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Performance Measurement of 802.11a Wireless Links from UAV to Ground Nodes

with Various Antenna Orientations

Chen-Mou Cheng

Pai-Hsiang Hsiao

H. T. Kung

Dario Vlah

{doug, shawn, htk, dario}@eecs.harvard.edu

Division of Engineering and Applied Sciences

Harvard University

Cambridge, MA 02138

ABSTRACT

We report measured performance of 802.11a wireless links

from an unmanned aerial vehicle (UAV) to ground stations.

In a set of field experiments, we record the received signal

strength indicator (RSSI) and measure the raw link-layer

throughput for various antenna orientations, communication

distances and ground-station elevations. By comparing the

performance of 32 simultaneous pairs of UAV and ground

station configurations, we are able to conclude that, in order

to achieve the highest throughput under a typical flyover UAV

flight path, both the UAV and the ground station should use

omni-directional dipole (as opposed to high-gain, narrow-

beam) antennas positioned horizontally, with their respective

antenna null pointing to a direction perpendicular to the UAV’s

flight path. In addition, a moderate amount of elevation of the

ground stations can also improve performance significantly.

I. INTRODUCTION

We envision that in the future, low-flying UAVs could

provide a cost-effective wireless networking means for ground

devices. Such UAV-based wireless networking could have a

number of advantages, including (1) that UAVs can provide

on-demand, high-quality communication due to line-of-sight

signal propagation; (2) that UAVs can be sensing and data-

fusion nodes dynamically deployable in the region of interest;

(3) that UAVs can tailor their flight paths to enhance the quality

of wireless networking and communication; and, finally, (4)

that UAVs can themselves carry and forward huge amounts

of data, e.g., gigabytes of terrain images or databases. With

these capabilities, a UAV-based network can, for example,

provide high-speed transport of multimedia data (e.g., videos

and images) for ground nodes and overcome environmental

shadowing effects caused by blocking structures such as

mountains and tall buildings.

The UAV-based networking approach has become especially

attractive in the recent years due to the availability of low-cost,

This material is based on research sponsored by Air Force Research

Laboratory under agreeement numbers FA8750-05-1-0035 and FA8750-06-2-

0154. The U.S. Government is authorized to reproduce and distribute reprints

for Governmental purposes notwithstanding any copyright annotation thereon.

The views and conclusions contained herein are those of the author and

should not be interpreted as necessarily representing the official policies,

either expressed or implied, of Air Force Research Laboratory or the U.S.

Government.

Commercial Off-The-Shelf (COTS) wireless equipment, such

as IEEE 802.11 wireless LAN (“WiFi”) [4]. For example, by

integrating compact, 802.11 wireless equipment into a small

94-inch wing-span UAV, we can readily create a powerful

networking node in the air [8].

In this paper, we address the issue of configuring 802.11

antennas in UAV-based networking. It is well-known that

antenna types (e.g., omni or directional) as well as their

positions and orientations can greatly affect the performance of

wireless links [2], [3]. In addition, when a UAV communicates

with ground nodes, we need to consider ground effects (such

as interference from reflected signals, modeled by two-ray

propagation [6]). Given the large number of complicated issues

involved, it is essential that we conduct field experiments in

order to understand the performance impact of various antenna

configurations at the application level.

We have instrumented a UAV and several ground nodes

with two types of 802.11a antennas in various orientations.

Using this equipment, we have conducted a set of flight tests

to measure their raw link-layer throughput performance in the

field. In this paper, we report and analyze our findings from

these flight tests (Sections II, III). Furthermore, we report

the measured received power as a function of communication

distance and their correlation (Section IV). These results

provide baseline performance information on 802.11a wireless

links for UAV and ground node communication. They can be

useful for future work in UAV networking, such as antenna

selection strategies and multi-hop wireless networking.

II. FIELD EXPERIMENT SETUP

Our networking testbed consists of a UAV node and several

ground nodes, all equipped with 802.11a wireless devices. The

ground nodes were placed on a line, with about 6 ft separation

between the two end nodes. For these nodes, we used two

types of single-board computers, made by Thecus and Soekris.

Our UAV is based on the Senior Telemaster model [7]. We

conducted our flight experiments at a private airfield in Wood-

stock, Connecticut. In these experiments, we used Atheros-

chipset-based Wistron CM9 802.11a/b/g adapters, with 18dBm

transmit power and channel 56 in the 802.11a band.

We encountered a serious problem where our single-board

computer would interfere with the 72MHz R/C receiver on the

airplane, possibly due to noise from the 66MHz system bus.

Proc. 15th International Conference on Computer

Communications and Networks (ICCCN 2006),

October 9-10, 2006

Fig. 1.

A U.S. Geological Survey (USGS) satellite map showing the location of an elevated ground node (N), node 3 in Figure 5,

and a sample UAV path in a fly test. The horizontal, light-colored band under the UAV node (U) is the airport runway, which is

approximately 25 yards wide. The dots show the positions reported by the GPS once a second.

We solved the interference problem by doing three things:

(1) moving the R/C receiver to the back of the airplane, (2)

shielding the box hosting the single-board computer with metal

screen wrap, and (3) moving the computer on/off switch and its

wire—which was radiating the board noise—into the shielded

enclosure.

We used two types of antennas on both the UAV and

ground nodes. One was a 7-dBi, 2.4/5 GHz dual-band, omni-

directional antenna purchased from a commercial vendor

(Netgate), and the other was a custom 2-dBi dipole antenna.

Samples of these antennas are shown in Figure 2. The key

difference between these two antenna types is that the Netgate

antenna produces an omni-directional beam that is much

narrower in the vertical direction than the dipole, as can be

seen from the manufacturer’s radiation pattern plot in Figure 3.

The UAV was equipped with two wireless adapters, each

with two antennas. The UAV would broadcast data packets

using its four antennas in a round-robin manner. Each ground

node was equipped with two wireless adapters, each with one

antenna. Both adapters of a ground node could simultaneously

receive UAV’s packets. One of the four ground nodes was

mounted on the top of a 14-ft wooden pole. Later in this

section we will describe the antenna configurations and traffic

patterns in detail.

The UAV flew approximately at 50-yard altitude and at

40 miles per hour over the ground nodes. The UAV had an

on-board GlobalSat BU-353 GPS receiver, which provided

position information at 1 second intervals. The GPS readings

allowed us to visualize in real time, on a laptop, the UAV

moving on a U.S. Geological Survey (USGS) satellite map

shown in Figure 1. We have performed a coarse calibration of

the GPS; our estimate of the position error was about 5 meters.

The UAV GPS trace and the static ground node coordinates

allowed us to analyze various performance parameters as

Fig. 2.

Samples of the two antenna types used in our testbed.

Left: our hand-made dipole antenna, tuned to 5.28 GHz (channel

56). Right: the off-the-shelf Netgate antenna.

functions of distance.

A. Antenna Configurations

The antennas were configured as follows. First, let us define

the following labels for referring to various antenna types and

orientations:

H

horizontal dipole (i.e., dipole is parallel to the

ground), orthogonal to flight direction

HN

horizontal Netgate antenna, orthogonal to flight

direction

Hp

horizontal dipole, parallel to flight direction

V

vertical dipole (i.e., dipole is perpendicular to the

ground)

VN

vertical Netgate antenna

Fig. 3. The radiation pattern of the Netgate antennas.

adapter

2

adapter

1

“Hp”

Dipole

“HN”

Netgate antenna

“H”

Dipole

“V”

Dipole

Fig. 4. The UAV antenna setup.

The UAV carried 4 antennas, Hp, HN , H, and V , as

depicted in Figure 4. The ground nodes each carried two

antennas—one on each wireless adapter. The following table

lists their antenna orientations, derived relative to a straight

flight path along the direction of the runway:

Node 1

Node 3

Node 5

Node 6

Antenna 0

V

H

H

Hp

Antenna 1

H

VN

VN

VN

Figure 5 depicts the flight pattern of the UAV and the

orientations of the ground antennas.

B. Description of Traffic Patterns

The UAV was the sole data transmitter during the ex-

periment. It generated an endless stream of sequenced 320-

byte UDP packets (which means roughly a 500 ตs packet

transmission time at the 6Mbps rate) and broadcast them

over its 4 antennas in an approximately round-robin order.

More specifically, a user-mode program alternated enqueueing

pairs of packets into the first and second network adapter’s

socket queue; the kernel-level driver would add a timestamp

and output each packet to the antenna identified by the least

significant bit of the sequence number. For example, of the first

4 packets, numbered 0-3, packets 0 and 1 would go to the first

adapter, while 2 and 3 would go to the second. Furthermore,

packets 0 and 2 would be sent using each adapter’s first

antenna, while 1 and 3 would go out on the second antennas.

One reason for such a multiplexing scheme is to avoid

interference between probe packets; in this scheme, the packets

are interleaved in time so that at any moment, there is at most

one packet in the air. It is for the same reason that we decided

in the experiment to have the UAV node as the sole transmitter

and evaluate only the performance of one-way communication

from UAV to the ground. Full bidirectional measurements

would otherwise require scheduled transmissions from ground

nodes, which would lead to unacceptably large guard times

and thus significantly decrease the temporal resolution of

the measurement. Fortunately, under the typical symmetric-

link assumption in free-space or nearly free-space propagation

models, these unidirectional link measurements can still be

useful in characterizing the bidirectional UAV-ground links.

Under this multiplexing scheme, ideally each antenna would

send one packet every 2ms. Indeed, individual transmit queues

always contain packets for alternating antennas; however, the

combined output of the two adapters can not be perfectly

interleaved since the sending pattern is subject to the random

backoff in the 802.11 CSMA mechanism. We measured the

resulting interleaving pattern in the lab, and found that runs

of packets from the same adapter had at most 7 packets, while

their mean length was 1.53 packets.

The ground nodes captured the broadcast packets using

two wireless adapters and recorded the transmit timestamp,

sequence number, size, and the RSSI figure. This way, from

the data traces of just one ground node we can obtain the

performance for the 8 different links created by the combi-

nation of 4 UAV and 2 ground node antennas. Therefore, the

combined traces of all 4 ground nodes contain the performance

for 32 different antenna combinations.

The reason for measuring so many link combinations nearly

simultaneously is to eliminate the variations that would in-

Node: 1

6 5

3

antenna 0

antenna 1

V

H

Hp

H

H

VN

VN

VN

Fig. 5. Orientations of the antennas on the ground nodes relative

to the UAV flight pattern. The thick gray sticks represent Netgate

antennas.

evitably occur if we measured the different links using separate

UAV flights. All of our 32 traces contain data points that lie

at most several milliseconds apart, which is short enough to

regard many physical parameters of the environment constant.

For example, it may take several hundreds of milliseconds for

the bank angle of the UAV to change enough to appreciably

affect the receiver’s position in the antenna pattern. This is

also long enough that we can fairly compare many interesting

properties for the 32 links, such as throughput, signal strength

and packet loss.

III. MEASURED THROUGHPUT AS A FUNCTION OF

ANTENNA ORIENTATION

In this section, we report the throughput measurement

results, based on which we compare the performance of several

different antenna configurations and identify the best one.

As described in Section II, in the experiments, the UAV

node was constantly sending out UDP packets at 6Mbps, or

1.5Mbps from each of the four transmit antennas. By counting

the number of received packets at a receive antenna in a short

period of time, we can measure the instantaneous performance

of that particular antenna orientation configuration, which we

will call “the UDP throughput.” We use this throughput as

the main performance metric in evaluating various antenna

orientation configurations.

The total flight time in the two flights reported is approx-

imately 24 minutes, during which the UAV node sent out

more than 2.4 million packets. The total number of packets

received at the eight antennas of the four ground nodes is

about 1.8 million; however, most of the packets are received

by more than one antenna, and therefore, the achieved end-to-

end throughput, averaged over all antennas, is merely 120.8

kbps. Due to the relatively large flight area, the UAV node

and the ground nodes are out of each other’s communication

range for a significant portion of time. For this reason, out

of all possible antenna orientation configurations, even the

best configuration (horizontal transmit antenna to elevated

horizontal receive antenna) only receives about 33% of the

packets. We plot the UDP throughput of the top four best-

performing antenna configurations versus distance in Figure 6.

There are four curves in Figure 6. Following the order

described in the legend, the topmost curve represents the

throughput achieved from a horizontally oriented dipole an-

tenna on the UAV node to another horizontally oriented dipole

antenna on an elevated ground node; this combination is the

best antenna orientation configuration we have seen in this

experiment. The second topmost one differs from the previous

one in that the ground node is not elevated. By comparing the

two, we can see that an elevation of 14 feet helps achieve

a significantly higher throughput. The third curve shows the

throughput from a vertically oriented dipole antenna on the

UAV node to another vertically oriented dipole antenna on a

ground node. It is interesting to note that, although inferior

to other three configurations when the distance is small, this

configuration actually outperforms the two horizontal config-

urations in which the ground node is not elevated when the

30

40

50

60

70 80 90100

200

300

0

200

400

600

800

1000

1200

1400

Distance in Logarithmic Scale (meter)

Throughput (kbps)

Horizontal to Horizontal Elevated

Horizontal to Horizontal

Vertical to Vertical

Horizontal to Horizontal Cross−Polarized

Fig. 6.

The UDP throughput of the top four best-performing

antenna configurations.

distance is large enough. This is because when the distance is

small, the vertically oriented dipole antennas on the transmitter

and the receiver are more likely to be in each other’s null,

resulting in worse performance, whereas it is less likely for

the two antennas to be in each other’s null when the distance is

large. Lastly, the bottom curve corresponds to a pair of cross-

polarized dipole antennas on the UAV node and a ground node.

To our surprise, this configuration actually performs quite well,

especially at the farthest distance of around 300 meters. We

believe that this is because when the UAV is at that distance,

it is probably banking at sharp angles such that it turns back

towards the ground nodes; at this time, the antennas are no

longer cross-polarized. We have looked more closely into the

GPS trace and found that the furthest distance between the

UAV and the ground nodes during the entire course of the

flight is around 300 to 350 meters, further confirming our

theory.

We summarize the best throughput results for various

antenna orientation configurations in Table I. Overall, the

horizontal/horizontal antenna orientation configuration has the

best performance; furthermore, elevating the ground node can

also help improve throughput. Cross-polarization in general

has a negative impact on performance; this is evident from

the vertical/horizontal combinations in Table I. Finally, we

find that the off-the-shelf Netgate antennas perform poorly

compared with dipole antennas, most likely due to their narrow

beams along the vertical direction.

IV. MEASURED RECEIVED POWER AS A FUNCTION OF

DISTANCE

In this section, we investigate the correlation between mea-

sured received power and distance. In particular, we perform

linear regression on received power and distance following a

Transmit Antenna

Receive Antenna

Throughput

H

H Elevated

433 kbps

H

H

289 kbps

V

V

246 kbps

Hp

H Elevated

223 kbps

V

H Elevated

160 kbps

Hp

H

143 kbps

V

Hp

137 kbps

Hp

V

119 kbps

V

VN

110 kbps

TABLE I

The throughput performance of various antenna orientation

configurations.

log-distance path loss model. The correlation coefficient of

the linear regression can tell us whether there is a correlation

between received power and distance. We are also interested

in the slope of the linear regression, because it gives us the

path loss exponent of the environment.

Under the log-distance path loss model, received power

(measured in dBm) is expressed as a function of the logarithm

of distance. More specifically, the received power Pr(d) at

distance d can be computed from received power Pr(d0) at

distance d0 with the following formula (α being the path loss

exponent):

Pr(d) = Pr(d0) − 10αlog10(

d

d0

)

(1)

For each packet received by the ground nodes, we logged

its RSSI as reported by the Atheros cards. We then derived

the received power for each packet from the reported RSSI.

It has been shown that there is a constant difference of −95

between the RSSI and the actual received power when RSSI is

greater than 6 [9]. Based on that finding, we derived received

power (in dBm) by adding −95 to the reported RSSI.

We recorded GPS readings on the UAV node and on

the elevated ground node (node 3 in Figure 5) to derive

distance between the two nodes. Because the GPS only reports

coordinate every second, we need to estimate the coordinate

of the UAV when a packet is being transmitted. We estimated

the coordinate by performing linear interpolation between two

enclosing GPS reports and use it to compute distance.

As input to the correlation computation we use data from

the 17 second flyover segment depicted in Figure 1. We chose

this particular segment because its path is close to a straight

line, so the variations in relative antenna orientations due to

UAV turning and banking are expected to be small.

Figure 7 shows the correlation of the measured received

power and distance during this flyover; each red marker rep-

resents one packet. The packets plotted are those transmitted

from the UAV H antenna and received by the H antenna

of Node 3 (the elevated node). We chose this particular

antenna pair because the distance—not randomness induced

by, e.g., ground reflection—should be the dominating effect

on the receive power due to elevation. The straight line in the

-85

-80

-75

-70

-65

-60

-55

300

200

125

80

50

30

Receive Power (dBm)

Distance in Logarithmic Scale (meter)

linear regression (-0.7927 -1.8029 -37.25)

Fig. 7. Received power vs. distance and linear regression result for

the flyover shown in Figure 1. Each marker represents a packet,

and the numbers in the parentheses are correlation coefficient,

slope, and intercept.

figure represents the output of linear regression. Specifically,

the correlation coefficient is −0.7927, slope −1.8029, and

intercept −37.25.

The correlation coefficient indicates there is a good cor-

relation between received power and distance. However, the

absolute value of the slope, which can be interpreted as the

path loss exponent, is lower than expected: for this experiment,

we expected α to be greater than 2, but the slope of −1.8029

suggests a path loss exponent less than 2.

We believe that such low path loss exponent can be ex-

plained by the limitation of our 802.11 equipment and the

dynamic range of the received power in this experiment. More

specifically, when the received power of a packet is too low, the

equipment can not decode it; as a result, no RSSI is reported

for that particular packet. As the distance increases, there will

be more packets that can not be decoded due to low RSSI.

This causes the distribution of received power vs. distance to

be skewed when the distance is large. That is, at long distance

only packets of sufficiently high received power are recorded,

while packets of low received power are dropped. As a result,

absence of packets of low received power results in a skewed

regression slope.

To better measure the correlation of received power and

distance, we would need to revise our experimental setup. We

would need to raise the operating received power range of

our experiments so the distribution of receive power are not

distorted at long distance. We would also want to repeat the

experiments using 802.11b/g in order to obtain measurements

of wider range of distance.

V. DISCUSSION

In Section III we reported throughput measurements based

on data taken during 24 minutes of UAV flights. We now take

a closer look at the flyover segment depicted in Figure 1, in

order to study transmission performance apart from the various

other segments where the UAV motion is not as regular.

Furthermore, we narrow down the data set to that received

by antenna H of the elevated ground node (node 3), since it

would be the least affected by ground effects.

Table II lists the throughputs achieved during the flyover

from the four UAV antennas. The performance of antenna pairs

orthogonal to the flight path (H-H and HN -H) is noticeably

better than the other two; even the second-best pair performs

more than twice as well as the third-best. On the other hand,

the worst performer is the V -H pair, possibly due to cross-

polarization and the fact that the receiver is placed closer to

the antenna null region of the transmitter.

H

0.63101 Mbps

(42.1%)

HN

0.59425 Mbps

(39.6%)

V

0.23607 Mbps

(15.7%)

Hp

0.16682 Mbps

(11.1%)

TABLE II

The throughput performance of four UAV antennas to the H

antenna on the elevated ground node, expressed in megabits per

second and as fractions of the maximum possible throughput.

Figure 8 shows the raw RSSI data from the flyover. This

data agrees with the throughput measurements in that the

strongest signal comes from the H and HN antennas. Fur-

thermore, the plots uncover two additional observations.

First, we note that even though its performance is second

best, the HN antenna exhibits a significant peak around 10s

into the flyover. This is not an isolated incident, since we

observed similar peaks on other flyover traces. We believe that

this is caused by the HN antenna’s narrow beam pattern—

as the UAV flies and banks at varying angles toward the

ground nodes, it occasionally “hits” them with the main

lobe of the HN antenna. The narrow beam seems to be

a disadvantage here, since it increases the variation in link

quality without actually beating the wide-beam antenna in

throughput performance.

The second observation is that the signal strength of the best

antenna pair does not vary smoothly as the UAV flies over the

ground nodes; instead, we can see at least three major peaks

in the curve for antenna H. Since the antennas in this pair

have axially symmetric beam patterns, the peaks cannot be

explained by the UAV motion alone. Instead, we believe that

the cause is interference from a reflected ray as modeled by

two-ray propagation.

Some receiving antennas, other than those in Figure 8,

performed very poorly. For example, the VN antenna of Node

5 did not receive any packets at all. It turns out that most poor

performers were VN antennas; we believe that their narrow

horizontal beam patterns were largely underneath the UAV.

This further illustrates the difficulty with use of directional

antennas.

VI. CONCLUSIONS

Our measurement data have shown that, for UAV’s commu-

nication with a ground node, horizontal dipole antennas with

-95

-90

-85

-80

-75

-70

-65

-60

-55

0

2

4

6

8

10

12

14

16

RSSI (dBm)

Time (s)

Packets from UAV antenna Hp

Packets from UAV antenna HN

Packets from UAV antenna H

Packets from UAV antenna V

Fig. 8.

There are four sets of points shown, corresponding to

packets coming from each of the UAV’s four antennas.

their nulls pointing to a direction perpendicular to the UAV

flight path yields the highest throughput among 32 antenna pair

configurations. In addition, the measurement data suggest that

the path loss in an airfield environment is roughly proportional

the square of the communicating distance. These results appear

to be among the first antenna measurement results for 802.11

based UAV networking. We chose to start our measurement

work with 802.11a because there is relatively less interference

from the environment in the 5GHz band. We plan to conduct

similar measurements for 802.11 b/g in the future.

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[5] Haykin, S., “Communication Systems,” John Wiley & Sons, Inc.,

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