1. FACULTY OF ENGINEERING
DEPARTMENTS OF ELECTRONICS ENGINEERING AND AUTOMATION ENGINEERING
PIRAEUS UNIVERSITY OF APPLIED SCIENCES
MSc IN NETWORKING AND DATA COMMUNICATIONS
COURSEWORK
MODULE:
CI7110: Data Communications
ID:___ K1465167______
Module Coordinator:
Dr. Hercules Simos , Dr. Charalampos Patrikakis
Date of Module:
14/05/2016
Name of Student:
Michael Xevgenis
Module:
Module Coordinator:
Kingston University London

2. FACULTY OF ENGINEERING
DEPARTMENTS OF ELECTRONICS ENGINEERING AND AUTOMATION ENGINEERING
PIRAEUS UNIVERSITY OF APPLIED SCIENCES
Subject: Use case: B2B connection over a microwave link and the impact of
BER on TCP throughput.
Submission Date: 14/5/2016_____________________________________
Grade (%):__________________________________________________
% Grade reduction because of submission delay: _____
(5% Grade reduction per every day of Cwk delay).
Final Grade (%): ________________________________________
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Kingston University London

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Abstract— The majority of the companies, nowadays, are
seeking for solutions in order to establish connections with their
branch offices. These connections should satisfy the
requirements relatively to data rate, availability and cost
effectiveness. The scope of this paper is to study a scenario of
two branch offices that are located in two different islands in
Greece, the one is at Paros island and the other is at Naxos
island, which will be connected via a microwave backhaul link
LOS. This connection will be used in order to connect the two
different computer infrastructures of those two branches. In
this paper an analysis of the microwave link will take place
where we will focus on the behavior of the link for different
SNR levels. Furthermore, we will examine how the behavior of
the link may influence the functionality of the networking
communication between the two branches focusing on the
average TCP throughput in the end to end communication.
Additionally, a simulation of the microwave link will take place
and also a simulation of the network implementation will be
presented. The results of those simulation tests will be examined
and useful conclusions will be produced.
Index Terms— Microwave link, QAM, SNR, TCP,
throughput
I.INTRODUCTION
The demand of high speed and reliable
communication increases rapidly as the technology
evolves. The services that are used in a business to
business (B-to-B) communication require high data
rate and reliable channels. It is a fact that every
company is interested in solutions that ensure the
aforementioned criteria and are affordable in terms
of finance. In our use case, the company is searching
for a solution in order to connect the two branch
offices, the one of Paros with the other that is
located at Naxos. The company has to options for
the point-to-point communication concerning the
medium. The medium may be wired, using optical
fiber under the sea, or may be wireless using a point-
to-point microwave link.
To begin with, the wired solution is more
expensive than the wireless solution in order to be
established and maintained. On the one hand, the
optical fiber solution may offer higher data rate and
reliability than the wireless. On the other hand, a
microwave point-to-point link may offer the

desirable data rate and reliability and it would be
easier in matters of troubleshooting and
implementation and as a result cheaper in matters of
finance. In our use case we will select the wireless
solution as the two points that will be connected
have line of sight and their distance is 10km. In
addition, the desirable data rate between those two
offices is between 0.5-1Gbps. In our scenario, the
desirable data rate will be corresponded to the
average TCP throughput of the end to end
communication.
In this paper, a planning of the communication
model that will be used will take place and an
analysis of the modulation that will be used will be
presented. In this particular scenario we will
examine the bit error rate (BER) of the link
relatively to the signal to noise ratio (SNR) and the
modulation order. Additionally, based on the Mathis
formula we will proceed to the calculation of the
average TCP throughput of the end to end
communication and we will focus on the impact of
the BER to the average TCP throughput in several
cases.
In section 1, a planning of the communication
channel will be presented and a theoretical analysis
of the modulation, which will be used, will take
place. In section 2, an analysis of the Mathis formula
will take place and the network topology will be
presented. In section 3, a simulation of the
communication model will take place using Matlab
Simulink and useful information will be extracted. In
section 4, a simulation of the network based on
measurements from the previous simulation, will be
presented and information relatively to the network
part will be extracted. Finally in section 5, useful
conclusions will be presented relatively to the results
of our simulations.
II. A THEORETICAL APPROACH FOCUSING
ON THE PHYSICAL PART
In this section a theoretical analysis of the
communication channel will be presented. The
analysis will focus on the physical part and the part
of channel coding and modulation and their relation
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Use case: B2B connection over a microwave link and the impact
of BER on TCP throughput.
Xevgenis Michael, Postgraduate student Kingston University, Athens
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with SNR. In order to achieve a point-to-point
communication, we will use directional antennas
which will have line of sight propagation. The
carrier frequency which will be used is fc= 10 GHz
and the bandwidth will be B= 1GHz (fmin= 9.5 GHz
and fmax= 10.5GHz). In the present use case we will
perform channel coding and modulation as the block
diagram describes below.
Figure 1. Block diagram of the communication channel
In this particular use case we perform channel
coding with encoding rate ¾, using convolutional
codes, puncturing and interleaving. The Bernoulli
binary generator represents the traffic that is
generated from the one branch and is directed to the
other. Furthermore a Quadrature Amplitude
Modulation (QAM) is used in order to convert bits
to analog symbols and send them over the
microwave link to the QAM demodulator which acts
reversibly. Then channel decoding is performed
.Finally the other branch office is able to acquire the
information.
a) The capacity of the channel
In order to design the microwave link for this
particular scenario the parameter of channel
capacity should be considered. The capacity of
the channel denotes the maximum data rate that
can be transmitted under certain conditions via a
communication channel. The capacity of the
channel derives from the below mathematical
equations. [2]
The capacity of the channel can be described
according to Shannon (supposing that only
additive white Gaussian noise is present):
(1) where B: the
available bandwidth
SNR: the signal to noise ratio
Also the maximum capacity of the channel can
be described according to Nyquist:
(2), where B: the available
bandwidth
M: the modulation order
In our scenario we take advantage of the
mathematical formulas above as we suppose that
only additive white Gaussian noise is present in
our channel. In our use case the available
bandwidth is 1GHz and the SNR and the
modulation order M, are variable. The fact that
the medium which is used is the air, therefore it
is an unguided medium, the SNR parameter may
produce variations. Bursty errors may occur due
to SNR variations and therefore it is important
to provide mechanisms to secure the information
that will be transferred.
b) Channel coding
As we afore mentioned the wireless link
produces variations relatively to SNR due to its
nature. Therefore, it is important to secure our
information during the propagation in order to
reach the destination safely. The mechanism that
our system uses is the channel coding. In the
previous figure we clearly see, the convolutional
coding blog, the puncturing blog and the
interleaving block. The coding rate in the coding
systems varies. Popular coding rates are , and
. In this particular scenario we will use the
coding rate that derives after the puncturing.
The convolutional codes add redundancy bits in
a quasi-continuous manner and they do not
divide source data streams into blocks. A
convolutional encoder consists of a shift register
with L memory cells and N adders (modulo-2)
as the below figure displays [1]. This kind of
codes and also the block codes are able to
perform error detection and correction, therefore
we may meet them in many applications [2]. In
addition, the channel coding is characterized also
by the coding rate that in the most of the systems
is adaptive. The coding rate represents the
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redundancy bits that we use in a system. It is
worth mentioning that a basic problem that we
face with encoding is the reduction in spectral
efficiency.
Figure 2. Convolutional encoder
Another technology that is used in order to
protect our information is the interleaving. As
we clearly see in the block diagram above the
interleaving is usually after the coding and its
role is to spread and mix the coded information.
This property is beneficial especially in channels
where bursty errors occur. In our use case it is
very beneficial because the channel is the air
which is a very sensitive one. By spreading the
information, the probability of saving a part of
the information or even more the hole
information increases.
c) The Quadrature Amplitude Modulation (M-
QAM)
As we fore mentioned the channel that we use in
our scenario is wireless therefore the medium is
analog. In order to transmit our digital data we
have to convert them into analog waveforms.
The modulation is able to implement that kind of
conversion. Additionally, the type of modulation
determines if the analog waveform represents
one bit or a group of bits. There are three main
types of digital modulation the ASK (Amplitude
shift keying), the FSK (Frequency shift keying)
and the PSK (Phase shift keying). From those
main three types derive many other categories of
modulation (i.e BPSK, BFSK, QPSK, QAM). In
our scenario we will use the M-QAM type.
The Quadrature Amplitude Modulation is a
combination of ASK and PSK. By using QAM
we can transmit two different signals
simultaneously over the same currier frequency
(fc) by using two copies of the fc that differ by
90o
. According to QAM every currier is ASK
modulated. These two independent signals are
transmitted simultaneously over the same
medium and at the receiver these two signals are
combined in order to produce the original binary
form [2]. In order to realize how this modulation
operate we will procced to an example. If a two
level ASK is used then each stream could be
only in the one of the two levels so 2 2=4
levels. Similarly, if we use 4 level ASK then we
will have 4 4=16 levels. The QAM modulation
is described by the below mathematical equation.
(3)
[1]
Figure 3. The QAM modulator
This type of modulation is commonly used in
wireless communications and usually includes an
index M that denotes the order of the
modulation. The higher the order, the higher the
data rate that we may achieve and also the
higher the bit error rate (BER) that might derive.
Nowadays, in wireless communications we may
use 64-QAM, 256-QAM and in some cases we
can use higher order i.e 1024-QAM. In our
simulation part we will perform experiments by
changing the M factor and the SNR of our
channel, while we keep stable the coding rate in
order to realize the changes in BER factor. [2]
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Figure 4. Constellation diagram of 256-QAM
III.A THEORETICAL APPROACH FOCUSING
ON THE NETWORK PART
In this section of the paper we will procced to a
theoretical analysis of the network part of our use
case. Based on the OSI layer we will focus on the
network layer (layer 3) and transport layer (layer 4).
A network topology will be designed according to
our use case, where there are two branch offices that
have their own local area networks (LAN) and the
goal is to connect those two LANs via the
microwave link. The network of each branch
consists of routers, switches and terminals that
should be properly configured. Based on the above
scenario we proceed to the design of the network
topology. The two different routing methods will be
discussed and compared, the static routing and the
dynamic routing. Furthermore, an analysis of the
TCP throughput will be presented based on the
Mathis formula and an analysis of the variables that
may influence the average TCP throughput will take
place.
Figure 5. Network topology
The above network topology is based on our
scenario and displays the LANs of the two branch
offices. The branch office of Paros consists of a
router, a switch and three terminals. The router has
two Gigabit Ethernet interfaces where the one is
used for the LAN and the other is used for WAN.
The switch is connected to the router via a Gigabit
Ethernet interface and connects to the terminals via
Ethernet interfaces. The same structure exists in the
branch office of Naxos. As the figure above displays
the interfaces are configured with the appropriate
ips. Finally, the figure above also displays the
network that will be simulated.
a) Static routing vs Dynamic routing
In the previous section we focused on the physical
layer (layer 1) of OSI model. In this part we will
bypass the data link layer (layer 2) and we will focus
on network layer activity. The network layer is
responsible for the communication between
networks and subnets. This layer is responsible for
the proper routing of the information. There are two
main categories of routing, the static and the
dynamic routing. However, it is in our hands which
method we may use.
In static routing the network administrator has to
configure manually the routing tables of the routers
in order to provide connectivity. This type of routing
is preferred in small networks which may not have
potentials for further extension. Nevertheless, if the
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networks are big or medium and are extensible the
afore mentioned technique is not efficient.
The dynamic routing uses protocols which are able
to locate networks, automatically update the routing
tables of the routers and connect them according to
the administrator’s weal. This method of routing is
easier than the static routing but requires routers
with strong CPU and bandwidth due to overhead.
Additionally, dynamic routing is preferred in
medium to large networks and sometimes in small
networks due to its simplicity. There are many
protocols that can be used for dynamic routing.
Some of them are the RIPv1 and RIPv2 which
nowadays are not very popular, the OSPF protocol,
the BGP and the EIGRP.
In our use case, the networks are small and for
educational purposes we implemented static routing.
In the table below we observe the differences of
dynamic and static routing and we are able to
distinguish the advantages and disadvantages in each
method. [3,4,5]
Table 1. Dynamic routing vs Static routing
b) The average TCP throughput
In this part of the paper we will focus on the
transport layer and especially on the TCP protocol.
In our use case our goal is to realize how the low
layer conditions influence the throughput of TCP. In
order to achieve our goal, we will use the Mathis
formula which includes the MSS (maximum segment
size) variable, the RTT (round trip time) variable
and the L (Loss) variable. An analysis of those three
variables will be presented below.
Mathis formula
(4) [6]
The MSS is an option in the TCP header and
describes the amount of data that can be transmitted
or received by a computing system in a single TCP
segment. The MSS includes the payload and does
not include the headers. Additionally, we should
mention that in cases where there are several
fragments of the same packet the MSS refers to the
reconstructed segment that is located at the
destination and it is measured in bytes. This option
of the TCP is able to change by the network
administrator if it is necessary, however, in our use
case we will set it at 1500 Bytes and we will not
change it. Furthermore, the RTT is the time that
takes to a packet to reach the destination and then
return to the source. We should mention that the
RTT is measured in milliseconds, and the desired
value is the minimum value.
The L (Loss) factor is the effect of different kind of
losses in the TCP throughput and may contain
packet loss, corrupted packets, packet delay due to
processes, packet delay due to transmission, packet
delay due to propagation, packet delay due to
queuing delay [7]. It is worth mentioning that the
packet delays may cause packet loss, in example,
when there is a queuing problem the buffer of the
router may become full and other packets that
arriving will be dropped due to buffer overflow [8].
As we clearly see there are several sources that may
cause a problem and they are all included in the
factor L. In our use case we will assume that the L
factor represents the BER which in our use case will
vary according to our simulation tests from the
physical part of the paper. Finally based on our
previous assumption we may extract a useful
conclusion relatively to the effect of the lower levels
of OSI model on the network layer and especially on
the TCP throughput. [3, 6]
IV. THE SIMULATION OF THE PHYSICAL
PART
In this part of the paper we will proceed to the
simulation of the physical section based on the block
diagram that has been presented. During our
simulation we will use encoding rate of ¾ after
puncturing and we will change only the modulation
order in the QAM modulator while we will also
change the SNR level of the communication channel.
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Dynamic routing Static routing
Configuration
complexity
Generally independent
of network size
Increases with
network size
Required
administrator
knowledge
Advanced knowledge
required
No extra
knowledge
required
Topology
changes
Automatically adapts to
topology changes
Administrator
intervention
required
Scaling Suitable for simple and
complex topologies
Suitable for
simple topologies
Security Less secure More secure
Resource usage Uses CPU, memory,
link bandwidth
No extra
resources needed
Predictability Route depends on the
current topology
Route to
destination is
always the same
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Our goal is to extract the values of the BER for
numerous combinations of modulation and SNR.
In our simulation we will assume that the upper
threshold of the proper functionality of our model
will be the 0.5 value of BER. For simplicity, we use
an AWGN noise component and we will use also
modulation order of 64 QAM and 256 QAM.
Furthermore , as we afore mentioned in the
theoretical part of the physical section the
communication model is designed to operate in a
specified bandwidth with a specified carrier
frequency and the capacity formulas will contribute
in order to calculate the capacity of the channel.
Table 2. 256-QAM
Modulatio
n Order
Encoding
Rate
SNR
(dB)
Maximu
m
Capacity
Nyquist
(Gbps)
Capacit
y of the
channel
Shannon
(Gbps)
BER
256-QAM 3/4 80 16 26.6 0
256-QAM 3/4 60 16 19.9 0
256-QAM 3/4 40 16 13.3 0
256-QAM 3/4 20 16 6.65 9*10-8
256-QAM 3/4 15 16 5.03 0.0079
256-QAM 3/4 10 16 3.45 0.5
256-QAM 3/4 5 16 2.06 0.5
Table 3. 64-QAM
Modulatio
n Order
Encoding
Rate
SNR
(dB)
Maximu
m
Capacity
Nyquist
(Gbps)
Capacit
y of the
channel
Shannon
(Gbps)
BER
64-QAM 3/4 80 8 26.6 0
64-QAM 3/4 60 8 19.9 0
64-QAM 3/4 40 8 13.3 0
64-QAM 3/4 20 8 6.65 0
64-QAM 3/4 15 8 5.03 5*10-7
64-QAM 3/4 10 8 3.45 0.0447
64-QAM 3/4 5 8 2.06 0.5
The tables above provide us with useful information
relatively to the conditions of the channel and the
modulation that is used. Although the 256-QAM is
able to achieve higher data rate, this order of
modulation is less robust than the 64-QAM. Due to
our channel’s nature the SNR value may vary and
thus the BER may change frequently. In our use
case the communication channel is able to function
properly even if the BER is 10-7
. Therefore, we may
use 256-QAM when the SNR is at 20dB and when
the SNR is above the 20dB we may use 64-QAM
where the BER is acceptable.
Figure 6. BER figure of 256-QAM and 64-QAM
Additionally, the figure above describes the behavior
of 256-QAM and 64-QAM relatively to the BER.
The aforementioned observations are confirmed for
numerous values of noise and we are able to realize
the difference in matters of robustness when we
increase the modulation order from 64 to 256. The
results of this simulation will be used in the
simulation of the network part in order to realize the
effect of the BER in the average TCP throughput of
the link.
V. THE SIMULATION OF THE NETWORK
PART
In this part of the paper the behavior of the average
TCP throughput will be examined relatively to the
BER that is produced due to noise. The goal is to
implement the network topology which is presented
in section 2 by using static routing and then the
Mathis formula will be used in order to calculate the
values of the average TCP throughput. In our
simulation we will use 1941 Cisco routers and
2950T-24 switches therefore we will use the CLI of
cisco in order to configure the routing. According to
the network topology we will use three networks
where the one network is the 192.168.1.0/24 and is
the Paros network, the other is the 192.168.2.0/24
which is the Naxos network and the last network is
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the 10.0.0.0/30 which in the network that connects
the two routers.
It is important to mention that the Paros network
and the Naxos network use a network mask /24
which means that we are able to connect 28
-2= 254
hosts. However, in our use case we will use only
three hosts for each networks for simplicity.
Furthermore, it is worth mentioning that when we
configure the static routes we should configure each
route in both sides of the network in order to
achieve the desired communication. In order to
configure a static route for Cisco routers we use the
command ip route <destination network> <network
mask> <via the interface that is able to communicate
with this particular network>. Finally, when we
finish with the implementation of our network
topology and the configuration of static routing we
will use the Mathis formula to calculate the average
TCP throughput. The Mathis formula has been
presented in section 2.
In our use case we will set the RTT= 10 ms and the
MSS= 1500 bytes as constants and only the L
parameters, that in our case is L= BER, will be a
variable. The table below shows us the value of
average TCP throughput according to Mathis in
each of our cases.
Table 4. Averrage TCP throughput
Modulation
Order
Encoding
Rate
SNR
(dB)
Max
Capacity
Nyquist
(Gbps)
Shannon’s
Bound
(Gbps)
BER Avg.
TCP
throu-
ghput
256-QAM 3/4 20 16 6.65 9*10-8
0.76
Gbps
64-QAM 3/4 15 8 5.03 5*10-7
0.32
Gbps
The content of the table above is based on the
previous tables of the physical part simulation. The
figure below describes the behavior of the TCP
throughput in several values of BER. The average
TCP in this figure is measured in bits per second. It
is obvious that when the bit error rate decreases the
avg. TCP throughput increases. Considering that the
routing method that we used was the static so we do
not add extra overhead into our network, we may
achieve high data rate point to point communication.
Figure 7. Average TCP throughput and BER
However, we should also mention that when the
BER increases the avg. TCP throughput decreases.
Considering that our medium is the air, bursty errors
may occur and when the BER is between 10-8
and
10-7
we may switch to lower modulation order i.e 64
QAM in order to maintain high TCP throughput.
VI.CONCLUSIONS
During our study we focused on the physical part
and on the network part of our use case. Relatively
to the physical part we examined how the high order
QAM behaves in a use case that demands high data
rate and in an environment which may present
various values of SNR due to noise. The results that
we produced are reasonable considering the way
that high order modulation works relatively to noise.
In our use case we used only 256-QAM and 64-
QAM and we did not change the encoding rate. Due
to the transmission medium that we used we may
suggest that in case of a low SNR and high BER we
could use lower order modulations in order to keep
our communication more robust to error, however,
that means that the data rate may decrease rapidly.
We may also suggest that we could use different
encoding schemes and error detection, error
correction techniques that may keep the bit error
rate in lower values.
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Relatively to the network part of our study, we
design the network topology and we proceed to an
analysis of the routing methods that we may use.
Additionally, we tried to calculate the average TCP
throughput using the Mathis formula. Our goal was
to relate the average TCP throughput with the BER
and observe the effect of the physical part (lowest
OSI layer) on the network part (layer 3 of OSI
stack). During the network implementation we
familiarized with the Cisco router configuration and
the way that static routing operates. Finally, we
calculated the average TCP throughput for various
values of BER, assuming that only BER influences
the average TCP throughput. The results were
reasonable, considering the BER variations and its
effect relatively to the modulation that was used.
Focusing on the BER vs Noise and on the average
TCP throughput vs BER curves we can realize the
similarity.
VII. FUTURE WORK
In this particular paper we made some assumptions
in order to study the physical and networking
behavior of our system focusing on the noise. It
would be very interesting to examine our scenario
by involving additional factors such as free space
loss, multipath fading and other factors that may
influence the operation of the link. Also, it will be
interesting to examine more modulation and
encoding schemes as we did in this paper.
Additionally, the design of the antennas that we may
use is also a field that should be examined.
Furthermore, in the network part it will be very
interesting to examine the overhead that dynamic
routing may produce in our system. Also, we may
examine factors that may lead to packet loss, delays
or other problems. These factors could be added in
the loss field of Mathis formula.
REFERENCES
[1] Wireless Communications, Second Edition Andreas F.
Molisch , Fellow, IEEE
University of Southern California, USA
[2] Wireless Communications and Networks, Second Edition
William Stallings
[3] Computer Networking : A Top-Down Approach , Fourth
Edition James F. Kurose Keith W. Ross
[4] CCNA Routing and Switching Study Guide-Lammle ,Todd
[5] Cisco CCNA Routing and Switching ICDN2 200-201 Official
Cert Guide,Wendell Odom CCIE
[6] The Macroscopic Behavior of the TCP Congestion Avoidance
Algorithm , Pittsburg Supercomputing Center, Matthew
Mathis, Jeffrey Semke, Jamshid Mahdavi
[7] Scheme for Measuring Queueing Delay of a Router Using
Probe-Gap Model: The Single-Hop Case Khondaker M.
Salehin; Roberto Rojas-Cessa IEEE Communications Letters
Year: 2014, Volume: 18, Issue: 4
Pages: 696 - 699, DOI: 10.1109/LCOMM.2014.030114.132775
IEEE Journals & Magazines
[8] Packet loss performance due to buffer overflow at transmitter
side of wireless link S. E. Hong; C. G. Kang Electronics Letters
Year: 2002, Volume: 38, Issue: 23
Pages: 1432 - 1434, DOI: 10.1049/el:20020996 Cited
by: Papers (1) | Patents (3) IET Journals & Magazines
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