This document summarizes an experiment analyzing the adaptivity of modulation mode and error correction coding rate in 802.11g wireless networks based on channel conditions. The experiment measured signal strength and throughput at different locations in an indoor environment. Simulation results modeling different modulation modes and coding rates under varying noise levels were also presented and compared to experimental measurements. The analysis found that more robust modulation/coding combinations were selected in noisier channel conditions to maintain reliability and throughput. Adding an additional access point was recommended to further improve quality of service for users.

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Abstract—Nowadays the majority of people are users of the
Wi-Fi and their demands of a better Quality of Service are
increasing day by day. Most of users use the 802.11g standard
in their indoor environment and they enjoy their connection on
the Internet. In order to satisfy the user's demands, this
connection should always be reliable and stable. That is why
802.11g has mechanisms to achieve this goal. In this thesis we
will analyze the mechanisms of digital modulation adaptivity
and Forward Error Correction adaptivity in an indoor
environment depending on the coverage conditions of the
channel. Based on coverage measurements and channel
conditions in different scenarios (including a real indoor
experiment) we will record the RSSI's (Received Sign al
Strength Indicator) and the Throughput's thresholds where we
can locate the change of the modulation mode and the change of
FEC in 802.11g networks. Afterwards, by setting a desirable
level of service (Quality of Service, QoS) and knowing the fore
mentioned thresholds we will recommend a new topology by
using one more access point. For the indoor experiment we used
the open source programs inSSIder and Netstress and for the
simulation we used Matlab.
Index Terms—802.11g ; Throughput ; RSSI ; BER; QoS;
BPSK;QPSK;QAM;OFDM;FEC
I.INTRODUCTION
In the last decades the need for a better quality of service in
Wlan systems increases constantly. Many devices such as
mobile phones, tablets and other devices are able to be
connected wireless to the internet. In order to establish that
kind of connection we create the 802.11 Wi-Fi protocol. One
of the variations of 802.11 protocol is the 802.11g (f0=2.4
GHz).At the present, the Wi-Fi and the usage of 802.11g
protocol is widespread. The Wi-Fi is applicable for business,
education and home environments, that is why it is so
popular.
Although the 802.11 is a protocol that provides easy
connectivity to the user, that type of connectivity is not always
reliable and stable. The Wi-Fi uses the air as a medium to
communicate with the devices via electromagnetic signals.
The electromagnetic signal has to overcome the conditions of
the channel, which influence the channel propagation. The
channel conditions are variable. The radio channel has severe
delay spread due to multipath propagation. The delay spread
causes "Inter Symbol Interference"(ISI), which influences the
throughput (data rate) [1],[2].Also extra neighboring
sources
that transmit in the same or adjoining channel cause inter-
symbol interference. Another factor that influences the

propagation is the SNR (Signal to Noise Ratio) problem that
occurs due to the distance between transmitter and receiver
and also due to the non-line of sight connection. Therefore,
channel estimation is very important for finding suitable
modulation and coding schemes.
The different modulation schemes such as BPSK (Binary
Phase Shift Keying), QPSK (Quadrature Phase Shift Keying)
and QAM (Quadrature Amplitude Modulation) with different
encoding rates such as 1/2, 2/3, 3/4 are the solutions for these
problems. By using these mechanisms we achieve better
values of Bit Error Rate (BER) and so our transmitted
information arrives with safety to the node(end user).Another
mechanism that we use is the OFDM(Orthogonal Frequency
Division Multiplexing). OFDM is based in inverse Fourier
transform and is a mechanism that reduces the phenomenon
of interference from multipath and neighboring channels. As
we know in the field of communications, there is always a
tradeoff. In that case, the profit is a more stable channel and
the loss is that the throughput (data rate) decreases rapidly
.The user also realizes that the throughput is sharply
decreased. However, the user demands higher speed and so
higher throughput .Users always demand higher quality of
service and therefore we should find a way to provide that. As
we understand these parameters are interrelated. Our goal is
to find where we should change a modulation and encoding
scheme in order to have better Quality of Service (QoS).
In this paper we will analyze the mechanisms of modulation
schemes and encoding rate and more specifically the
adaptivity of those two mechanisms in a wireless channel in
different scenarios. Also , we will cite a real word scenario
experiment where we will study our measurements that have
been selected from an indoor environment (indoor
experiment).Nevertheless , we proceed to a simulation of an
802.11g channel by using Matlab and we compare our
simulation results to our experimental results. In addition, we
use the IEEE 802.11a 1999 specifications to compare them
with our experimental results .Finally, we use another AP in
our experiment so we can achieve a better quality of service
for the user, which is our goal.
II.A THEORITICAL APPROACH
A.Modulation schemes
As we fore mentioned the wireless channel, that we use, is an
analog medium. Over this channel we have to send analog
waveforms. In order to achieve that, the digital modulator at
the transmitter has to convert the digital data, which is
produced from the source, to analog waveforms. The receiver
is responsible for recovering the bits from the received
Wi-Fi 802.11g: Adaptivity of modulation mode and
encoding rate for an improved Quality of Service
(January 2015)
Xevgenis M. Author, Postgraduate Student, Kingston University of London
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waveform by using the digital demodulator. The type of
modulation that we use determines if the analog waveform
represents one bit or a group of bits. By choosing a
modulation scheme in a wireless system, we determine the
bandwidth and the energy that we want to use in order to
transmit as much information as possible over the channel
and assuring a certain transmission quality (BER).There are
two basic criteria that we should consider before choosing a
modulation mode:
-The spectrum efficiency of the modulation mode should be
as high as possible. We can accomplish that by using a
"higher order" modulation mode, so we can transmit many
data bits with each symbol.
- The interference from adjacent channel and from multipath
propagation must be small. In addition, the analog signal
must be filtered before the transmission.
-The sensitivity with respect to noise should be very small. By
using a low order modulation the waveforms of the alphabet
are largest.
- Robustness with respect to delay and Doppler dispersion
should be as large as possible. The signal that we transmit
should be filtered as little as possible because filtering creates
more delay and dispersion.
- Waveforms should be easy to generate with hardware that is
easy to produce and it is highly energy efficient. That means
that the hardware should be able to generate the necessary
waveforms.
Many of the above criteria are contradictory. So considering
the above criteria we should choose a modulation scheme
according to the requirements of our system and application
[3].
B.Modulations schemes and Constellation diagrams
The binary modulation BPSK is the simplest because we can
represent +1-bit value with one specific waveform and -1-
bit value with a different waveform. In this modulation
scheme the carrier phase is shifted by ±π/2. In BPSK the
number of bits is equal to the number of symbols.
Figure 1. Constellation diagram BPSK
A Quadrature-Phase Shift Keying (QPSK)-modulated signal
is a Pulse Amplitude Modulation. The signal carries 1bit per
symbol interval on both the in-phase and quadrature-phase
component. The QPSK is twice more efficient than BPSK.
The reason is that, both the in-phase and the quadrature-
phase components are exploited for the transmission of
information. However, the signal shows strong envelope
fluctuations.
Figure 2. Constellation diagram QPSK
Higher Order QAM transmits multiple bits in both the in-
phase and the quadrature-phase component .It does so by
sending a signal with positive or negative polarity, as well as
multiple amplitude levels, on each component [3].
Figure 3. Constellation diagram 16QAM
Figure 4. Constellation diagram 64QAM
C.Encoding Rate
The convolutional codes that we use, can give us a specific
subset of coding rates, like Rc = 1/2, 1/3, 1/4. The easiest
way, which we use for the adaption of encoding rate, is by
structuring a code that has a rate lower than the desired.
Certain bits of the coded sequence are excused from the
transmission. Considering that, the code includes
considerable redundancy that is similar to the loss of code-
bits due to fading in the physical layer. Therefore it is not a
problem. In this situation it is required that the "punctured"
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bits are appropriately distributed throughout the code-word,
so that not all information about a certain bit is loosed.
A basic problem that we face with the encoding is the
reduction in spectral efficiency. Considering that we have to
transmit more bits, we need more bandwidth as we add check
bits. We can control the dimensions of this problem by using
higher order modulation schemes. The higher order
modulation schemes allow the transmission of more bits with
the usage of the same bandwidth. Finally, we assume that
modulation and encoding are designed as a "joint process".
Their combination gives us the result that we search for [3].
Table 1. 802.11a-1999 [4]
The above table displays the change of modulation mode and
coding rate according to the change of the actual throughput
(Data rate in Mbits/s) that ends up to the user. This table is
very important and helpful for our study because we will
compare our experimental and simulation results with the
content of that table, in order to realize when and how the
modulation scheme and the encoding rate change.
III. AN OVERVIEW OF THE SIMULATED SYSTEM
A.Introduction to our Simulink model
In this section we will proceed to the simulation of the
wireless channel. The simulation will help us to understand
the propagation of the signal from the source until the
destination where we receive the information. We will run a
few tests with deferent modulation schemes and encoding
rates in different scenarios. These scenarios differ in the field
of noise. Considering that the ultimate goal of 802.11g is to
maintain a reliable and stable connection, the factor that
concerns us is the BER (Bit-Error Rate).The flow chart below
displays an overview of the simulated system.
Figure 5. Simplified simulation diagram
As we fore mentioned we will run a few simulation tests in
order to realize how the electromagnetic signal changes. The
modulation schemes that we use are: BPSK with encoding
rate 1/2 , BPSK with encoding rate 3/4 ,QPSK with encoding
rate 1/2 ,QPSK with encoding rate 3/4, 16 QAM with
encoding rate 1/2 ,16 QAM with encoding rate 3/4 ,64 QAM
with encoding rate 2/3 and 64 QAM with encoding rate 3/4.It
is very important to understand the behavior of the signal
with the above modulation schemes and encoding rate with
different values of SNR. The value of SNR represents the
field of noise. Before we begin our simulation we should
make clear that as the SNR value decreases the noise
increases. It is important to mention that our simulation
model is built according to the specifications of IEEE 802.11
g.
Based on our experimental results, we will use the values of
SNR that we measured to our simulation model and we will
run the simulation to find the value of BER. Based on the
values of BER we will order the modulation schemes and the
encoding rates according to their robustness. As we evaluate
the performance of an indoor 802.11g channel we took
samples of SNR when we have non-line of site
communication and when we have an interfere signal. We
used the channel 13 in our access point and here are the
results of SNR in different degrees and meters.
Table 2. Calculated SNR
degrees 1
m
2m 3m 4m 5m 6m 7m 8m
157 - - - - 7.46 11.55 7.34 11.98
180 - - - - 22.32 15.24 13.52 2.95
202 - - - - 17.37 18.31 - -
315 - 14.97 11.96 - - - - -
In order to calculate the Signal to Noise ratio we use the
following type :
SNR(dB)=Preceived(dBm) - Pinterference(dBm) (1)
B.ResultsTable 3. BER in deferent SNR
values
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C.Discussion
The table above displays the BER value for each modulation
and encoding schemes in different values of SNR. According
to the results from our simulation, we can realize that the
BPSK modulation with encoding rate 1/2 is the most robust
scheme. The way that we have sorted the modulations
schemes and the encoding rates reflects the order from the
most robust combination to the least robust combination.
Based on the Table 1 we realize that the throughput is
influenced from the combination of modulation and encoding
rate. So the connection between the node and the access point
is based in channel estimation. According to the SNR value
and in addition, the BER, the connection is influenced and
therefore the combination of modulation and encoding rate
changes ,in order to have a reliable and stable connection. At
this point we can realize the adaptivity of modulation and
encoding rate according to the channel estimation.
As we fore mentioned according to the channel estimation the
access point and the node agree to the combination of
modulation and encoding rate that they use. It is very
important for us to realize where the change happens.
Therefore we set a BER threshold, which is set to 10-5
[5]. We
compare the BER values to our BER threshold and we can
locate where the change of modulation and encoding rate
takes place. If BER ≥ BERthreshold the modulation and the
encoding rate changes. However, when BER<BERthreshold the
modulation and the encoding rate does not change. Also,
when the BER value reaches the 0.5 we assume that we do
not have connection.
IV. INDOOR EXPERIMENT
A.Introduction to our indoor experiment
In this section we proceed to a real world experiment. The
ultimate goal of our experiment is to study the performance of
an indoor channel, which uses 802.11g protocol. We will
examine how and where the modulation mode and encoding
rate change according to the conditions of the channel, in order to
have a stable and reliable connection. To achieve our
experiment we use a HUAWEI Echolife HG520c access point
that we configure to transmit in 802.11g mode. We choose
the channel 13 because in that channel we do not have many
interfere signals. Assuming that the signals are transmitted
orthogonally the only interference that we have is by access
points that transmit in the same channel. We use the insider
[6] program to measure the signals that our NIC receives.
Furthermore, we use the Netstress [7] program to measure the
throughput that our NIC receives. The inSSIder and the
Netstress are open source programs. Our NIC is a Realtek
RTL8188CE Wireless LAN 802.11n COMBO PCI-E NIC.
Furthermore, we divide our indoor environment into sections
according to meters and degrees from our access point in
order to make a radio coverage of our environment. In that
way we cover all the possible points that the node can be. In
addition we calculate the signal that our NIC receives and in
that way we calculate the interferences from other access
point that transmit to the same channel. As our measurements
are in dBms we calculate the Signal to Noise ratio in Table 2
BER 2.95
dB
7.34
dB
7.468
dB
11.5
dB
11.961
dB
11.979
dB
13.524
dB
14.968
dB
15.243
dB
17.37
dB
18.3
dB
22.3
dB
BPSK
1/2
0 0 0 0 0 0 0 0 0 0 0 0
BPSK
3/4
0.0007 0 0 0 0 0 0 0 0 0 0 0
QPSK
1/2
0.13 0 0 0 0 0 0 0 0 0 0 0
QPSK
3/4
0.44 0.02 0.016 0 0 0 0 0 0 0 0 0
16QAM
1/2
0.4 0.02 0.016 0 0 0 0 0 0 0 0 0
16QAM
3/4
0.49 0.34 0.33 0.009 0.0045 0.0043 0.0002 0 0 0 0 0
64QAM
2/3
0.5 0.48 0.48 0.32 0.27 0.27 0.116 0.027 0.019 0.0005 0 0
64QAM
3/4
0.5 0.5 0.5 0.4 0.38 0.38 0.254 0.113 0.09 0.008 0.002 0
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by the type (1).For the previous procedure we use the
inSSIder. However, we calculate our throughput in Mbps by
using the Netstress. To accomplish these measurements we
connect our desktop computer with our access point via
Ethernet and then we transmit and receive TCP frames from
our laptop to our desktop. We use only TCP with MTU size
1500 bytes. Finishing with our experiment we gather our
measurements and we create diagrams in order to evaluate
our channel's performance.
B.Experimental results
In this part we gather our measurements and we create
diagrams. Below we display two diagrams, each diagram
refers to the type of propagation that we have. The first
diagram refers to line of sight propagation (LOS) and the
second refers to non-line of sight propagation (NLOS).
Diagram 1. Line of sight propagation
The diagram above shows us the throughput that our NIC
detects, according to the meters and degrees that we stand.
The lines that are colored symbolize the degrees that we are
located. In this diagram we have line of sight propagation.
The diagram below displays in the same way, the throughput
that we detect in non-line of sight propagation.
Diagram 2. Non-line of sight propagation
C.Discussion
As we fore mentioned the throughput that we receive depends
on the conditions of the channel. However, we can realize
that our measurements from our experiment differ from those
that we gather from our simulation. The fact that we have this
difference is because we measure a real channel performance
in an indoor environment. That is why we can accept the fact
that when we have line of sight propagation and we are
located 90 degrees and 1 meter away from our access point
we have 22Mbps throughput.
Nevertheless, it is clear that we have a better channel
performance when we have line of sight propagation than
when we have non-line of sight propagation. Based on our
measurements and on our Table 1 we can locate what type of
modulation and encoding rate we use each time. Combining
our measurements and the Table1we can also locate where
the modulation mode and the encoding rate changes. Now we
can realize the adaptivity of modulation and encoding
schemes in order to have a stable and reliable connection.
The node realizes that the throughput decreases as it moves
away from the access point and that fact influences his data
rate. Also, in some situations the connection drops and we
have no service. In order to have a stable and reliable
connection and furthermore a better Quality of service we
place another same access point with the same configuration
at the point that we have low throughput. We decide to place
our second access point 202 degrees and 5 meters away from
the first access point. Then we divide our area into sections
and we set our center at the point of the second access point.
Then we repeat our experiment and we create diagrams with
our extracted measurements.
Diagram 3. Access Point 2 (LOS)
Considering that we place the second access point in a
different place of our indoor environment it is reasonable to
have different measurements. Now we cover a larger section
when we have line of site propagation as we can see from the
diagram above.
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Diagram 4. Access Point 2 (NLOS)
Nevertheless, the measurements that we have when we have
non-line of sight propagation are taken when we are 1 meter
away from our second access point. The 45 degrees of the
second access point match to the 202 degrees of the first
access point that we use. So the 202 degrees and 4 meters
point of the first access point matches to the 45 degrees and 1
meter point of the second access point. That is a detail that
we will exploit by using the Handover mechanism in order to
achieve a better channel performance and a better Quality of
service.
V. CONCLUSIONS
Finishing with our current study we end up with conclusions.
Firstly, it is clear that the combination of modulation schemes
and the encoding rates is determined by the channel
characteristics. Also we achieved to realize how and when we
have changes in the modulation mode and in the encoding
rate according to the specification and according to our
measurements. We also made clear how the adaptivity of the
modulation mode and encoding rate influences the node and
how we can achieve better Quality of Service .Furthermore,
we saw the difference between simulation and experiment
and we were able to make a channel estimation.
REFERENCES
[1] K. Deok Soo, etal, "Performance analysis of OFDM system
based on IEEE 802.11a", Int. Tech. Conf. on Circuits, Systems,
Computers and Communications, July, 2002.
[2] H. Kyung Son and H. Soo Lee, "Performance analysis of
802.1 la WLAN in real indoor environments", IEEE Ant. &
Prop. Society Int. Symposium, Jun, 2003.
[3] Wireless Communications, Second Edition Andreas F. Molisch , Fellow,
IEEE
University of Southern California, USA
[4] Part 11: Wireless LAN Medium Access
Control (MAC) and Physical Layer (PHY)
specifications
High-speed Physical Layer in the 5 GHz Band, IEEE Std 802.11a-1999
[5] A QoS-based Rate Adaptation Strategy for IEEE a/b/g
PHY Schemes using IEEE 802.11e in Ad-hoc Networks
[6] InSSIder. http://inssider.en.softonic.com/
[7] Netstress. http://nutsaboutnets.com/netstress/
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Diagram 4. Access Point 2 (NLOS)
Nevertheless, the measurements that we have when we have
non-line of sight propagation are taken when we are 1 meter
away from our second access point. The 45 degrees of the
second access point match to the 202 degrees of the first
access point that we use. So the 202 degrees and 4 meters
point of the first access point matches to the 45 degrees and 1
meter point of the second access point. That is a detail that
we will exploit by using the Handover mechanism in order to
achieve a better channel performance and a better Quality of
service.
V. CONCLUSIONS
Finishing with our current study we end up with conclusions.
Firstly, it is clear that the combination of modulation schemes
and the encoding rates is determined by the channel
characteristics. Also we achieved to realize how and when we
have changes in the modulation mode and in the encoding
rate according to the specification and according to our
measurements. We also made clear how the adaptivity of the
modulation mode and encoding rate influences the node and
how we can achieve better Quality of Service .Furthermore,
we saw the difference between simulation and experiment
and we were able to make a channel estimation.
REFERENCES
[1] K. Deok Soo, etal, "Performance analysis of OFDM system
based on IEEE 802.11a", Int. Tech. Conf. on Circuits, Systems,
Computers and Communications, July, 2002.
[2] H. Kyung Son and H. Soo Lee, "Performance analysis of
802.1 la WLAN in real indoor environments", IEEE Ant. &
Prop. Society Int. Symposium, Jun, 2003.
[3] Wireless Communications, Second Edition Andreas F. Molisch , Fellow,
IEEE
University of Southern California, USA
[4] Part 11: Wireless LAN Medium Access
Control (MAC) and Physical Layer (PHY)
specifications
High-speed Physical Layer in the 5 GHz Band, IEEE Std 802.11a-1999
[5] A QoS-based Rate Adaptation Strategy for IEEE a/b/g
PHY Schemes using IEEE 802.11e in Ad-hoc Networks
[6] InSSIder. http://inssider.en.softonic.com/
[7] Netstress. http://nutsaboutnets.com/netstress/
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