Post on 01-Apr-2018
Impact of Channel Width on the Performance
of Long-distance 802.11 Wireless Links
Jhair Tocancipa TrianaT
HE
U N I V E RS
IT
Y
OF
ED I N B U
RG
H
Master of Science
Computer Science
School of Informatics
University of Edinburgh
2009
Abstract
A wireless communication channel can be characterized by a center of frequency (Fc)
and a channel width (Fw). Recent studies suggest that, in the presence of changing
environmental conditions, adjusting the channel width of a communication channel
might improve network variables like throughput and power consumption.
This thesis focuses on the effect that variable channel widths may have on the per-
formance of long distance IEEE 802.11 mesh networks, in particular on the Tegola net-
work, a network deployed at the Scottish Highlands by researchers from the University
of Edinburgh. One of the main contributions of this work is towards the understanding
of how variable channel widths could be used to improve the performance of wireless
long distance links affected by tidal effects.
i
Acknowledgements
I want to thank Prof. Mahesh Marina, head of the Wireless and Mobility Group (WiMo)
at the University of Edinburgh, for his continuous support during this project. I also
would like to thank Arsham Farshad and Sofia Pediaditaki, researchers from the WiMo
group, for their valuable feedback. Finally, I would like to express my gratitude to Prof.
Peter Buneman, Prof. Rick Rohde and Giacomo Bernardi for their assistance in setting
up a new long-distance wireless link for the Tegola network in the Scottish Highlands.
ii
Declaration
I declare that this thesis was composed by myself, that the work contained herein is
my own except where explicitly stated otherwise in the text, and that this work has not
been submitted for any other degree or professional qualification except as specified.
(Jhair Tocancipa Triana)
iii
Table of Contents
1 Introduction 1
2 Background and related work 32.1 Long-distance wireless mesh networks . . . . . . . . . . . . . . . . . 3
2.2 Link characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Interference characteristics . . . . . . . . . . . . . . . . . . . . . . . 5
2.4 The problem of channel width adaptation . . . . . . . . . . . . . . . 5
2.5 Medium Access Control (MAC) protocols . . . . . . . . . . . . . . . 6
2.6 Channel allocation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Long-distance wireless link characteristics in the presence of tides 93.1 Simulation of the effect of tide levels on signal strength . . . . . . . . 10
3.2 Use of diversity to overcome the negative effect of tides on signal strength 11
4 Experimental study of the impact of channel width on network perfor-mance 144.1 Hardware and software . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 Indoor experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.2.1 UDP throughput and packet loss at the receiver node . . . . . 19
4.2.2 Additional indoor investigations . . . . . . . . . . . . . . . . 20
5 Mitigating over water propagation effects using channel width adaptation 255.1 Preliminary activities . . . . . . . . . . . . . . . . . . . . . . . . . . 25
5.2 Issues faced after preliminary activities . . . . . . . . . . . . . . . . . 26
5.2.1 Low signal strength on the new test link . . . . . . . . . . . . 26
5.2.2 Production performance regression . . . . . . . . . . . . . . 27
5.2.3 Operating system crashes . . . . . . . . . . . . . . . . . . . . 28
5.3 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . 28
iv
5.3.1 Tide levels affect the signal strength . . . . . . . . . . . . . . 29
5.3.2 The role of channel width adaptation . . . . . . . . . . . . . . 30
6 Conclusions and future work 34
A Basic concepts 36A.1 Signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
A.2 Channel characterization . . . . . . . . . . . . . . . . . . . . . . . . 37
B Link planning with SPLAT 38
C Greedy edge coloring implementation 41
Bibliography 44
v
Chapter 1
Introduction
In recent years, wireless mesh networks with long-distance point-to-point links have
shown to be a feasible, cost-effective alternative for broadband Internet access in rural
areas. Using commodity IEEE 802.11 equipment 1 and directional antennas it’s possi-
ble to build operational IP based mesh networks with link lengths in the order of tens
of kilometers. An example of such network is the Tegola network[1] deployed in the
Scottish Highlands by a team of the University of Edinburgh.
Although cost-effective, the use of IEEE 802.11 equipment to build long-distance
networks has some disadvantages. The protocols implemented by such equipment
were initially designed as an alternative to Ethernet-based LANs but not optimized
for long-distance links. For example, the use of channel reservation schemes such
as the Request to Send (RTS) and Clear to Send (CTS) exchange slows down the
performance long distance links, since the channels are usually allocated in such a way
that interference among adjacent links is avoided.
Efforts have been made by the research community to develop adaptive protocols
that work efficiently on long-distance networks. The basic idea behind adaptive pro-
tocols is to adjust some variable relevant to the network to improve its performance.
This adjustment can be done statically before deploying the network, or dynamically
adjusting the variable over time based on other parameters of the network, e.g. user
demand or environmental changes.
One variable of interest for adaptation is the center of frequency of the communica-
tion channels in the network. The range of frequencies passed by a given communica-
tion channel can be thought as centered around a particular frequency Fc. Algorithms
1Examples of this equipment includes radio cards and the software driver that controls them, bothimplementing the IEEE 802.11a standard.
1
Chapter 1. Introduction 2
have been devised by the research community to allocate centers of frequency in such
a way that the network throughput on long-distance links is improved. An instance of
such algorithm is presented by Dutta[2].
A relatively new idea is to adapt, in addition to Fc, the channel width Fw. Re-
cent research efforts by Chandra[3] focus on channel width adaptation on single links,
claiming throughput gains, in the presence of interference, up to 60% over static chan-
nel width allocation. Gummadi[4] studies how channel width adaptation among a set
of long-distance links can be used to reduce interference between the links.
Nevertheless, no known efforts have been made to study the effects of variable
channel widths on long distance wireless links over water. One of the contributions of
this thesis is to evaluate the feasibility of channel width adaptation as a mechanism to
counter the negative effect of variable tide levels.
The structure of this thesis is as follows. Chapter 2 provides some background on
long-distance wireless networks and channel (width) allocation. Chapter 3 discusses
the simulation of several diversity mechanisms in the presence of tidal effects. Chapter
4 focuses on the hardware/software requirements to evaluate variable channel widths,
and provides indoor experimental results. Chapter 5 describes the changes done to the
Tegola network to get nodes with variable channel width support, and analyzes outdoor
experimental results. Finally, Chapter 6 contains the conclusions and outlines venues
of future work.
Chapter 2
Background and related work
2.1 Long-distance wireless mesh networks
A long-distance wireless mesh network consists of nodes, some of them connected
to the wired Internet, and long-distance point-to-point wireless links connecting the
nodes.
Figure 2.1 shows the graph representation of the Tegola long-distance wireless net-
work. Here each node has two directional antennas, each one of them pointing to a
different antenna at its peer node. I.e. node O has one antenna pointing to one antenna
at I, and a different antenna pointing to an antenna at C. The currently deployed an-
tennas support horizontal and vertical polarizations so each pair of antennas can form
2 different links. The antenna dishes at each node are separated by a few meters. Ad-
ditionally, an access point (AP) at each node provides connectivity to end-users. One
of the antennas and the AP at node O are shown in Figure 2.2.
•G© •I
•O •B
•C
Figure 2.1: Graph representation of the Tegola network. The longest link (GB) is 19km
long. The node connected to the wired Internet is circled. All links are over water.
3
Chapter 2. Background and related work 4
Figure 2.2: The Ornsay node (O) from the Tegola network. The antenna on the left has
line-of-sight with its peer antenna at the Corran node (C). On the far right the access
point which provide access to end-users can be seen. The antenna responsible for the
Ornsay↔ Inver link (OI) is not shown.
2.2 Link characteristics
Wireless links can be characterized by the bands of the electromagnetic spectrum they
use. In particular, the Tegola network uses the 5GHz[5] band of the spectrum, which is
supported by the IEEE 802.11a standard. The advantages of using the 5GHz band over
the more popular 2.4GHz band, supported by the IEEE 802.11b/g standards, include
the better reflection and the lower atmospheric absorption of the signal. On the other
hand, given that packet loss is inversely proportional to the signal’s wavelength, the
path loss of signals on the 5GHz band is higher than on the 2.4GHz band. To com-
pensate this path loss, the IEEE 802.11a standard allows 5GHz links to use a higher
amount of power[6].
More importantly, long-distance point-to-point links in rural areas have been char-
acterized in the literature as reliable enough to assume that the link abstraction holds
for them over time, i.e. given two nodes connected by a wireless link, the link ei-
ther provides 1) full, reliable connectivity between the nodes or 2) no connectivity at
all. Research by Chebrolu[7], Gokhale[8] and Nedevschi[9] found empirical evidence
about the validity of the link abstraction in rural long-distance networks.
Chapter 2. Background and related work 5
2.3 Interference characteristics
Phenomena such as multi-path, external interference, propagation delay and path loss
directly affect the performance of long-distance wireless links. Nevdevschi[9] pro-
vides evidence on how interference due to multi-path has little or no impact on the
performance of long-distance links. On the other hand, in the same research, a strong
correlation between the centers of frequency of adjacent links and packet loss rate is
found.
As mentioned in section 2.1 nodes in long-distance mesh networks have one direc-
tional antenna per link. Ireland[10] shows how the throughput on a given long-distance
link can vary significantly depending on antenna characteristics such as orientation,
placement and polarization.
A possible explanation for this variation is given by Raman[11]. Even with high
antenna gains, side-lobe radiation generated by antennas placed near each other, us-
ing the same center of frequency, will cause packet loss due to interference. Raman
found, empirically, that simultaneous transmission and reception at a given node cause
interference due to side-lobe radiation. E.g., in the graph shown in Figure 2.1, signals
being received at G from B and signals being transmitted from G to I simultaneously
interfere with each other, iff both links use the same channel. This interference is also
known as Mix-Rx-Tx interference1.
2.4 The problem of channel width adaptation
Adaptive algorithms adjust their run-time behavior depending on resource availability
over time. The adjustment is made towards optimizing the value of a given metric.
In particular, a channel width adaptation algorithm assigns widths Fw to the links in a
network based on a given network performance metric, such as consumed power at the
nodes or average throughput.
The assignment of Fw makes only sense in conjunction of the assignment of a
center of frequency Fc. For this reason the channel width adaptation problem can be
thought as the assignment of 〈Fc,Fw〉 pairs to links on a network. The assignment of
the 〈Fc,Fw〉 pairs may require a channel negotiation phase, which is described in 2.6.
1The scenarios where all antennas at a node are either transmitting (Syn-Tx) or receiving (Syn-Rx)also cause interference due to side-lobe radiation. However, Raman’s results suggest that the packet lossin the Syn-Rx and Syn-Tx cases is significantly lower than in the Mix-Rx-Tx case.
Chapter 2. Background and related work 6
Chandra[3] provides experimental evidence of the benefits of adjusting the chan-
nel width of a single link on a wireless LAN. A channel adaptation algorithm called
SampleWidth assigns a center of frequency Fw ∈ {5,10,20,40}MHz to a given link
based on the average throughput T and average data rate R measured on the link2,
aiming to maximize the throughput from the sender to the receiver. Chandra claims
that SampleWidth converges to the optimal channel width, i.e. to the Fw where the
throughput from the sender to the receiver is the highest.
Theoretical and experimental evidence of the benefits of channel width adaptation
in the context of long-distance urban links is provided by Gummadi[4]. The authors
present the channel adaptation algorithm VWID which assigns 〈Fc,Fw〉 pairs to links
on a network. VWID is somewhat similar to SampleWidth since it also uses probes
to measure the throughput of a link, and based on those measurements, decide which
〈Fc,Fw〉 pair is optimal. However VWID takes into account the throughput of several
links simultaneously while SampleWidth focuses on a single link only.
VWID is not distributed, i.e. the assignment of channel widths is done by a central-
ized entity which has knowledge about the global state of the network. Unfortunately,
Gummadi doesn’t provide details about their strategy to ensure that two end-points of
a given link are assigned the same 〈Fc,Fw〉 pair.
2.5 Medium Access Control (MAC) protocols
The consensus in the research community is that the IEEE 802.11 MAC protocols
are ill-suited for long-distance links. The higher propagation delays in long-distance
links imply that retransmissions are more frequent due to ACK packets received af-
ter the ACK timeout3. With the CSMA/CA4 protocol, the probability that two nodes
sense the medium as idle and send frames to reserve the medium simultaneously, caus-
ing a collision, increases with the distance between nodes. Moreover, as shown by
Gummadi[4], CSMA/CA imposes a non-optimal theoretical upper bound to the aggre-
gated throughput.
2Here an informal description of the algorithm: In a probing step the algorithm measures T and Rand compares R with constant data rates α and β. If R is lower than α, a lower Fw value is set for thelink. If R is greater than β, a higher Fw value is set for the link. The probing step is then performedagain with the new chosen Fw (the number of probes is finite). Once all probes terminate, the algorithmassigns Fw the channel width for which the measured throughput from the sender to the receiver was thehighest.
3For this reason, long-distance networks like Tegola need to adjust the ACK timeout to avoid suchretransmissions.
4Carrier Sense Multiple Access/Collision Avoidance.
Chapter 2. Background and related work 7
Examples of a MAC protocol proposed by the research community to overcome
some of the IEEE 802.11 MAC limitations are the P2P protocol and the JazzyMac
protocol[12].
The P2P protocol designed by Raman[11] aims to be an efficient alternative to
CSMA/CA, allowing maximum throughput even if adjacent links in the network use
the same center of frequency.
To see how P2P works, consider the bipartite graph shown in Figure 2.3. Let V1
be the set of nodes in the first partition and V2 the set of nodes in the second partition.
With P2P, during time slot t, the nodes in V1 = {O,G} transmit (nodes are in Tx mode)
while the nodes in V2 = {I,B,C} listen (nodes are in Rx mode).
•G •I
•O •B
•C
〈Fc,Fw〉
))〈Fc,Fw〉
""
〈Fc,Fw〉
77
〈Fc,Fw〉//
Figure 2.3: P2P nodes from the first partition in Tx mode.
During time slot t + 1 the nodes in V2 = {I,B,C} are in Tx mode while the nodes
in V1 = {O,G} are in Rx mode. This mode of operation is illustrated in Figure 2.4.
•G •I
•O •B
•C
mm〈Fc,Fw〉WW
〈Fc,Fw〉
ss
〈Fc,Fw〉
oo〈Fc,Fw〉
Figure 2.4: P2P nodes from the second partition in Rx mode.
P2P switches between Tx and Rx modes of operation over time. P2P doesn’t re-
quire that the channels used by the links of the graph to be different. In fact, P2P im-
proves throughput even if the same channel is used for all links in the bipartite graph5,
since Mix-Tx-Rx interference is avoided.5If the graph is not bipartite P2P divides the graph in several bipartite sub-graphs. All links in a given
Chapter 2. Background and related work 8
2.6 Channel allocation
While the protocols presented in the section 2.5 try to improve throughput under the
assumption that all links use the same center of frequency (or channel), another fam-
ily of protocols exists that allocates different centers of frequency to adjacent links,
effectively avoiding packet loss due to Mix-Rx-Tx interference.
Dutta[2] proposes an algorithm based on graph coloring that assigns a different Fc
to every link at a given node in such a way that no two adjacent links get the same
center of frequency. Given that Mix-Rx-Tx interference is only relevant for links using
the same center of frequency, the application of the Dutta’s algorithm improves the
throughput of each link in a network. Nevertheless, the algorithm is static, i.e. it’s run
over a network topology without taking into account traffic, routing or other dynamic
aspects of the network.
As an additional contribution of this thesis, an implementation of a greedy graph
coloring algorithm is presented in Appendix C.
Instead of assigning centers of frequency at network design time, they could be
assigned in real-time. Wormsbecker[13] suggests the introduction of a channel nego-
tiation phase where the end-points of a given link agree on the Fc to use. A simple
protocol for this would involve one end-point sending a list of available frequencies
(channels) to its peer. This peer would select one of the received frequencies and use
it to start data transmission. This could be done with a new protocol or, as Worms-
becker proposes, extending the IEEE 802.11 MAC’s RTS/CTS frames with a bit-mask
representing the different Fc values being negotiated.
A channel width negotiation algorithm, at the application level, used to perform the
automatized measurements for this project is presented in Section 4.2.
sub-graph is assigned the same channel. Adjacent sub-graphs get assigned different channels. Then thesynchronized Rx-Tx modes are executed on each sub-graph.
Chapter 3
Long-distance wireless link
characteristics in the presence of tides
The long-distance links of the Tegola network are not as stable as expected by previous
research (see Section 2.2), which didn’t focus on over water signal propagation. For
instance, Bernardi[1] documents significant fluctuations in the Sound to Noise (SNR)
ratio and Received Signal Strength Indication (RSSI) measured at the Tegola nodes,
as much as 20dB over 1-2 hour periods. Bernardi suggest that a major cause of such
fluctuations might be the changes in the sea levels caused by the tidal effect. Recent
measurements by G. Bernardi showing these fluctuations are shown in Figure 3.1.
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06/0300:00
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R(d
B)
Time
SNR for the Beinn to CollegeN link
ath0 (V polarization)ath2 (H polarization)
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RS
SI(
dBm
)
Time
RSSI for the Beinn to CollegeN link
ath0 (V polarization)ath2 (H polarization)
Figure 3.1: Fluctuations of SNR (left) and RSSI (right) over a 24 hours period on the
Beinn↔ College link. This is the GB link shown in Figure 2.1.
To better understand the relationship between tidal effects and signal strength soft-
ware simulations were performed.
9
Chapter 3. Long-distance wireless link characteristics in the presence of tides 10
3.1 Simulation of the effect of tide levels on signal strength
The Corran↔ Ornsay long-distance link 1 was modeled with the third-party software
Pathloss2. Pathloss is an advanced package for the simulation and analysis of several
wireless phenomena, such as refraction, reflection and multipath. For the simulation
of tidal effects the reflection module was used.
The inputs of the simulation include:
1. Node information: This includes the geographical coordinates of the nodes to-
gether with the channel frequencies and antenna gains.
2. Elevation pattern: Based on the node information and its terrain database, Pathloss
generates the elevation pattern between both nodes. The obtained elevation pat-
tern is shown Figure 3.2.
Figure 3.2: Elevation pattern generated by Pathloss (left) and antenna height
settings (right) for the Corran↔ Ornsay link.
3. Antenna heights. These were set at 2m for the Ornsay node and at 3m for the
Corran node. The line-of-sight is shown in Figure 3.2.
4. Reflective plane. A constant elevation (0m) reflective plane at sea level was
defined. This reflective plane is shown in Figure 3.3.
5. Channel frequencies, polarization (vertical) and tidal level range ([1 . . .5]m).
With the provided inputs Pathloss calculates the relative signal strength for the
different tide levels. The results of this calculation are shown in Figure 3.3. Here,
1This is the OC link shown in Figure 2.1.2http://www.pathloss.com/
Chapter 3. Long-distance wireless link characteristics in the presence of tides 11
Figure 3.3: Sea level reflection plane (left) and simulation results showing the
effect of the tide level on signal strength (right).
when the tide level is 2.5m high the lowest signal strength is expected. This lowest
point is also known in the literature as a null. On the other hand, tide levels above the
2.5m should increase the signal strength for the given polarization, antenna height and
radio frequency.
3.2 Use of diversity to overcome the negative effect of
tides on signal strength
Diversity techniques are commonly used to reduce the effect of environmental phe-
nomena (e.g. tide levels) on the quality of a wireless link. Three forms of diversity for
the Corran↔ Ornsay link were simulated with Pathloss:
• Frequency diversity: In this case the nodes adjust the center of frequency in such
a way that the signal strength is maximized. This is illustrated in Figure 3.4
(bottom right). Here, when the tide is 2.5m high, channel 36(5.18GHz) provides
the lowest signal strength. Switching to the channel 165(5.825GHz) improves
the signal strength at the 2.5m tide level, all other things being equal.
• Polarization diversity: Here the polarization that improves the signal strength
is preferred. Pathloss predicts a slightly better signal strength when using the
vertical polarization. This is shown in Figure 3.4 (bottom left).
• Antenna height diversity: In this case, at a given node, a second antenna is placed
at a height d with respect to the original antenna. d is chosen in such a way
that the null signal strength value obtained with the original antenna becomes
Chapter 3. Long-distance wireless link characteristics in the presence of tides 12
a peak (maximum) value with the new placed antenna. Hassett[14] provides a
theoretical approximation for d,
dsep(in meters)≈ 75df h1
(3.1)
with d the distance between the nodes (in kilometers) , h1 the height above the
reflective plane of one of the antennas (in meters) and f the channel frequency
(in GHz).
From the Pathloss terrain model, d ≈ 16km and h1 ≈ 59m (see Figure 3.4 top
left3). So for f = 5.18GHz the height separation between the two antennas at
the Ornsay needs to be,
dsep(in meters)≈ 75 ·165.18 ·59
≈ 4
This means that a second antenna at the Ornsay node placed 4 meters above the
original antenna at the very same node would receive a maximum signal strength
when the tide is 2.5m, all other things being equal.
The result of the simulation when using the obtained dsep theoretical value is
shown in Figure 3.4 (top right). Here the signal strength with the second antenna
is the highest while the original antenna measures a null.
In this project we study the feasibility of new diversity mechanism based on the
adaptation of the channel width, as a way to improve the quality of a long-distance
link in the presence of tidal effects. To evaluate this on the Tegola network, it was
necessary to investigate how the channel widths could be changed on real hardware.
The next chapter describes this investigation and how the modified channel widths
affect network performance.
3Pathloss shows an elevation of 56m to which the height of the antenna is added (estimated 3m atthe Corran node). h1 in Equation 3.1 refers to the height above the sea level.
Chapter 3. Long-distance wireless link characteristics in the presence of tides 13
Figure 3.4: Pathloss simulation results for antenna height diversity (top right), polar-
ization diversity (bottom left) and frequency diversity (bottom right). The geographical
information entered in Pathloss for the Corran↔ Ornsay link is shown at the top left.
Chapter 4
Experimental study of the impact of
channel width on network
performance
Variable channel widths require hardware and software support. For this project, hard-
ware (radio cards) supporting variable channel widths were made available by the
WiMo group. These are the same type of cards being used at the nodes of the Tegola
network.
On the other hand, the software currently running on those cards didn’t support
variable channel widths. For this reason, the software on the cards needed to be up-
graded. Since the cards are embedded devices, efforts were made to learn the skills
required for embedded software installation and development. This learning effort
was facilitated by the Tegola network’s documentation1.
4.1 Hardware and software
The hardware involved in the experiments included:
• Gateworks Avila GW2348-4 boards: these are embedded devices controlled by
an Intel IXP425 processor (ARM architecture). The boards have 15Mb of flash
memory, 64Mb of SDRAM, two Ethernet ports and a serial RS-232 interface.
Four 32-bit mini-PCI Type III sockets are also provided. The boards are powered
by a 9-48V@23W interface.
1http://www.tegola.org.uk/dev/index.php/Main_Page
14
Chapter 4. Experimental study of the impact of channel width on network performance15
• Ubiquiti Networks XtremeRange5 radio cards: these are 32-bit mini-PCI Type
III cards which can be installed on the PCI sockets of the Gateworks Avila
boards. These cards provide a single MMCX antenna port. The cards are based
on the Atheros AR5414 radio-on-a-chip (RoC) which supports the IEEE 802.11a
standard. Support for 5, 10, 20 and 40MHz channel widths is provided.
• Pigtails: These are the cables used to connect an antenna to the MMCX port on
the radio cards.
• Laird 5.8GHz omnidirectional antennas: These are antennas with 3dBi power
gain. Were used in the indoor experiments.
• Directional Laird HDDA5W-29-DP dish antennas: These are antennas with 29dBi
power gain and dual polarization used for the long-distance links on the Tegola
network.
• Directional 5.8GHz parabolic grid antennas: These are antennas with 30dBi gain
used to create a new outdoor test link described in section 5.1.
• Wi-Spy USB 5GHz spectrum analyzer: this device is used to track the amplitude
and width of the signals traveling through the air during the experiments. It was
particularly valuable for debugging purposes.
The software compiled and installed by the author to control the hardware included:
• OpenWrt 8.09.1 (Kamikaze) GNU/Linux operating system: this operating sys-
tem is aimed at embedded devices. This version is based on the 2.6.26.8 release
of the Linux kernel and supports the Intel IXP425 processor found in the Gate-
works Avila boards. It also includes the free Madwifi driver, release version
0.9.4, for Atheros based cards (such as the XtremeRange5 radio cards).
• Ubiquiti Networks 0.7-beta.379 driver for the XtremeRange5 radio cards: this
driver is based on the free Madwifi driver, development version r3319 from the
Subversion repository2.
• spectools utilities: these are applications for the Wi-Spy USB 5GHz spectrum
analyzer. The development version r3319 from the Subversion repository3 was
used. The tool was installed on a laptop running Ubuntu 8.04.1 GNU/Linux.2http://svn.madwifi-project.org/madwifi/trunk3https://www.kismetwireless.net/code/svn/tools/spectools
Chapter 4. Experimental study of the impact of channel width on network performance16
Figure 4.1: Gateworks Avila board (green) with one XtremeRange5 Ubiquiti radio card
(bottom left) connected to a 5GHz omnidirectional antenna through a pigtail. A laptop
is connected to one of the Ethernet ports and to the serial interface.
• Network measurement tools: iperf, development version from the Subversion
repository4, was initially used for performance tests. nuttcp, stable version
6.2.15, was used for most of the final results in this thesis. These tools were cross
compiled to run on the ARM based boards. Additionally the kismet network
sniffer was used for the investigation described in section 4.2.2.
• Shell script utilities: Automated measurements were written in shell scripting
language. The processing of the log files generated by the measurements was
done with shell utilities, e.g. awk,sed. This was done mainly due the storage
limitations of the boards where the use of more advanced scripting languages like
python or java is strongly limited. Measurements were automatically sched-
uled with the cron utility on the boards.
4https://iperf.svn.sourceforge.net/svnroot/iperf/trunk5ftp://ftp.lcp.nrl.navy.mil/pub/nuttcp/
Chapter 4. Experimental study of the impact of channel width on network performance17
4.2 Indoor experiments
The first goal was to evaluate the Ubiquiti driver to ensure that the variable channel
widths were being correctly set by the driver. The change of the channel widths is
controlled by values of a particular entry of the proc filesystem on the boards (with
the exception of the 40MHz width channel). Table 4.1 shows the required commands.
Channel width (MHz) Shell command
5 echo 0x156d0001 > /proc/sys/dev/wifiX/cwidth
10 echo 0x156d0002 > /proc/sys/dev/wifiX/cwidth
20 (default) echo 0x156d0000 > /proc/sys/dev/wifiX/cwidth
40 (TurboMode) iwpriv athX turbo 1
Table 4.1: Commands to change the channel width on interface X. Before issuing each
command the interface must be shut down (ifconfig athX down) and after each
command the interface must be activated (ifconfig athX up).
Two boards connected to omnidirectional antennas were configured to communi-
cate with each other in point-to-point (ad-hoc) mode. Both cards shared the same IEEE
802.11a channel, same channel widths and same IP subnetwork. Traffic was injected
to the wireless link with the nuttcp tool.
To avoid the influence of uncontrolled parameters in the final results some parame-
ters were set to particular values. For instance UDP was used to avoid the overhead of
transport layer (TCP) mechanisms, such as the Automatic Repeat reQuest (ARQ) pro-
tocol and the congestion control algorithm. The MAC layer data rate was fixed instead
of using the Minstrel rate adaptation algorithm provided by default by the Ubiquiti
driver. Minstrel basically adjusts the sender’s MAC layer data rate (6, 9, 12, 18, 24,
36, 48 or 54Mbps) and modulation (BPSK, QPSK or QAM) using heuristics based
on the quality of the link. The transmission power was also fixed at 10dBm. The
parameters are summarized in Table 4.2.
Chapter 4. Experimental study of the impact of channel width on network performance18
Algorithm 11: procedure SYNCCHANNELWIDTH(〈Fc,Fw〉source, interface, target, stats)
2: 〈Fc,Fw〉source← estimateBestWidth(〈Fc,Fw〉source, interface, stats)
3: 〈Fc,Fw〉target← send(target, changeChannel, 〈Fc,Fw〉source) . Synchronous call
4: if 〈Fc,Fw〉target = 〈Fc,Fw〉source then . Target adjusted its interface as proposed
5: adjustInterface(interface, 〈Fc,Fw〉source)
6: radio and MAC stats log start(interface, stats)
7: injectTraffic(interface)
8: radio and MAC stats log end(interface)
9: end if10: end procedure
A channel width synchronization algorithm was implemented as a shell script run-
ning on the boards. Algorithm 1 shows the pseudo-code of the synchronization pro-
cedure. This algorithm assumes the existence of an additional channel (interface) for
message passing (e.g. at Step 3 of the algorithm).
While traffic was flowing through the wireless link the spectrum analyzer was used
to gather the range of frequencies used by the channel. As shown in Figure 4.2 the
width of range of frequencies is proportional to the channel width set on the driver.
-110
-100
-90
-80
-70
-60
-50
-40
5.765GHz (153)
Sig
nal s
tren
gth
(dB
m)
Frequency (Channel)
5MHz10MHz20MHz
-110
-100
-90
-80
-70
-60
-50
-40
5.76GHz (152)
Sig
nal s
tren
gth
(dB
m)
Frequency (Channel)
40MHz (Turbo Mode)
Figure 4.2: Signal strength (y-axis) and range of frequencies (x-axis) as reported by
the Wi-Spy USB 5GHz spectrum analyzer. Each line on the plot corresponds to the
measured signal strength during a network performance test with the nuttcp tool.
Once there was confidence that the Ubiquiti driver was correctly limiting the range
of frequencies as the channel width changed, performance tests were done to evaluate
different network metrics.
Chapter 4. Experimental study of the impact of channel width on network performance19
Name Value
Transmitter node A (IP 192.168.3.11)
Receiver node B (IP 192.168.3.13)
Traffic type UDP
Measurement tool nuttcp
Transmission power 10dBm (fixed)
Data rate (MAC layer) 6Mbps (fixed)
Channels (center frequency) 152 (5.76GHz), 153 (5.765GHz)
RTS/CTS disabled
MAC layer ACKs enabled
Packet size 1490bytes
Test duration 20sec
Antenna Omnidirectional (3dBi gain)
Test frequency Per minute (scheduled with cron)
Table 4.2: General parameters for the indoor experiment on the wireless testbed at the
WiMo group’s laboratory (Informatics Forum).
4.2.1 UDP throughput and packet loss at the receiver node
A correlation between the channel width and the measured throughput at the server is
expected. From the Shannon capacity formula,
C = B log2(1+SNR) (4.1)
with C the capacity of the channel, B the bandwidth (or channel width) of the
channel in Hertz and the SNR (understood as the ratio between the signal and the
noise powers). Assuming a SNRdB = 20 (SNR = 102)6,
C40MHz = 40 ·106 · log2(1+102) ≈ 40 ·106 ·7 = 280Mbps
C20MHz ≈ 20 ·106 ·7 = 140Mbps
C10MHz ≈ 10 ·106 ·7 = 70Mbps
C5MHz ≈ 5 ·106 ·7 = 35Mbps
6SNRdB = 10log10(SNR) =⇒ SNR = 10SNRdB
10
Chapter 4. Experimental study of the impact of channel width on network performance20
I.e. by doubling the channel width we should expect an increase in the throughput
by a factor of two. Nevertheless the above rates cannot be achieved by IEEE 802.11
systems, given that the capacity formula provides only a theoretical upper bound re-
gardless the protocol used to transmit the data.
Jun[15] provides formulas for the estimation of the maximum theoretical through-
put expected by applications running over the IEEE 802.11 MAC layer. From Jun’s
formulas, the maximum expected throughput for a fixed 6Mbps OFDM modulation
with RTS/CTS frames disabled (as in the experiment (see Table 4.2)) should be near
the 5Mbps. Nevertheless, Figure 4.3 shows throughput in the order of 10Mbps with
the standard channel width of 20MHz.
The root cause of this discrepancy is the channel initially used for the experiment.
Channel 152 (and channels 50, 58, 152 and 160) support TurboMode (40MHz) width
channels. During the measurements the TurboMode was being activated by the driver
hence the increased measured throughput. For this reason, experiments with and with-
out 40MHz width channels needed to be performed separately.
The results of such indoor experiments are shown in Figure 4.4. A shell script
was written to loop over different rates and channel widths while running Algorithm
1. Here the doubling of the measured throughput as predicted by the capacity formula
can be observed for almost all rates. For the 40MHz channel width this trend could not
be observed for MAC layer transmission rates higher than 36Mbps. This result can not
be attributed to packet loss since the packet loss TurboMode was less than the packet
loss for the 20MHz channel width.
The obtained results, for the 5, 10 and 20MHz channel widths, reproduced previous
results from Chandra[3] where the effect of channel widths on throughput in indoor
environments was first studied. This gave confidence in moving forwards to perform
the necessary activities to make measurements with variable channel widths on the
Tegola network itself.
4.2.2 Additional indoor investigations
To further reduce the number of uncontrolled parameters during the experiments an in-
vestigation was performed to find out how to deactivate the sending of MAC layer ac-
knowledgments at the target node. Due the greater probability of packet loss in wireless
networks over wired networks, the IEEE 802.11 MAC layer provides an ARQ mecha-
nism for data reliability. When measuring the effect of channel widths on throughput
Chapter 4. Experimental study of the impact of channel width on network performance21
0
2
4
6
8
10
12
04:00 06:00 08:00 10:00 12:00 14:00 16:00
Thr
ough
put -
Nod
e B
(M
bps)
Time (hour)
20MHz10MHz5MHz
0
500
1000
1500
2000
2500
3000
3500
4000
4500
04:00 06:00 08:00 10:00 12:00 14:00 16:00
UD
P p
acke
ts lo
st
Time (hour)
20MHz10MHz5MHz
Figure 4.3: Measured throughput at the receiver (left) and UDP packet loss (right) for
different channel widths.
these acknowledgments might also be an overhead in the same way ARQ mechanisms
provided by TCP are.
The Ubiquiti and Madwifi drivers don’t provide a parameter to deactivate the MAC
layer acknowledgments. Fortunately the source code of the drivers is freely available
and the deactivation of MAC layer acknowledgments was already done by members of
the Madwifi user community. Disabling acknowledgments requires two basic changes:
1. Disable the automatic generation of acknowledgments by the hardware of the
receiver node. This is done by writing a particular value to a hardware register
of the radio card, e.g. with:
OS_REG_WRITE(ah, 0x8048, 0x00000002);
2. Tell the hardware don’t to wait for acknowledgments before transmitting data.
This is done by passing a particular flag to the HAL:
flags |= HAL_TXDESC_NOACK;
Both changes and the rationale behind it were already available in the mailing list
of the Madwifi driver7. The changes were ported to the Ubiquiti driver by the author
and by Arsham Farshad and then made available in a git version control repository
for better interaction with other researchers at the WiMo group 8.
7http://thread.gmane.org/gmane.linux.drivers.madwifi.devel/61608The repository is accessible with git clone git@github.com:jhairtt/ubnt-hal-0.7.379.git.
Chapter 4. Experimental study of the impact of channel width on network performance22
0
5
10
15
20
25
30
35
40
6 9 12 18 24 36 48 54
Thr
ough
put -
Nod
e B
(Mbp
s)
MAC layer data rate (Mbps)
5MHz10MHz20MHz40MHz
0
1
2
3
4
5
6
6 9 12 18 24 36 48 54
% P
acke
t Los
s
MAC layer data rate (Mbps)
20MHz40MHz
Figure 4.4: Throughput measured indoor for different modulations and channel widths.
The maximum 27dBm transmission power was used. The 40MHz channel width was
tested on channel 42. Other channel widths were tested on channel 149.
The modified driver was then cross compiled for the ARM architecture and in-
stalled on the boards. Additionally the kismet 802.11 network sniffer was installed on
the transmitter node and iperf UDP traffic was generated from this node. The tshark
utility was used to analyze the data gathered by kimset. For instance,
tshark -r "wlan.fc.type_subtype == 0x1d && \
wlan.ra == 00:15:6d:63:a1:d5" kismet_dump_file
reports the MAC layer acknowledgments originated from the network card with
MAC address 00:15:6d:63:a1:d5.
Because of time constraints the modified driver with disabled acknowledgments
was not used for the experiments described in this research. It has been used by other
researchers at the WiMo group though.
Chapter 4. Experimental study of the impact of channel width on network performance23
-120
-100
-80
-60
-40
-20
0
20
40
60
04:00 06:00 08:00 10:00 12:00 14:00 16:00
dBm
Time (Hour)
Link QualitySignal Level
Noise
-70
-65
-60
-55
-50
-45
04:00 06:00 08:00 10:00 12:00 14:00 16:00N
oise
- N
ode
B(d
Bm
)
Time (Hour)
20MHz10MHz5MHz
28
30
32
34
36
38
40
42
44
46
48
04:00 06:00 08:00 10:00 12:00 14:00 16:00
Link
Qua
lity
- N
ode
B(d
Bm
)
Time (Hour)
20MHz10MHz5MHz
-70
-65
-60
-55
-50
-45
04:00 06:00 08:00 10:00 12:00 14:00 16:00
Sig
nal S
tren
gth
- N
ode
B(d
Bm
)
Time (Hour)
20MHz10MHz5MHz
Figure 4.5: Measured link quality, signal level and noise on the target node (top left)
during the experiments. The same data filtered by channel width is shown in the other
three graphs.
Chapter 4. Experimental study of the impact of channel width on network performance24
0
100
200
300
400
500
0 15 30
Cou
nt
Time (sec)
MAC ACKs enabledMAC ACKs disabled
Figure 4.6: Count of MAC layer acknowledgments originating from the receiver node
during a 30s flow of TCP packets using iperf. The changes made to the Ubiquiti
driver effectively dropped the MAC layer acknowledgments count to 0.
Chapter 5
Mitigating over water propagation
effects using channel width adaptation
In order to evaluate the role of different channel widths on the Tegola network it was
decided to deploy a new long-distance test link and upgrade the software on some of
the nodes.
To achieve this goal, several preliminary activities were performed some of them
with the support from researchers at the WiMo group and Prof. Peter Buneman.
5.1 Preliminary activities
1. Test the two 5.8GHz parabolic grid antennas. They were tested indoor to confirm
their proper functioning before their deployment at the locations of the nodes of
the new test link.
2. Upgrade and configure the software of several Gateworks Avila boards. The
required software to allow variable channel widths, see section 4.1, was installed
and configured by the author on several boards at the WiMo group laboratory.
The installation and configuration of the OSPF routing software quagga1 was
performed by the researcher G. Bernardi at the location of each of the pertinent
nodes.
3. Replace boards on site and deploy a new test link. G. Bernardi, researcher from
the WiMo group, and the author visited the nodes at College, Corran and Ornsay
at the Isle of Skye (Scottish Highlands) to replace the Gateworks Avila boards1http://www.quagga.net/
25
Chapter 5. Mitigating over water propagation effects using channel width adaptation26
with the ones prepared in the preliminary activity 2. The geographical location
of the nodes and the board deployed at the Corran node are shown in Figure 5.1.
With the valuable aid of Prof. Peter Buneman, Prof. Rick Rohde and G. Bernardi,
a new mast, with one parabolic grid antenna, was setup at the Li location. The
other parabolic grid antenna was attached to the mast at the Corran node.
The antenna at Li had line-of-sight with the antenna at Corran. The line-of-
sight was verified on site and also using the link planning tools RadioMobile
and SPLAT. The use of SPLAT is described in Appendix B. The masts of the
new Li↔ Corran link are shown in Figure 5.2.
Figure 5.1: Location of the nodes at the Tegola network (left). The boards on College
(57 05’ 14.6”N, 5 52’ 16.2”W), Ornsay (57 09’ 16.9”N, 5 48’ 28.8”W) and Corran (57 07’
28.4” N, 5 33’ 03.8”W) were replaced. Li (57 6’26.33”N, 5 34’44.08”W) is the new test
node. Image on the left was obtained with the SPLAT link planning tool which provides
integration with GoogleEarth. The board deployed at Corran is shown on the right.
5.2 Issues faced after preliminary activities
Once all preliminary activities were performed, the required hardware/software to eval-
uate variable channel widths on long-distance over water links was in place. Neverthe-
less, three issues delayed the start of the experiments:
5.2.1 Low signal strength on the new test link
The first issue faced after the preliminary activities was the low signal strength, be-
tween 0 and 7dBm, measured at the Li and Corran nodes as reported by the iwconfig
Chapter 5. Mitigating over water propagation effects using channel width adaptation27
Figure 5.2: Parabolic grid antenna attached to the Corran mast (left) and mast with
parabolic grid antenna deployed at the Li node (right (Picture by G. Bernardi)). The
over water link has a length of 2.5km.
tool. Thanks to Prof. Buneman both nodes were visited twice by the autor and G.
Bernardi (which required two boat trips) to crosscheck the hardware.
Days later, Prof. Buneman himself replaced the radio cards at the Corran node
to discard them as the root cause of the problem. Additionally, the signal strength
was measured with the spectrum analyzer at Corran’s shore. Although signals were
detected by the spectrum analyzer, the signal strength was not enough to transmit data
with a high data rate to the Li node.
Tests done remotely from the WiMo laboratory demonstrated that ping packets
(and even ssh traffic) sent from one node could reach the other. However, the long
latencies made the execution of performance tests not possible. It’s highly probable
that the root cause is hardware related, e.g. cabling problems or a defective board.
Fortunately, since the boards at Ornsay and Corran were replaced, it was decided
to use the Ornsay↔ Corran link for the outdoor experiments. In order to avoid inter-
ference with user traffic, the vertical polarization of the link was used and all routing
(OSPF) traffic over the vertical polarized link disabled. These routing configuration
changes were done by G. Bernardi remotely from the WiMo laboratory.
5.2.2 Production performance regression
The second issue faced was a performance regression with the Ubiquiti driver. Pro-
duction users of the Tegola network complained about low throughput short after the
new boards were deployed. With G. Bernardi it was found that the root cause was the
SampleRate rate adaptation algorithm used by the driver. After instructing the driver
Chapter 5. Mitigating over water propagation effects using channel width adaptation28
to use the Minstrel rate adaptation algorithm the performance increased but wasn’t
completely satisfactory.
A possible reason for this might be the fact that the Ubiquiti driver predates the
original Madwifi driver so performance improvements made to the latest version of the
Madwifi driver are not available in the Ubiquiti driver. However the highest measured
throughput was good enough to run experiments (see Figure 5.3).
5.2.3 Operating system crashes
The third issue was crashes of the operating system on the boards, caused by the iperf
utility. Although iperf worked reliably on the boards during the indoor experiments,
any attempt to transmit data with this tool over the Corran ↔ Ornsay link resulted
on a crash (the same problem was experienced with netserver tool). A crash of the
operating system is a worst case scenario since the boards cannot be remotely reset.
During the visit to the Ornsay node one of the users was instructed on how to reset the
boards on site.
The only difference in the software configuration between the boards used for out-
door and indoor experiments was that the boards on Tegola run the routing software
(quagga). A tool with lower memory footprint than iperf, nuttcp2 worked reliably
without causing further crashes.
5.3 Experimental results
After the issues described in section 5.2 were solved, measurements were started on
the Ornsay↔ Corran link, most of them using the implementation of Algorithm 1.
First, the throughput for different modulations with the maximum transmission
power of 27dBm was obtained. The obtained results are shown in Figure 5.3. Here,
in TurboMode (40MHz channel width), the measured maximum throughput for the 6,
12 and 24Mbps data rates exceeds the theoretical value predicted by Jun[15]. For the
54Mpbs modulation the expected throughput is within bounds though.
The next experiment evaluated the throughput and the signal strenght at the nodes
when using a fixed 6Mbps transmission rate. The results of this experiment are shown
in Figure 5.4. Here the correlation between channel width and throughput described in
Section 4.2.1 is cleary seen.
2Which is implemented in a single .c file.
Chapter 5. Mitigating over water propagation effects using channel width adaptation29
0
5
10
15
20
25
30
35
40
6 12 24 54
Thr
ough
put (
Mbp
s)
MAC Layer data rate (Mbps)
Channel 42 (TurboMode)Channel 36
Figure 5.3: Maximum application level throughput on the Corran ↔ Ornsay link ob-
tained with 27dBm transmission power and different fixed MAC layer data rates.
From the Ubiquiti XtremeRange5 radio card’s technical specifications the card re-
ceiver sensitivity to achieve a 6Mbps rate is -94dBm. As seen in Figure 5.4 the signal
strength at Ornsay is in the order of -70dBm, above the sensitivity threshold for 6Mbps.
This explains the stable throughput for autorate in Figure 5.4, i.e. the link quality is
good enough to use the predetermined fixed rate.
5.3.1 Tide levels affect the signal strength
Comparing the obtained measuruments with the tide level information provided by
the BBC weather site3 some correlation between the tide level and the signal strength
was observed. For instance, in Figure 5.4 the highest tide level (4m) measured at
20:00 matches the highest signal strength on both end nodes of the link. This peak is
consistent with the Pathloss simulation results shown in Section 3.1 (channel frequency
5180MHz).
Although the peak signal strength and the highest tide level seem to happen simul-
taneously, this experiment cannot be regarded as a proof of a cause-effect relationship
between tide level and signal strength. There are several reasons for this:
3E.g. http://www.bbc.co.uk/weather/coast/tides/tides.shtml?date=20090811\&loc=0367 for the tide level at Corran on 2009-08-11.
Chapter 5. Mitigating over water propagation effects using channel width adaptation30
Name Value
Transmitter node Corran (IP 10.3.0.1)
Receiver node Ornsay (IP 10.1.0.25)
Traffic type UDP
Measurement tool nuttcp
Transmission power 10dBm (fixed)
Data rates (MAC layer) 6Mbps (fixed) and autorate (Minstrel algorithm)
Channels (center frequency) 36 (5.18GHz), 42 (5.22GHz)
RTS/CTS disabled
MAC layer ACKs enabled
Packet size 1490bytes
Test duration 20sec
Antenna Directional (29dBi gain)
Test frequency Per minute (scheduled with cron)
Table 5.1: General parameters for the outdoor experiment on the Tegola network.
1. Granularity of the tide level data: numerical data is provided every 6 hours, while
the radio statistics are gathered automatically every 2 minutes. That’s the reason
of the saw-toothed pattern in in Figure 5.4.
2. Clock synchronization: Although the clocks of all nodes in the Tegola network
are synchronized with the ntp protocol, there is no guarantee that the clocks
are synchronized with the BBC’s computers providing the tide level informa-
tion. This can yield to shifts between the measured radio statistics and the tide
information.
5.3.2 The role of channel width adaptation
To investigate how the variable channel widths might be used as a diversity mechanism
further measurements were done over several days. For this experiment the Minstrel
rate adaptation algorithm was used on the transmitter node (Corran).
The results of this experiment are shown in Figures 5.5 and 5.6. The fluctuations in
throughput when using the 20MHz channel width are consistent with the packet loss.
Again, the correlation between channel width and throughput described in Section
4.2.1 is observed.
Chapter 5. Mitigating over water propagation effects using channel width adaptation31
0
0.5
1
1.5
2
2.5
3
3.5
08/1115:00
08/1116:00
08/1117:00
08/1118:00
08/1119:00
08/1120:00
08/1121:00
08/1122:00
08/1123:00
08/1200:00
08/1201:00
08/1202:00
08/1203:00
Thr
ough
put (
Mbp
s)
Time (hour)
20MHz10MHz5MHz
-80
-78
-76
-74
-72
-70
-68
-66
-64
-62
-60
08/1115:00
08/1116:00
08/1117:00
08/1118:00
08/1119:00
08/1120:00
08/1121:00
08/1122:00
08/1123:00
08/1200:00
08/1201:00
08/1202:00
08/1203:00
Sig
nal S
tren
gth
- O
rnsa
y (d
Bm
)
Time (Hour)
20MHz10MHz5MHz
-95
-90
-85
-80
-75
-70
-65
-60
-55
-50
08/1115:00
08/1116:00
08/1117:00
08/1118:00
08/1119:00
08/1120:00
08/1121:00
08/1122:00
08/1123:00
08/1200:00
08/1201:00
08/1202:00
08/1203:00
Sig
nal S
tren
gth
- C
orra
n (d
Bm
)
Time (Hour)
20MHz10MHz5MHz
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
08/1116:00
08/1118:00
08/1120:00
08/1122:00
08/1200:00
08/1202:00
Tid
e Le
vel -
Cor
ran
(m)
Time (Hour)
Tide Level
Figure 5.4: Throughput, signal strength and tide level for an experiment with 6Mbps
data rate, 10dBm transmission power and channel 36 (5.18GHz).
Nevertheless the measured signal strengths at the receiving node (Figure 5.5, top
right), are not the expected. For instance, all channel widths seem to provide similar
signal strengths, i.e. it’s not possible to discriminate which channel width is optimal at
a given moment. Most importantly, these results seem to contradict the indoor results
shown in Figure 4.5, where the channel width was correlated with the signal strength
at the receiver.
On the other hand the measured signal strengths at the transmitter (Figure 5.5,
bottom left) show a more reasonable trend and provide insight on how the channel
widths can be adapted over time in such a way that the signal strength is maximized.
Given the radio card sensitivities (shown in parenthesis on the y-axis of Figure
5.5), it’s important to notice the default channel width (20MHz) doesn’t provide the
optimal signal strength over time. For instance, in the context of the experimental
data, the 20MHz channel’s quality is good enough to exploit a 54Mbps rate on the
Chapter 5. Mitigating over water propagation effects using channel width adaptation32
0
5
10
15
20
25
30
08/1716:00
08/1720:00
08/1800:00
08/1804:00
08/1808:00
08/1812:00
08/1816:00
08/1820:00
08/1900:00
08/1904:00
08/1908:00
Thr
ough
put (
Mbp
s)
Time (hour)
20MHz10MHz5MHz
-86(24Mbps)
-83(36Mbps)
-77(48Mbps)
-74(54Mbps)
-70
-65
-60
-55
08/1716:00
08/1720:00
08/1800:00
08/1804:00
08/1808:00
08/1812:00
08/1816:00
08/1820:00
08/1900:00
08/1904:00
08/1908:00
Sig
nal S
tren
gth
- O
rnsa
y (d
Bm
)
Time (Hour)
20MHz10MHz5MHz
-86(24Mbps)
-83(36Mbps)
-77(48Mbps)
-74(54Mbps)
-70
-65
-60
-55
-50
-45
08/1716:00
08/1720:00
08/1800:00
08/1804:00
08/1808:00
08/1812:00
08/1816:00
08/1820:00
08/1900:00
08/1904:00
08/1908:00
Sig
nal S
tren
gth
- C
orra
n (d
Bm
)
Time (Hour)
20MHz10MHz5MHz
0
1
2
3
4
5
08/1716:00
08/1720:00
08/1800:00
08/1804:00
08/1808:00
08/1812:00
08/1816:00
08/1820:00
08/1900:00
08/1904:00
08/1908:00
Tid
e Le
vel -
Cor
ran
(m)
Time (Hour)
Tide Level(m)
Figure 5.5: Throughput, signal strength and tide level for an experiment with auto rate
(Minstrel algorithm), 10dBm transmission power and channel 42 (5.21GHz). Data was
obtained running Algorithm 1 over several days.
radio card (given the radio card sensitivity) at 4:00. However at 8:00 both the 5MHz
and the 10MHz channel widths provide better link quality. If the goal is to maximize
throughput the 10Mhz channel could be preferred over the 5MHz channel in this case.
Chapter 5. Mitigating over water propagation effects using channel width adaptation33
0
500
1000
1500
2000
2500
3000
3500
4000
4500
08/1716:00
08/1720:00
08/1800:00
08/1804:00
08/1808:00
08/1812:00
08/1816:00
08/1820:00
08/1900:00
08/1904:00
08/1908:00
UD
P p
acke
ts lo
st
Time (hour)
20MHz10MHz5MHz
Figure 5.6: Packet loss at the Ornsay node using auto rate (Minstrel algorithm), 10dBm
transmission power and channel 42 (5.21GHz).
Chapter 6
Conclusions and future work
The simulation of a long-distance wireless link using the Pathloss software contributed
to explore and understand how different diversity mechanisms (namely center of fre-
quency, polarization and antenna height adaptation) mitigate the negative effects of
tide levels on the signal strength.
Studying the different forms of diversity raised the question whether the adaptation
of the channel width might be a feasible mechanism to mitigate the effects of tide lev-
els on link quality. After a process involving the upgrade and deployment of software
supporting variable channel widths on real hardware, the effect of channel widths on
different network variables was investigated. The obtained experimental indoor repro-
duced previous findings by Chandra[3].
The deployment of the necessary upgrades to support variable channel widths on
the Tegola network, allowed the evaluation of the effect of variable channel widths in
the presence of tidal effects. The outdoor experimental results suggest that adapting
the channel width improves the signal strength at the transmitter.
Additionally, the expertise gained by the author on-site, i.e. 1) by deploying the
boards on the existing Tegola nodes and the 2) setting up the new Li ↔ Corran link
might be useful in the future to setup long-distance links using IEEE 802.11 technolo-
gies in other locations. The lessons learned from this stage of the project can help
future researchers using the Tegola network as a testbed.
Several lines of future research can be derived from this work. While the synchro-
nization algorithm described in 4.2 works at application level and requires an additional
channel for message passing, algorithms which don’t require an additional interface,
but perform negotiation of the channel width and center of frequency at the MAC layer
could be developed. The implementation of such algorithms might require to heavily
34
Chapter 6. Conclusions and future work 35
modify the radio card driver.
Second, the measurements on the Corran ↔ Ornsay long-distance link, show the
behavior of a high quality link with virtually no interference. The role of channel
widths in noisy long-distance links might be worth exploring.
Finally, the simulation results obtained with Pathloss can be used to help extend
network simulation software such as Qualnet1 with a new adaptation protocols that
include support for variable channel widths and that take decisions based on models of
tidal effects.
1http://www.scalable-networks.com/products/developer.php
Appendix A
Basic concepts
The concepts defined in sections A.1 and A.2 are based on the given in the books [16]
and [17].
A.1 Signals
Every electromagnetic signal can be modeled as a function of time or a function of
frequency. As a function of time s(t) a signal is characterized by its amplitude (A),
frequency ( f ) and phase (ϕ). For digital communications the fundamental (periodic)
signal is the sine signal: s(t) = Asin(2π f t +ϕ), with s(t) the peak amplitude (usually
in volts) at time t.
A real-world signal don’t comprise only one frequency but many. In particular, the
periodic square wave signal with amplitude A used for digital communications (a signal
that can be used to carry 1s and 0s) can be approximated with the sum of different sine
signals with different, in theory an infinite number of, frequencies1:
s(t) =4Aπ
∑k∈N(odd)
1k
sin(2πk f t) (A.1)
The spectrum of a signal is the range of frequencies comprised by it. For example,
the signal of amplitude 1 described by
s(t) =4π(sin(2π f t)+
13
sin(2π(3 f )t)+15
sin(2π(5 f )t) (A.2)
has a spectrum in the range [ f . . .5 f ]. The signal width (Sw) is the width of the
signal’s spectrum. A.2 has a width of 5 f −1 f = 4 f .1By definition s(t) is an infinite sum however the value of the terms in the sum tend to 0 as k tends
to ∞.
36
Appendix A. Basic concepts 37
Signal width and data rate are related. Suppose the square wave signal A.2 with
f = 1MHz = 106cycles/second. The width of the signal is 4 f = 4MHz and its period is
T = 1/ f = 10−6seconds. Assuming that 2 bits (a 1 followed by a 0) can be carried by
each cycle, a bit can be transmitted every 0.5µs, that is at a rate of 2 ·106bits/second =
2Mbps2.
Given a signal with a spectrum in the range [α . . .β] the center frequency of the
signal is given by Sc = (β−α)/2. It’s said such a signal is centered about frequency
Sc. In the previous example s(t) is centered about Sc = 2MHz with width Sw = 4MHz.
A.2 Channel characterization
A communication channel (C) is composed by a physical medium and a device capable
of transmitting information in signal form. When applying an arbitrary periodic signal
s(t) to C, say the square wave signal in equation A.3, the channel attenuates/delays the
input signal. The output signal of the channel is an attenuated/delayed signal s′(t)
s′(t) = ∑k∈N(odd)
akA(k f )sin(2πk f t +ϕ(k f )) (A.3)
with A(k f ) the ratio of the amplitude (power) of the input and output signals at
frequency k f (attenuation factor) and ϕ(k f ) the change in phase between the output
and input signals at frequency k f (delay factor). The combination of A and ϕ can be
understood as a filter that modifies the frequency components of a signal. The width of
the range of frequencies (spectrum) [β . . .γ] that a channel, understood as a filter, allows
to pass to the medium is called the channel width (Fw). The range of frequencies passed
by a channel can also be thought as centered about a frequency Fc = (γ−β)/2 with
channel width Fw. A channel can be characterized by the pair 〈Fc,Fw〉.
2It’s possible to obtain a different data rate given the same signal width by using a different squarewave signal. I.e. the same width might support different data rates.
Appendix B
Link planning with SPLAT
This appendix briefly describes the way SPLAT was used to verify the line-of-sight
nature of the Li ↔ Corran link. Unlike other link planning tools like RadioMobile,
SPLAT and command-line based and free software (GNU GPL license).
The following input data is given to SPLAT to obtain the line-of-sight and Longley-
Rice analysis.
• Site location files: These files contain the geographical coordinates of the link’s
nodes. For the test link the contents of the files where:
$ cat li.qth
Li
57.107313
5.578911
9 meters
$ cat corran.qth
Corran
57.12455
5.551056
44 meters
• Antenna radiation pattern files: The azimuth and elevation files are used to de-
fine the direction of the radiation emitted by the antenna. The files are called
corran.az and corran.el, analogous for the Li node. The radiation patterns
are shown in Figure B.1.
• Terrain files. The terrain files were obtained from http://dds.cr.usgs.gov/
srtm/version2/SRTM3/Eurasia/.
• Longley-Rice parameters. The following file was used:
80.000 ; Earth Dielectric Constant (Relative permittivity)
5.000 ; Earth Conductivity (Siemens per meter)
38
Appendix B. Link planning with SPLAT 39
20
15
10
5
0
5
10
15
20
15 10 5 0 5 10 15 20
dBm
dBm
Azimuth antenna radiation pattern used for path analysis
Antenna gain
0.04
0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
dBm
dBm
Elevation antenna radiation pattern used for path analysis
Antenna gain
Figure B.1: Directional antenna’s azimuth (left) and elevation (right) radiation pat-
terns used for SPLAT.
301.000 ; Atmospheric Bending Constant (N-Units)
5400.000 ; Frequency in MHz (20 MHz to 20 GHz)
5 ; Radio Climate
1 ; Polarization (0 = Horizontal, 1 = Vertical)
0.50 ; Fraction of situations
0.90 ; Fraction of time
With
splat -t li.qth -r corran.qth -kml -metric
The Longley-Rice analysis results are written to the Li-to-Corran.txt file. The
Li-to-Corran.kml is also generated and can be imported into GoogleEarth to visu-
alize the line-of-sight between the two nodes. This visualization is shown in Figure
B.2.
Appendix B. Link planning with SPLAT 40
Figure B.2: GoogleEarth visualization of the deployed Li↔ Corran test link.
Appendix C
Greedy edge coloring implementation
During the first days of the project the graph theoretical approach to center of frequency
allocation was explored. As mentioned in Section 1, Dutta[2] proposes an algorithm
for channel allocation using this approach.
The main objective of this contribution was to gain insight on the implementation
of simple graph theoretical algorithms relevant for static channel allocation, using the
C++ Boost Graph Library (BGL)1.
The problem of edge coloring is to assign a color (or weight) to all edges in a graph,
in such a way that no two adjacent vertices get the same color. An optimal solution
finds a minimal number of weights to color the graph. Let G = (E,V ) an undirected
graph. Two edges e1,e2∈ E are adjacent if they share a vertex. This problem is in NP,
i.e. there is no generic deterministic algorithm that solves it in polynomial time.
However, a greedy algorithm for edge coloring, though not optimal, is very near to
the optimal solution and surprisingly very fast. Algorithm 2 was implemented using
the BGL. The implementation is able to color the edges of graph with 50 edges and
nearly 800 vertices in less of a half of a second2.
1http://www.boost.org/doc/libs/1_39_0/libs/graph/doc/index.html2On an Intel(R) Core(TM)2 Duo CPU T5450 @ 1.66GHz CPU.
41
Appendix C. Greedy edge coloring implementation 42
Algorithm 2 Greedy edge coloring1: procedure GREEDYEDGECOLORING(G = (E,V )) . G undirected graph
2: color map← /0
3: while |color map|< |E| do4: e← random edge(G) . Pick an edge randomly
5: if e /∈ color map then6: c← ad jacent colors(e,G) . Get the colors of the adjacent edges
7: color map[e]← max(c)+1 . e has lowest possible color
8: end if9: end while
10: return color map
11: end procedure
The greedy algorithm isn’t optimal. Moreover, the use case of the algorithm is to
assign centers of frequency, the maximum number of colors obtained by the algorithm
might exceed the number of available channels. A naive approach which just uses
the modulo operation to constraint the number of different colors to the number of
available channels is shown in Algorithm 3. Obviously Algorithm 3 won’t preserve
the invariant of the edge coloring algorithm, i.e. adjacent edges might receive the same
color.
Algorithm 3 Channel mapping1: procedure CHANNELMAPPING(G = (E,V ),color map,num channels)
2: num colors← max(color map)
3: if num colors <= num channels then4: nop . There are enough channels to color the edges
5: else6: for entry ∈ color map do7: entry.color = entry.color%num channels . Limit to channels
8: end for9: end if
10: return color map
11: end procedure
Here an example of usage of the implementation of algorithms,
$ graph_driver --input-file=graph.txt --nc 4
Appendix C. Greedy edge coloring implementation 43
Number of channels is: 4
Input file is: graph.txt
Result:
(0,3)->2
(0,1)->3
(3,2)->1
(2,0)->6
(3,4)->6
(1,3)->7
(2,4)->5
(4,1)->4
(4,0)->1
Max color:7 (Number of channels:4)
Adjusting mapping. There are no enough channels to color the edges!
(0,3)->3
(0,1)->4
(3,2)->2
(2,0)->3
(3,4)->3
(1,3)->4
(2,4)->2
(4,1)->1
(4,0)->2
The C++ implementation of the algorithm is available with git at the URL git@
github.com:jhairtt/graph_driver.
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