)
Third, the distribution of measured signal attenuation data points was evaluated using this technique at 5
meter intervals over the range of distances from 5 meters to 100 meters as shown in Fig. 15.
11.5
o
1a 11
v
D 10.5
d
9.5
9 ~0 20 40 60 80 100
D~st a nce [m]
Fig. 15 Standard Deviation of Time Averaged Data PointsCorresponding to Vehicles in Motion vs. Distance
Finally, the standard deviation of each of these distributions were averaged, yielding the overall standard
deviation, ¢~, of the zero-mean Gaussian distributed random variable, Xo. The mean value of each of these
distributions is accounted for using the Log-Distance Path Loss Model as discussed earlier in this thesis.
With the exception of those at 5 and l0 meters, the standard deviation data points appear to be fairly
consistent at all distances out to 100 meters. As wa,.s the case when evaluating the Log-Distance Path Loss
Model above, the number of measured signal attenuation data points at 5 and 10 meters is sufficiently
small to cast doubt on the validity of the calculations of both mean and standard deviation. If the standard
deviation of the distributions at 5 and 10 meters are ignored, the average standard deviation at distances of
15 to 100 meters is c~ = 9.4321 dBm. T. Schwengler and M. Gilbert had similar results, finding that he
standard deviation was approximately ~ = 6.9 in line-of-sight scenarios and c~ = 9.5 in non-line-of-sight
scenarios using 5.8 GHz in residential neighborhoods [5]. Likewise, G. Durgin, et al. found that the
standard deviation outside of homes was ~ = 8.0 [6].
D. Link Reliability
29
Another excellent example of the close relationship between the various system performance variables is
that of received and dropped packets as related to the distance between transmitter and receiver or other
such variables. A plot of received and dropped packets vs. distance between transmitter and receiver
clearly as shown in Fig. 16 shows that there tends to be a larger number of packets dropped at greater
distances, however, it also shows that packets are both received and dropped at all distances.
m 20~
c 1~n
~taD 100
C I~0 ~
t
5000 10000 15000 0 5000 10000 15000Packet Rlurnber Packet Number
(A) (B)
Fig. 16 Packets Transmitted From GM--4 Intended For GM-3 But Dropped (A) andPackets Transmitted From GM-4 And Received By GM-3 (B) vs. Distance
in Squirrel Hill Neighborhood on 11 November 2004
This relationship is best analyzed using packet error rate statistics. The determination of link reliability is
based upon an evaluation of packet error rates as a function of distance, relative speed, and absolute
speed. The evaluation of packet error rates as a function of received signal strength is not a good
indication of link reliability at this time due to the fact that it is nol yet possible to accurately estimate the
received signal strength of dropped packets.
D. 1 Data Analysis Techniques
The packet error rate as a function of distance was determined in the following way. First, the range of’
distances was divided up into one meter segments corresponding with integer distance values with a halt"
.30
meter margin to either side, forming a "bin." Second, a count was made of all data packets that ’were
received while the nodes were at a distance within the given bin. Third, a similar count was made o,f all
data packets that were dropped at a distance within the given bin. Finally, the packet error rate (PER) was
calculated for each distance by dividing the number of packets dropped by the total number of packets
sent(8).
num_ dropPER = (8)nurn _ drop + num_ rec’ d
A very similar technique was used to determine the packet error rate as a function of absolute and relative
speeds, dividing the range of speeds into a series of bins and counting the number of packets received and
dropped at each speed interval.
D.2 Determination of Link Reliabili~
The measured packet error rate as a function of distance is shown in Fig. 17. The packet error rate is less
than 0.01 out to about ten meters, after which it steadily increases to about 0.1 at 100 meters. The origin
of the PER peak between 10 and 20 meters is not presently understood.
10-2
0 20 40 60 80 1 O0D~stance [m]
Fig. 17 Packet Error Rate vs. Distance
.31
Figure 18 shows the packet error rate as a function of relative speed between the transmitting and
receiving node. The PER is generally less than ().1, and does not show a strong variation with re|ative
speed.
0.05
-20 -1O 0 10 20 30Relative Speed [mileeJhour]
Fig. 18 Packet Error Rate vs. Relative Speed
Finally, Fig. 19 shows that the packet error rate is also thirly flat with values typically of less than 0.1 at
all absolute speeds, suggesting the Doppler effiects do not have a significant influence on system
reliability.
0.2-~
0.15
o.1
01)5
0 5 10 15 20 25 30Absolute Speed [MPH]
Fig. 19 Packet Error Rate vs. Absolute Speed
The negative "clipping" effect on the distribution of received packet data points as described above can
also be seen in packet error rate analysis. If the data points that are "clipped" in the Monte-Carlo
32
simulation are considered "dropped packets" and the packets that are kept in the "clipped" distribution are
considered "received packets," the "packet error rate" for the simulation can be evaluated just as if it was
empirical data. If the "packet error rate" for the simulated data is plotted over the packet error rate for the
empirical data shown in Fig. 17 as is shown in Fig. 20, distinct similarities between the two curves can
clearly be seen, suggesting that a significant portion of the lost packets were lost as result of this
"clipping" effect.
rorr 10"~
t
p 10=
Empidcal Data -
Monte-Carto Simulation
2~0 40 60 80 1 O0Distance [m]
Fig. 20 Packet Error Rate of Empirical Data andMonte-Carlo Simulation Data vs. Distance
VI. CONCLUSIONS
Careful analysis of the wireless communications channel between the nodes of the vehicular ad hoc
network suggests that the channel can be characterized by the Log-distance Path Loss Model using a path
loss exponent of n = 1.03 and a standard deviation of cy = 9.43 dBm. Unfortunately, this path loss
exponent value is significantly less than theoretical calculations suggest it ought to be. Efforts are under
way to improve data analysis techniques to account for this discrepancy between empirical data and
theory. Also, performance verification measurements made on the test kit receivers after these data were
collected showed erratic signal strength readings. Although the equipment was believed to be functioning
33
correctly during the course of these measurements, equipment malfunction cannot be eliminated as a
possibility at this time.
The evaluation of the packet error rates of the individual links suggests that such links are about 90%
reliable at speeds less than 48 KPH (30 MPH) and distances not in excess of 100 meters in residential
suburban neighborhoods. Figs. 18 and 19 further suggest that there are not any significant Doppler effects
on the packet error rates. The acceptability of the packet error rates is best defined by the tolerance of the
given application.
VII. FUTURE WORK
In spite of the fact that the channel characterization findings were negative, they provide a firm
foundation for future research work. The hardware used in these experiments is no longer in use and new
hardware has been provided to continue the research effort. Once the new hardware is integrated into the
experimental platform, it can be calibrated just as described above, and all of the data analysis tools and
techniques developed and used during this research effort can be used again for future work. If the new
hardware configuration produces similar results, additional work must be done to refine the data analysis
techniques, including careful assessment of the data point averaging techniques, the linear regression
tools, and the weighting of the data points.
One of the challenges that ought to be addressed in the future is the observation that packets transmitted
over greater distances are much less likely to be received, thus creating a much smaller sample size of
received packets at these distances. Additional data ought to be collected at or near the observed
transmission distance limits to increase the size of the data sample, thus increasing the confidence in the
channel characterization and link performance determinations at greater distances. Additional
experiments could be run in which the lost packets are identified and associated with the worst-case SNR
and RSSI values. In such a case, the path loss exponent derived from this experiment would likely serve
as an "upper bound" rather than an absolute value as determined in this thesis.
In addition, future work ought to include the evaluation of various baseline cases in controlled
environments. For example, similar data measurements ought to be taken between vehicles at constant,
known distances and speeds in static and uncluttered environments. A careful evaluation of the channel
characterization and link performance in these "ideal" conditions can then be used as a baseline for
comparison with the unpredictable, dynamic, and cluttered environments common to suburban driving
conditions. Any major variations from this baseline would likely correspond to unique characteristics of
the given suburban driving environment and may explain the findings of this thesis.
35
REFERENCES
[1] R. Mangharam, J. Meyers, R. Rajkumar, D. Stancil, J. Parikh, H. Krishnan, and C. Kellum, "A Multi-hop Mobile Networking Test-bed for Telematics," SAE International, 2005.
[2] K.A. Redmill, M.P. Fitz, S. Nakabayashi, T. Ohyama, F. Ozguner, U. Ozguner, O. Takeshita, K.Tokuda, and W. Zhu, "An Incident Warning System with Dual Frequency Communications Capability,"IEEE Intelligent Vehicles Symposium Proceedings, pp. 552 - 556, 2003.
[3] J. Maurer, T. F/igen, and W. Wiesbeck, "Narrow-Band Measurement and Analysis of the Inter-Vehicle Transmission Channel at 5.2 GHz," 55th IEEE Vehicular Technology Conference Proceedings’,pp. 1274 - 1278, Spring 2002.
[4] X. Zhao, J. Kivinen, P. Vainikainen, and K. Skog, "Propagation Characteristics for WidebandOutdoor Mobile Communications at 5.3 GHz," lEEk," Journal on Selected Areas in Communications, Vol.20, No. 3, April 2002.
[5] T. Schwengler and M. Gilbert, "Propagation Models at 5.8 GHz - Path Loss & Building Penetration,"IEEE Radio and Wireless Conference (RA WCON) 2000 Proceedings, Denver, CO, September 2000.
[6] G. Durgin, T. Rappaport, and H. Xu, "Measurements and Models for Radio Path Loss andPenetration Loss In and Around Homes and Trees at 5.85 GHz," IEEE Transactions on Communications,Vol. 46, No. 11, November 1998.
[7] A. Visser, H. H. Yakali, A-J. van der Wees, M. Oud, G.A. van der Spek, and L.O. Hertzberger, "AHierarchical View on Modeling the Reliability’ of a DSRC Link for ETC Applications," IEEETransactions on Intelligent Transportation Systems, Vol. 3, No. 2, June 2002.
[8] M. Torrent-Moreno, D..liang, and H, Hartenstein, "Broadcast Reception Rates and Effects of PriorityAccess in 802.1 l-Based Velhicular Ad-Hoc Networks," International Conference on Mobile Computingand Networking - Proceedings of the First ACM Workshop on Vehicular Ad Hoc Networks’, pp. 10 - 18,Philadelphia, PA, October 2004.
[9] J. Yin, T. E1Batt, G. Yeung, B. Ryu, S. Habermas, H. Krishnan, and T. Talty, "PerformanceEvaluation of Safety Applications over DSRC Vehicular Ad Hoc Networks," International Conference’ onMobile Computing and Networking - Proceedings of the First ACM Workshop on Vehicular Ad HocNetworks’, pp. 1 - 9, Philadelphia, PA, October 2004.
[10] C.E. Perkins and E.M lVloyer, "Ad Hoc On-Demand Distance Vector Routing," Proceedings of the2~d IEEE Workshop on Mobile Computing Systems" and Applications, pp. 90-100, New Orleans, LA,
February 1999.
[ll] U.S. Census Bureau Geographical Systems FAQ, "Q5.1: What is the best way to calculate thedistance between 2 points?," http://www.census.~ov/c~i-bin/~eo/jzisfiaq?Q5.1, 2001.
[12] D.C. Cox, R.R. Murray, A.W. Norris, "800 MHz Attenuation Measured in and around SuburbanHouses," A T& T Bell Labs l’echnical Journal, Vol. 63, pp. 921-954, July/August 1984.
36
[| 3] Rappaport, Theodore S., Wireless Communications" Principles and Practice, 2nd ed., Upper SaddleRiver, NJ: Prentice Hall PTR, 2002.
[14] A. Dempster, N. Laird, and D. Rubin, "Maximum likelihood from incomplete data via the EMalgorithm," Journal of the Royal Statistical Society, Series B, 39(1), pp. 1-38, 1977.
[15] G. McLachlan, and T. Krishnan, The EMAlgorithm and Extensions, New York: John Wiley & Sons,Inc., 1997.
37
APPENDIX A
Transmitter Calibration Techniques
Before the transmission power could be calibrated, Daniel Weller had to write a special "TXTest"
program which allowed the.. user to easily change the "ForcePower" settings, reinitialize the network,
transmit packets from the transmitting radio test kit to the receiving radio test kit for a specified period of
time, and then repeat the process for the full range of power settings.
The power [dBm] transmitted at a given "ForcePower" setting for the various radio test kits was
calibrated using the following technique. First, a radio test kit other than the one whose transmission
power was to be calibrated was selected to serve as the "server" radio test kit (receiver) and M/A-Com
ground-plane mount 802.1 l a radio antenna was connected to the antenna port. Second, the GTK
RoadMap program was started up on the receiver radio test kit to initialize the radio card, after which the
RoadMap graphical user interface was closed and IPERF Version 1.7.0 was started up to coordinate
packet transmission over the wireless communications link with the transmitting radio test kit. Third, a
Pomona Electronics BNC-C-36 Alpha Wire-J P/N 9058C RG 58C/U cable, in series with a Hewlett
Packard 8473C (0.01-26.5 GHz STD) Detector (Serial 1822A: 03039) and a RLC Electronics A-41-10-R
10 dB attenuator (8521), was used to link a Hewlett Packard 54645D (100 MHz 2+16 channel) Mixed
Signal Oscilloscope to the antenna port of the "client" radio test kit whose transmission power was to be
calibrated as shown in Fig. A-I. The antenna from the receiver test kit was then placed near the radio card
of the radio test kit whose transmission power was being calibrated to capture the signal leakage and
complete the wireless communications link between the two test kits. Fourth, the client (transmitter) and
server (receiver) IP addresses were specified in the TXTest program script. Fifth, the starting and ending
"ForcePower" values were specified as the TXTest program was started up on the transmitter radio test
kit, creating a transmitted packet signal waveform on the oscilloscope screen. Sixth, the time (horizontal)
38
and voltage (vertical) axes on the oscilloscope screen were adjusted so that the individual packets in the
signal trace filled the screen and could be easily distinguished.
Fig. A-1 Diagram of Transmitter Calibration Equipment Set Up - "Client’" RadioTest Kit (A), "Server" Radio Test Kit (B), 5.8 GHz Antenna (C), Attenuator
Signal Detector (E), Signal Generator (F), Oscilloscope
Seventh, one cursor was set at the DC (zero power) level and a second cursor at the center of the power
level of the transmitted packet signal on the oscilloscope screen. The TXTest program could be run as
many times as was necessary to determine the power level of the transmitted packet signal and single
sweeps could be used on the oscilloscope as was necessary to get a good screen shot. Eighth, the
attenuator and cable were disconnected from the antenna port of the radio test kit once the TXTest
transmission period timed out and connected to the RF output port of an Agilent E8251A 250 kHz- 20
GHz PSG - A Series Signal Generator. Ninth, the signal generator was set to generate a square pulse with
a width of approximately 125 gsec, a period of twice that, and a frequency of 5.:8 GHz (the carrier
frequency of the radio test kits). Tenth, the amplitude of the generated pulse, measured in terms of power
[dBm], was adjusted to the point where it exactly matched the amplitude of the transmitted packet signal
as defined by the set of cursors. Finally, the power level of the generated pulse was recorded and the cable
was switched back to the radio test kit for the next iteration.
39
This procedure was repeated once for each "ForcePower" index value, creating a single calibration data
set. After taking five complete calibration data sets, the measured power values [dBm] for each
"ForcePower" index value were averaged, yielding a calibration table of "ForcePower" index values and
the corresponding as overall average power values [dBm] shown graphically in Fig. 2.
40
APPENDIX B
Receiver Calibration Techniques
The received signal strength measurements for each radio test kit were calibrated using the following
technique. First, the antenna port of the "server" radio test kit whose received signal streng*h was to be
calibrated was linked directly to the antenna port of the "client" (transmitter) radio test kit using SMA
cables in series with an ARRA, Inc. Model 4684-20C variable attenuator and various known
combinations of an RLC Electronics A-41-10-R 10 dB attenuator (8521 ), RLC Electronics A-41-20-R
dB attenuator (8521), Pasternack PE7047-10 10 dB attenuator, and Pasternack PE7047-20 20
attenuator calibrated at 5.8 GHz as shown in Fig. B-1.
A
Fig. B-1 Diagram of Receiver Calibration Equipment Set Up - "Client" RadioTest Kit (A), "Server" Radio Test Kit (B), Fixed Attenuator(s) (C),
Variable Attenuator (D)
Second, the GTK RoadMap program was started up on both the transmitter and receiver radio test kits
with the vehicle driving scenario data simulation feature enabled. Third, the ’°ForcePower" transmitted
power setting of the transmitter and the level of attenuation in the direct link between the two radio test
kits were set in such a way that the variable attenuator was at the low end of the dynamic attenuation
range and the received signal strength measurements at the antenna port of the receiver radio test kit were
at the upper end of the "RSSI" index scale. Fourth, the GTK RoadMap program was restarted on the
receiver radio test kit with both the vehicle driving scenario data simulation feature and the logging
feature enabled after the total attenuation in the direct link between the two test kits and the ~’ForcePower"
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transmission power index of the transmitter were. manually recorded. Fifth, the transmission of packets
from transmitter and receiw~r radio test kits was started by activating the client function on the transmitter
and the server function on the receiver. Sixth, the transmission of packets from transmitter and receiver
radio test kits was stopped by deactivating the server function on the receiver after running for
approximately forty-five seconds. Seventh, the attenuation of the variable attenuator was increased by one
increment and the transmission of packets from transmitter and receiver radio test kits was restarted (no
less than twenty seconds after being stopped) by reactivating the server function on the receiver,
beginning the next iteration of logged measurements.
This procedure was repeated until the variable attenuator reached the upper end of its dynamic attenuation
range. At this point, the log file was saved, the transmitted "ForcePower" power setting and level of
attenuation in the direct link between radio test kits were set to levels which resulted in "RSSI" values at
or just slightly above the level last measured, the new transmission power and attenuation levels were
manually recorded, a new log file was created by restarting the GTK RoadMap program on the receiver
radio test kit with both the vehicle driving scenario data simulation feature and the logging feature
enabled, and the measurement procedure was continued as before. Once the attenuation was increased to
the point that the received signal strength fell below the minimum received signal strength threshold of
the radio card and the radio test kit no longer received data packets, the log was saved and the test kits
were shut down.
The data log files were then downloaded and the following procedure was used to determine the average
received signal strength value corresponding to each "RSSI" index value. First, the two complete data sets
corresponding to the upper and lower halves of the range of"RSSI" index values for each radio test kit
were individually parsed and placed into raw data matrices containing the packet number, the radio test
kit ID number, the time stamp, and the "ForcePower" or "RSSI" index value for each packet. Second, all
data in each raw data matrix corresponding to packets transmitted from that radio test kit was discarded,
leaving only the data corresponding to received packets. Third, each raw data matrix was restructured into
a new data matrix containing the variable attenuator setting and the packet number, radio test kit
number, time stamp, and "RSSI" index value con’esponding to each packet received at the given signal
attenuation level. Fourth, the value of each of the variable attenuator settings was replaced in the new data
matrix with the calibrated attenuation values [dB] corresponding to each setting. Fifth, all packets
corresponding to each of the possible "RSSI" index values were located in the given data matrix.
variable attenuator attenuation values [dB] corresponding to all packets received at each given "RSSF’
index value were averaged, which yielded an array of mean attenuation values for the variable attenuator
corresponding to each "RSSi" index value. This process was repeated for each of the two data sets. Sixth,
the value of the known fixed attenuation for the given data set was added to the array of mean attenuation
values for the variable attenuator for each of the two data sets. Seventh, the array of total attenuation
values [dB] corresponding to each of the "RSSI" index values was subtracted from the known transmitted
power value [dBm], yielding an array of received power values [dBm]. Finally, the two data sets of date
were combined together, yielding a calibration table of "RSSI" index values and the corresponding
received signal strength for each as shown graphically in Fig. 3. In the cases where the data contained
within the two data sets overlapped, an average value for the received signal strength was calculated by
summing the product of the received signal strength value and the number of data points used to
calculated that value for the given "RSSI" index value in each of the two data sets and dividing it by the
sum of number of data point’s used to calculated the received signal strength values for the given "RSSI"
index value in both of the data sets.
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