Performance study of IEEE 802.16d (Fixed WiMAX) Physical layer with Adaptive Antenna

Performance study of IEEE 802.16d (Fixed WiMAX) Physical layer with Adaptive Antenna
MEE08:10
Performance study of IEEE
802.16d (Fixed WiMAX) Physical
layer with Adaptive Antenna
System
Ahmer Ali Bajwa
Junaid Anwar Awan
This thesis is presented as part of Degree of
Master of Science in Electrical Engineering
Blekinge Institute of Technology
October 2008
Blekinge Institute of Technology
School of Engineering
Department of Applied Signal Processing
Supervisor: Dr. Jörgen Nordberg
Examiner: Dr. Jörgen Nordberg
ii
Abstract
In this thesis work, WiMAX (IEEE 802.16d) PHY layer with underlying OFDM
technology and an optional feature called Adaptive Antenna System has been
considered. The SUI-3 channel model (Rician fading) is used for creating fading
phenomena. An Adaptive Antenna System has been deployed at the receiver module
to reduce the fading effects caused by SUI-3 channel model. Adaptive Antenna
Systems (AAS) uses different beamforming techniques to focus the wireless beam
between the base station and the subscriber station. In this thesis, the transmitter
(SS) and receiver (BS) are fixed and AAS installed at the receiver is used to direct
the main beam towards the desired LOS signal and nulls to the multipath signals.
Pre-FFT beamformer based on Least Mean Square (LMS) algorithm is used. Different
aspects of the complete system model were investigated such as adaptive
modulation, angle of arrival of the incoming signals and number of array elements. It
has been demonstrated through MATLAB simulations that the performance of the
system significantly improves by AAS, where beamforming is implemented in the
direction of desired user. The performance of the system further increases by
increasing the number of antennas at receiver.
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Acknowledgments
We wish to extend our heartfelt gratitude to all who have supported us during this
endeavor.
We would like to express our deepest gratitude with bundle of thanks to our
Supervisor, Dr. Jörgen Nordberg for his support, enlivening guidance and interest in
our work efforts during the development of this thesis. We are also thankful to
Blekinge Institute of Technology – Department of Telecommunciation & Applied
Signal processing for allowing us to carry out the code excution work using MATLAB
in department’s labs.
We would also like to take a moment here and show our gratitude and love for our
parents who helped us and keep us motivated all along during our their thesis work
with their unconditional love, care and support.
Finally we express our deep apreciation to all of our friends and seniors , especially
Asher for his kind concern, guidance and moral support.
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Table of Contents
Abstract……………………………………………………………………………………………..iii
Acknowledgement………………………………………………………………………………..v
Table of contents…………………………………………………………………………………vi
List of Figures……………………………………………………………………………………viii
List of Tables...…………………………………………………………………………………….x
Chapter 1- Introduction…………………………………………………………………..……1
1.1 Motivation.…………………………………………………………………………………………………………..…1
1.2 Objective………………………………………………………………………………………………………………..2
1.3 Assumptions.…………….…………………………………………………………………………………………..3
1.4 Organization of Thesis……………………………………………………………………………………………3
Chapter 2- WiMAX: An Overview…………………………………………………………….4
2.1 What is WiMAX.........................................................................................4
2.2 WiMAX: How does it works…….………………………………………………………………………………4
2.3 WiMAX: Standards.…..……………………………………………………………………………………………5
2.3.1 802.16-2004….…………………………………………………………………………………………….6
2.3.2 802.16-2005……………………………………………………………………………………………….6
2.4 WiMAX: Physical Layer.…………………….………..…………………………………………………………7
2.4.1 Features of 802.16d OFDM PHY Layer………………………………………………………..9
2.5 WiMAX: MAC Layer..………………………………………………………….…………………………………11
2.6 Comparison of WiMAX with other wireless technologies.……………………………………12
2.7 WiMAX Advantages & drawbacks…….….…………….….……………………………………………14
2.8 OFDM…….………………………………………………………………………………………………………………15
2.8.1 What is OFDM.……………………………………………………………………………………………15
2.8.2 OFDM Advantages & drawbacks……….………………………………………………………16
2.9 Smart Antennas.……………………….…………………………………………………………………………17
2.9.1 What are Smart Antennas...………………………………………………………………………17
2.9.2 Switched Beam Antenna System………………………………………………………………18
2.9.3 Adaptive Array Antenna System.………………………………………………………………18
2.9.4 SAS Benefits & drawbacks……..…………………………………………………………………20
Chapter 3- System Model….…………………………………………………………………22
3.1 Transmitter Module………………………………………………………………………………………………22
3.1.1 OFDM Symbol Description.……………………………………………………………………….23
3.1.2 The Block Diagram………..………………………………………………………………………….24
a. Random Data Generation.…………………………………………………………….25
b. Modulation....…………………………………………………………………………………25
c. Pilot Modulation.…..………………………………………………………………………26
d. IFFT….……………………………………………………………………………………………27
e. Cyclic Prefix Insertion……………………………………………………………………27
3.2 Channel Module……………………………………………………………………………………………………28
3.2.1 Additive White Gaussian Noise (AWGN) channel..……………………………………31
3.2.2 Stanford University Interim Channel Models..………………………………………….32
3.2.3 SUI-3 Channel Model Simulation………………………………………………………………33
3.3 Receiver Module……………………………………………………………………………………………………37
3.3.1 Antenna Array Model…………………………………………………………………………………37
3.3.2 Pre-FFT Beamforming…..……………………………………………………………………………40
3.3.3 Adaptive Beamforming algorithm used…….………………………………………………40
3.3.4 The Block Diagram………………………………………………………….…………………………41
3.4 The Complete Simulation Block Diagram……………………………………………………………43
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Chapter-4 Simulation Results………………………………………………………………44
4.1 Simulation Environment………………………………………………………………………………………44
4.2 Uncoded BER Plots……….………………………………………………………………………………………45
4.2.1 SISO case…………………………………………………………………………………………………45
4.2.2 SIMO case…………….…………………………………………………………………………………50
Chapter-5 Conclusion & Future Work……………………………………………………60
5.1 Conclusion…………….………………………………………………………………………………………………60
5.2 Future Work………….………………………………………………………………………………………………61
Reference……………………………………………………………………………………….…62
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List of Figures
Figure 2.1 Point to multipoint deployment scenario with WiMAX base station
Figure 2.2 Frequency Division Multiplexing (FDM) Spacing is put between two
adjacent sub-carriers
Figure 2.3 Orthogonal Frequency Division Multiplexing (OFDM) Sub-carriers are
closely spaced until overlap
Figure 2.4 Switched beam System
Figure 2.5 Switched Beam (Left) and Adaptive Array (Right)
Figure 2.6 WiMAX BS with multiple antennas and AAS
Figure 2.7 Block diagram of an Antenna Array System
Figure 3.1 Time domain structure of OFDM Symbol
Figure 3.2 Frequency domain structure of OFDM Symbol
Figure 3.3 Transmitter Module
Figure 3.4 QPSK, 16QAM and 64 QAM constellations
Figure 3.5 PRBS for Pilot Modulation.
Figure 3.6: LOS and Multipath Scenario.
Figure 3.7: Co-channel Interference Scenario.
Figure 3.8: Received Signal passed through an AWGN channel
Figure 3.9 SUI channel model structure
Figure 3.10 Magnitude plot
Figure 3.11 Antenna Array Model
Figure 3.12 Receiver Module
Figure 3.13 Simulation Block Diagram
Figure 4.1 BPSK under AWGN
Figure 4.2 QPSK under AWGN
Figure 4.3 16QAM under AWGN
Figure 4.4 64QAM under AWGN
Figure 4.5 BPSK under SUI-3
Figure 4.6 QPSK under SUI-3
Figure 4.7 16QAM under SUI-3
Figure 4.8 64QAM under SUI-3
Figure 4.9 Uncoded BER for receiver having 6 antennas and employing QPSK
modulation
Figure 4.10 Array Factor of a 6 element antenna array in presence of LOS signal and
reflected rays Linear Plot
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Figure 4.11: Array Factor of a 6 element antenna array in presence of LOS signal and
reflected rays Polar Plot
Figure 4.12 LMS Absolute Error vs. Iterations
Figure 4.13 Array Factor of a 6 element antenna array in presence of LOS signal and
reflected rays Linear Plot
Figure 4.14 Array Factor of a 6 element antenna array in presence of LOS signal and
reflected rays Polar Plot
Figure 4.15 LMS Absolute Error vs. Iterations
Figure 4.16 Array Factor of a 6 element antenna array in presence of LOS signal and
reflected rays Linear Plot
Figure 4.17 Array Factor of a 6 element antenna array in presence of LOS signal and
reflected rays Polar Plot
Figure 4.18 LMS Absolute Error vs. Iterations
Figure 4.19 Uncoded BER for receiver having 9 antennas and employing QPSK
modulation
Figure 4.20 Array Factor of a 9 element antenna array in presence of LOS signal and
reflected rays Linear Plot
Figure 4.21 Array Factor of a 6 element antenna array in presence of LOS signal and
reflected rays Polar Plot
Figure 4.22 LMS Absolute Error vs. Iterations
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List of Tables
Table 2.1 Comparison between Fixed and Mobile WiMAX
Table 2.2 WIMAX 802.16d: Uplink & downlink with different Modulation & Coding
Schemes
Table 2.3 Comparison of WiMAX with other wireless technologies
Table 3.1 OFDM Symbol Parameters
Table 3.2 SUI-3 Channel Parameters
Table 4.1 Eb/No required to attain BER level at 10
−0.34
Table 4.2 BER level required to attain Eb/No=15dB
x
Chapter – 1
Introduction
1.1 Motivation
What has been seen and observed in recent years is a remarkable increase in the
Broadband Wireless Access (BWA) networks as the need for broadband and mobile
services are getting into demand. BWA is increasingly acquiring a great deal of
popularity as an alternative "last-mile” technology to DSL and cable modems.
In today’s world a large number of wireless transmission technologies exist. These
technologies are distributed over different network families depending upon the
network scale such as PAN, WLAN, WMAN and WAN. As the demand for data
transmission with higher rates changed so is the focus on the deployment of wireless
networks. Technologies that promise to deliver higher data rates are attracting more
and more vendors and operators towards them. One of the most promising
candidates of such arising technologies is WiMAX. Many researchers do believe that
WiMAX can move the wireless data transmission concept into a new dimension.
There are basically three limiting factors for transmitting high data rate over the
wireless medium that mainly include multipath fading, delay spread and co-channel
interference [1]. The published WiMAX standard (802.16d) [2] describes a MAC layer
and five physical layers, each suitable for particular application and frequency range.
Wireless MAN-OFDM is one of them [3]. The Wireless MAN-OFDM interface can be
extremely limited by the presence of fading caused by multipath propagation and as
result the reflected signals arriving at the receiver are multiplied with different
delays, which cause Intersymbol interference (ISI). OFDM basically is designed to
overcome this issue and for situations where high data rate is to be transmitted over
a channel with a relatively large maximum delay. If the delay of the received signals
is larger than the guard interval, ISI may cause severe degradations in system
performance. To solve this issue multiple antenna array can be used at the receiver,
which provides spectral efficiency and interference suppression [4]. Adaptive Antenna
System (AAS) is an optional feature in IEEE 802.16d standard but to enhance the
coverage, capacity and spectral efficiency, it should be essential for an OFDM air
1
interface. It has an advantage of having single antenna system at the subscriber
station and all the burden is on base station [3]. An array of antenna is installed at
the base station to reduce inter-cell interference and fading effects by providing
either beamforming or diversity gains. When small spacing is adopted, the fading is
highly correlated and Beamforming techniques can be employed for interference
rejection as compared to Diversity-oriented schemes [5]. As a result receiver can
separate the desired LOS signal from the multipath signals and nulls are formed at
the interfering signals.
1.2 Objective
The objective of this thesis work is to understand the physical layer of Fixed WiMAX
(802.16-2004) standard in particular by analyzing the OFDM technique. The optional
feature Adaptive Antenna Systems is also part of thesis work. AAS being used to
overcome the effect cause by multipath fading. Implementation of the OFDM
transmitter module, SUI-3 channel Model and the Receiver module for IEEE 802.16d
Base station in MATLAB. The performance metric used is Uncoded Bit Error Rate
(BER) and the beam pattern for the beamformer is illustrated.
In brief the objective of our thesis work can be summarized in the following way:
Step-1: Implementation of OFDM Transmitter Module
Step-2: Implementation of Channel Module (SUI-3)
Step-3: Implementation of Receiver Module
Step-4: BER plots for OFDM Physical layer (SISO system)
Step-5: Complete system implementation including Adaptive Antenna
System and their comparison & performance evaluation
(SIMO system vs. SISO system)
Step-6: BER plots for the complete system model
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1.3 Assumptions
Following assumptions have been used throughout in the thesis unless some thing
else have been stated.
1. Fixed transmitter and receiver are considered in thesis work
2. Channel coding module is not included
3. Signals incident on the AAS are composed of plane waves
4. Small antenna spacing is considered
5. Transmitter considered is in the far-field of an AAS
6. Mutual coupling considered negligible between antenna elements
7. Perfect time and frequency synchronization
1.4 Organization of Thesis
The thesis report is organized as follows.
Chapter2
This chapter gives a background of the work and presents a detail description of
WiMAX technology including its various standards, advantages and disadvantages of
WiMAX and in the end a brief introduction of OFDM technique and Adaptive Antenna
System used in 802.16d standard.
Chapter3
This chapter discusses in detail the structure of Fixed WiMAX Physical layer baseband
model, SUI-3 channel and the Adaptive Antenna System.
Chapter4
This chapter includes the over all simulation results that have been obtained while
working on the above mentioned scenarios. Simulation results are provided first for
the case of SISO system under the AWGN and the SUI-3 channel and then by
considering the case for SIMO (6 & 9 antenna elements) under the same channel
conditions. Finally the performance comparison of the above mentioned cases.
Chapter5
This chapter concludes the thesis work and it also includes the future work that can
be conducted by using the useful information presented in this thesis.
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Chapter – 2
Overview of WiMAX
This chapter presents an overview of WiMAX technology and brief introduction to an
OFDM technique and smart antennas. It also highlights some of the key issues
related
to
WiMAX
technology
such
as:
Standards,
Services,
Costs,
and
Advantages/Disadvantages of WiMAX.
2.1 What is WiMAX?
World Wide Interoperability for Micro Wave Access – WiMAX is the synonym given to
the IEEE 802.16 standard, that specifies a frequency band in the range between 10
GHz to 66 GHz. Basically WiMAX is a wireless internet service that is capable of
covering a wide geographical area by serving hundreds of users at a very low cost. It
particularizes a metropolitan area networking protocol that not only provides a
wireless alternative for cable, Digital Subscriber Line (DSL) and T1 level services for
last mile broadband access but also provides a backhaul for 802.11 hotspots and due
to its higher data rates WiMAX is also gaining interest in cellular sector as well [7].
2.2 WiMAX – How does it work?
WiMAX uses radio microwave technology to provide wireless internet service to
computers and other devices that are equipped with WiMAX compatible chips for e.g.
PDA’s, cell phones etc. It works more or less like cellular network technology,
because WiMAX technology also involves the use of a base station to establish a
wireless data communication link just as in the same way it is required in cellular
networks like GSM and UMTS. The theoretical range of WiMAX is up to 30 miles and
achieves data rates up to 75 Mbps, although at extremely long range that is greater
than 30 miles the throughput is closer to the 1.5Mbps [34]. WiMAX Operates in
similar manner as Wi-Fi but with two very convincing differences as compared to Wi-
4
Fi, these are data rate and data range. The typical WiMAX scenario involves a base
station normally mounted on top of the building or at some place high where it can
provide optimum coverage and a WiMAX receiver that can be in any form like for e.g.
CPE, or a Chip installed in laptops or home PCs just like a Wi-Fi chip. Now there are
two steps that make up the whole communication model in WiMAX, these steps are:
¾
Data transmission from WiMAX Receiver (CPE or WiMAX Chip) to the WiMAX
base station
¾
Data transmission from BS to backbone Internet
Figure 2.1: Point to multipoint deployment scenario with WiMAX base station [7]
Data transmission between two towers can be through a Microwave transmission link
and WiMAX BS can also be connected to the IP backbone Network using a wired
connection as shown in figure 2.1. Communication between WiMAX BS and
subscriber can be point to multipoint where as communication between two or
WiMAX BS could be in form point to point LOS.
2.3 WiMAX Standards
Before there have been several different standards that defines WiMAX, such
802.16a, 802.16d, and 802.16e. However now days there are two standards that are
addressed by WiMAX technology, the following two standards are:
5
¾
Fixed WiMAX (IEEE 802.16-2004)
¾
Mobile WiMAX (IEEE 802.16-2005)
The original WiMAX standard the 802.16 specified WiMAX for the 10 to 66 GHz range
and after the updation it became the 802.16-2004 standard specified for 2 to 11 GHz
range. The last amendment to the 802.16-2004 standard which is the 802.16-2005
standard will initially operate at 2.3 GHz, 2.5 GHz, 3.3 GHz, 3.4 to 3.8 GHz spectrum
bands. Although the above two terminologies (Fixed and Mobile WiMAX) are not
WiMAX standards but these are basically two general terms that are used commonly
all over to define the basics standards related to WiMAX technology. Both these
standards have addressed various issues; a brief comparison of these two standards
is shown below in table 2.1.
2.3.1 802.16-2 004
Earlier version known as 802.16a that was updated to 802.16-2004 (also known as
802.16d) is a WiMAX standard that supports fixed non-line of sight (NLOS) wireless
internet services thus forming a point to multipoint deployment scenario. The basic
goal of 802.16-2004 standard was to provide a stationary wireless transmission with
data rates higher then those provided by DSL and T1, this feature makes fixed
WiMAX an alternative for cable, DSL and T1. 802.16-2004 uses Orthogonal
Frequency Division Multiplexing (OFDM) for transmission of data thus serving a large
number of users in time division manner in round robin fashion. Some of the silent
features of 802.16-2004 standards are [6]:
¾
Designed to provide Fixed NLOS broadband services to Fixed, Nomadic and
Portable users
¾
256 OFDM PHY with 64QAM, 16QAM, QPSK, and BPSK modulation techniques.
¾
Support for Advance antenna and Adaptive modulation & coding techniques.
¾
Facilitates the use of point-to- multipoint mesh topology
¾
Low latency for delay sensitive services, thus improving on QoS parameters
¾
Support for both: Time Division Duplexing (TDD) and Frequency Division
Duplexing (FDD)
2.3.2 802.16-2 005
IEEE 802.16-2005 formally known as 802.16e or Mobile WiMAX is basically an
improvement of the 802.16-2004 standards. It is a bit more complex technology as
6
compared to it predecessor 802.16-2004 standard. The Mobile WiMAX allows the
convergence of mobile and fixed broadband networks through a common wide area
broadband radio access technology and flexible network architecture. Some silent
features of 802.16-2004 standards are:
¾
802.16-2005 standard offers support both fixed and mobile access over the
same infrastructure
¾
Provides improved coverage range with the use of Adaptive Antenna System
(AAS)
¾
It uses SOFDMA for transmission to carry data supporting channel bandwidths
between 1.25 MHz & 20 MHz with up to 2048 sub-carriers
¾
Combining SOFDM and MIMO: An improved spectral efficiency is obtained by
combining scalable orthogonal Frequency Division Multiplexing (SOFDM) with
Multi-input and Multi-output (MIMO) technology
¾
Provides resistance to multipath interference by deploying FFT algorithms.
¾
Provides support for optimized roaming and handover schemes to facilitate
real time VOIP applications without any degradation in service
Standard
Release
802.16-2004 WiMAX
802.16e WiMAX
802.16-2004
802.16e or 802-16-2005
(June 2005)
(December 2005)
Services
Supported
Fixed, Limited Portability
Mobile, Portable and Fixed
Applications
Data connectivity, VoIP
Service
Providers
DSL and cable modem
Mobile Operators,
Targeted
Service Providers,
DSL and Cable,
Wireless and Wired ISPs
Modem service providers, Wireless
and Wired ISPs
Outdoor or Indoor CPE,
Outdoor or Indoor CPE,
PCMCIA card for Laptops
PCMCIA card,
Subscriber Unit
Data connectivity,
Fixed and mobile VoIP
mini-card built in laptops, PDA,
Smart Phone
Started in August 2005
Certification
Certified products in January
2006
Early 2007 (Expected)
Table 2.1: Comparison between Fixed and Mobile WiMAX [7]
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2.4 WiMAX – Physical Layer
The focal point of this thesis work is mainly on the WiMAX physical layer so in this
context a brief description of WiMAX physical layer is presented. The WiMAX physical
layer depends up OFDM – A data multiplexing technique that distributes high bit rate
data over a large number of precisely spaced smaller sub carriers with each sub
carrier using a separate frequency for each carrier providing resistance against
interference thus reducing the amount of cross talk in the signal transmission. Or in
brief, the channel bandwidth is divided into multiple sub channels and information on
each channel is transmitted using different frequencies [7].
Why WiMAX uses OFDM technology for data transmissions? The answer is pretty
simple, because the huge benefits provided by this technique are far more than those
provided by existing wireless data transmission techniques. Some of the important
features concerned with the OFDM technology are:
¾
Use of Multi-carriers – As we know that narrow band signals are less sensitive
to ISI and frequency selective fading.
¾
OFDM achieves high spectral efficiency – As the FFT and IFFT operations
ensure that the sub channels do not interfere with each other.
¾
OFDM provides robustness against burst errors through the exploitation of
frequency diversity schemes.
¾
OFDM provides less complex equalization as compared to the equalization in
single carrier systems.
¾
OFDM provides effective robustness in multi-path environments.
However it must be kept in mind that the OFDM physical layer is implemented
differently in Fixed and Mobile versions of WIMAX. Fixed WiMAX uses 256 – FFT
based OFDM physical layer where as the Mobile WiMAX uses a scalable OFDMA
(SOFDMA) based physical layer and the FFT sizes in this case can vary from 128 bits
up to 2048 bits [7].
In the case of fixed WiMAX the number of sub-carriers is fixed and it is fixed to 256.
Out of the 256 sub-carriers, 192 sub-carriers are used for carrying data, 8 are used
for sub channel estimation and the rest of the carriers are used as guard band subcarriers. However in this case the spacing between the sub-carriers is directly
proportional to the channel bandwidth. It means that higher the channel bandwidth
is, the greater would be the sub-carrier spacing which ultimately results in decrease
of symbol time [7].
8
On the other hand in case of Mobile WiMAX the FFT size is scalable from 128 bits to
2048 bits, thus with the increase in the available bandwidth the FFT size also
increases. In case of Mobile WiMAX the spacing between the sub carriers is set to
10.94 KHZ [7]. This in return keeps the symbol time constant and thus produces a
minimal impact of the scaling on the higher layers. This kind of sub-carrier spacing
can support vehicular mobility at speed of 125 Km/h and supports delay spread value
up to 20 µs.
In order to enhance the range and performance of the Fixed WiMAX; a limited form
of sub-channelization is allowed in the uplink (only). It helps in link budget
improvements that can be used to enhance the range performance [7]. However in
case of Mobile WiMAX the sub-channelization occurs in both directions (i.e. uplink
and downlink). Thus different sub-channels are assigned to number of different users
by using a specific type of an access mechanism and this particular access
mechanism is called OFDMA. The establishment of the sub-channels is carried out in
the following two different ways:
1. In form of contiguous sub-carriers or
2. Sub-carriers distributed in a pseudo-randomly manner
Its noticeable here that sub-carriers formed in contiguous manner, are useful in
exploiting the multi-user diversity which provides significant gain in overall system
capacity. That is the reason why contiguous sub-carriers are more applicable and
suited for fixed and low mobility. On the other hand if channels are distributed
randomly across the frequency spectrum and more supportive for frequency diversity
then they are well suited for mobile applications. Besides these key attributes that
WiMAX physical layers perform there is another important function that physical layer
performs and that is allocation of slots and framing for wireless communication
channels. The slots that are formed using the contiguous series assigned to a
particular user are called “data region”. These data regions are assigned to different
users by various scheduling algorithms on basis of various channel conditions and
some QoS demand parameters.
2.4.1 Feat ures of 802.16d OFD M PHY layer
The following context will incite few very important features that constituent the
WiMAX 802.16d OFDM PHY layer.
9
Fl ex i bl e Ch a n ne l B a nd w i dt h
The 802.16-d Standard offers a flexible channel bandwidth so that WiMAX technology
can be compatible with other wireless technologies, which means that the bandwidth
of the channel can be adjusted according to the user requirements. The scale for
bandwidth channel flexibility starts from 1.25 MHz up to 20 MHz with channel
bandwidth selection parameters as 1.25 MHz, 1.50 MHZ, 1.75 MHz, ……………, 20 MHz
[10].
A d ap ti v e Mo d ul a ti on a nd Co d i ng
Adaptive modulation and coding process involves the radio link adjustments in
accordance with various signal coding, modulation schemes keeping in mind the
environmental factors involved (Interference, Multipath propagation, Doppler Effect
etc) affecting the signal strength during transmission [10].
Four schemes to modulate the transmitted bits have been used, and afterwards the
comparison on the transmitted data rates is taken into account for all of these four
modulation schemes. The modulation schemes used in this thesis work are:
¾
Binary Phase Shift keying (BPSK)
¾
Quadrature Phase Shift keying (QPSK)
¾
16 Quadrature Amplitude Modulation (16 QAM)
¾
64 Quadrature Amplitude Modulation (64 QAM)
The various Modulation and Coding scheme indulge in WiMAX 802.16d standard are
given below in the table 2.2 [7].
Modulation
Coding
Downlink
Uplink
BPSK, QPSK, 16 QAM, 64 QAM; BPSK
BPSK, QPSK, 16 QAM; 64 QAM
optional for OFDMA-PHY
optional
Mandatory: convolutional codes at rate
Mandatory: convolutional codes
1/2, 2/3, 3/4, 5/6
at rate 1/2, 2/3, 3/4, 5/6
Optional: convolutional turbo codes at rate
Optional: convolutional turbo
1/2, 2/3, 3/4, 5/6; repetition codes at rate
codes at rate 1/2, 2/3, 3/4, 5/6;
1/2, 1/3, 1/6, LDPC, RS-Codes for OFDM-
repetition codes at rate 1/2, 1/3,
PHY
1/6, LDPC
Table 2.2: WIMAX 802.16d: Uplink & downlink with different
Modulation & Coding Schemes [7]
10
Fo r w a rd E r ro r Co r re ct io n Co nt r ol M e ch a n is m ( FE C)
The 802.16d PHY layer provides a very robust technique for making sure the correct
data has reached the destination by using Forward Error Correction (FEC) control
mechanism which encloses redundancy in transmitted data. The 1st stage in FEC is
Reed Solomon Encoder that encapsulate the data with coding blocks and these
coding blocks are helpful in dealing with the burst errors, once that is achieved the
data is then passed towards the next process which is the convolution coding of the
data. Further more before the transmission the number of transmitted bits are
reduced by deleting certain bits and upon reaching at the receiver replacing the
deleted bits with certain fixed values through the process of Puncturing,
so that the
overall number of bits that needed to be sent on the channel is reduced [7].
A d ap t i v e A nt e nn a Sy st em
WiMAX PHY layer also provides an additional feature of adding Adaptive Antenna
System (AAS). As described earlier that the insertion of Guard Interval (GI) the
OFDM provides a resistance to multipath propagation problem, but in case if the
delay caused by multipath exceeds that GI length then Inter Symbol Interference
(ISI) happens thus resulting in signal loss. To deal with this problem of ISI AAS are
used, AAS basically subdue those multipath waves that are causing delays [4].
2.5 WiMAX – MAC Layer
The primary objective of MAC or Medium Access Control layer is to provide an
interface between the physical layer and the higher layers of the system model. The
WiMAX MAC layer takes data packets called MAC service data units or MSDUs and in
order to send MSDUs over the interface they are organized into MAC protocol data
units (MPDUs). When it comes to Compatibility WiMAX is quite compatible with
existing data communication protocols such as ATM, IP, and Ethernet etc. for this
purpose WiMAX MAC layer has a sub layer also known as a "Convergence layer". This
layer allows WiMAX enabled devices to communicate with devices using different
protocols such as ATM, IP or Ethernet etc. Besides providing an interface with higher
protocols the convergence layer also reduces the over heads for the higher layer by
suppressing the MSDU header. In order to address QoS parameters with even high
data rates the WiMAX MAC layer offers variable length MPDUs which means that
11
multiple MPDUs can be sent over the air interface in a single burst, thus resulting in
even reduce over head for the PHY layer also [7].
As from the name MAC, it can be concluded that MAC layer is responsible for defining
the mechanism in which the medium will be accessed. WiMAX being successful
mainly because of the reason that it provides a different kind of a medium access
control mechanism unlike its predecessors like WiFi where the medium or the channel
is accessed in a pseudo randomly manner. Every station tries to gain the attention of
the access point (AP), so the stations nearer to AP affect the performance of the
stations that are far from AP. In contrast WiMAX has a different mechanism for
accessing the channel. Unlike the contention based MAC layer used in WiFi; WiMAX
basically has a request – grant access mechanism similar to that used by DOCSIS
cable modem [7]. In WiMAX the bandwidth to be used in the uplink as well as in the
downlink channels is allocated by the Base Station (BS). Upon receiving the amount
of allocated bandwidth it is the MS that can distribute the aggregate bandwidth
among the multiple connections if needed to. Based on the type of traffic the
bandwidth required for the downlink is allocated without involving the MS and the
uplink bandwidth is allocated on the type of the demand the MS requested for. The
BS allocates dedicated or shared resources in periodic manner among multiple Mobile
stations through a process known as polling [7].
The time slot (capable of expanding and contracting) allocated to a client remains
reserved in at least a minimal level whether the client is actively utilizing the channel
or not.
2.6 Comparison of WiMAX with other wireless
technologies
Before WiMAX came into existence there was IEEE 802.11 standards addressed by
the WiFi forum, providing the users with wireless internet services. When WiMAX was
taken into consideration for replacing the DSL and cable modems and providing an
enhanced solution to the exiting solution (802.11 standards), no one thought that
WiMAX could be so strong and powerful that it could replace or even proves to be a
good competitor for 3G and beyond cellular networks. It’s a well known fact that
WiMAX achieves better spectral efficiency as compared to other existing wireless
communication technologies due to its higher bandwidth feature. However when its
comes to mobility WiMAX falls behind 3G network and this is basically due to the fact
that when 3G networks are designed the mobility or roaming part is one of the
essential features that 3G network must address where as in WiMAX design the main
12
goal was to provide higher bandwidths to fixed, nomadic, portable and mobile users
with the certain mobility capabilities as an extra feature. In IEEE 802.11 standard the
major problem was QoS parameters, as WiFi Forum was unable to address QoS
parameters in IEEE 802.11 standard. Table 2.3 depicts a comparison between WiMAX
and other wireless communication technologies [7].
Parameter
Standards
Fixed
Mobile
WiMAX
WiMAX
IEEE
IEEE
802.16-
802.16-
2004
2004
3.5MHz
and 7MHz
in 3.5GHZ
Bandwidth
band,
10MHz in
5.8GHz
band
Modulation
Frequency
5.8GHz
initially
IEEE
802.11a/g/n
802.11g;
20/40MHz for
802.11n
initially
64QAM
and
1.25MHz
8.75MHz
64QAM
3.5GHz
5MHz
10MHZ and
16QAM,
TDD, FDD
3GPP2
Wi-Fi
20MHz for
5MHz,
16QAM,
Duplexing
6
Rev A
7MHz,
QPSK,
TDM
3GPP Release
1x EV-DO
3.5MHz,
QPSK,
Multiplexing
HSPA
TDM/OFDM
A
QPSK, 16QAM
and
3.5GHz
16QAM,
64QAM
TDM/CDMA
CSMA
FDD
FDD
TDD
initially
2.5GHz
16QAM
BPSK, QPSK,
TDM/CDMA
TDD
2.3GHz,
QPSK, 8PSK,
800/900/1,80
0/
1,900/2,100M
Hz
initially
800/900/1,80
0/
2.4GHz, 5GHz
1,900MHz
<100ft
Coverage
(Typical)
3-5 miles
< 2 miles
1-3 miles
1-3 miles
indoor;
<1000ft
outdoors
Mobility
Not
Applicable
Mid
High
High
Table 2.3: Comparison of WiMAX with other wireless technologies
13
Low
2.7 WiMAX: Advantages and Drawbacks
Although the technology is new and yet there is lot more still to come but experts
have already mentioned certain advantages of WiMAX technology, along with the
advantages there is set of certain critics that have different views about WiMAX
technology. This section will highlight certain advantages and disadvantages of
WiMAX technology.
Some of the advantages of WiMAX technology are:
¾
Long Range: Perhaps the most significant advantage of WiMAX over
other wireless technologies is the range it provides. WiMAX has a
communication range of up to 30 miles. This can cover over 2800 square
miles meaning that it is enough to cover a medium size city.
¾
Higher Bandwidth: Before WiMAX, the existing wireless technologies
have various issues that are mostly related to the bandwidth. WiMAX
provides data rates of 40 Mbps which makes WiMAX a perfect solution
capable of replacing DSL and T1 services thus allowing a single base
station to serve hundreds of users.
¾
Low cost: Although the cost to install a WiMAX base station would be
around 20,000 $ but still it would be much less cheaper when it comes to
the deployment of wireless network that is capable of providing services as
those provided by T1 networks. This will allow faster deployment of
network and also provides a simple method for adding more members in
the existing WiMAX based network. The new addition of users in the
network would cost less as compared to what seen in case of DSL based
networks
On the other hand WiMAX do have certain drawbacks some of them are:
¾
Power Sensitive: WiMAX is basically a power sensitive technology,
meaning that it heavily relies on strong electrical support.
¾
LOS Requirement: A Line of sight is required in order to make a wireless
data communication connection extending over 6 miles or more.
Besides the above mentioned drawbacks, some other factors are also there that can
affect WiMAX efficiency. As WiMAX is wireless communication technology so like all
14
the other wireless technologies, its performance is also affected by changes in
weather conditions such as rain, fog etc.
2.8 Orthogonal Frequency Division Multiplexing
2.8.1 What is Orthogonal Frequency Division Multiplexing?
The main idea of OFDM (Orthogonal frequency division multiplexing) is to divide a
channel into a number of orthogonal sub-channels, and transform high-speed data
signals to parallel low-speed sub-data flows which are then modulated and
transmitted on each sub-channel [8].
To understand OFDM in a better way it would be helpful to start up with Frequency
Division Multiplexing (FDM).
Frequency
Figure 2.2: Frequency Division Multiplexing (FDM)
Spacing is put between two adjacent sub-carriers [9]
FDM involves the transmission of various signals from different transmitters at the
same time with each signal being transmitted at a different frequency and a proper
spacing (known as guard band) is left between the sub-carriers to avoid signal over
lapping as shown in the figure 2.2 [9].
On the other hand OFDM is slightly different technique that still follows the same idea
as that adopted by FDM but the signal spacing in case of OFDM is different. As seen
from figure 2.2, different signals placed distinctly on sub-carriers with different
frequencies, however in OFDM the signals are placed in such a way that the peak or
the center of one sub-carrier lands into the null of the neighboring sub-carrier as
shown in figure 2.3 [9]. The spacing is formed in an orthogonal manner and that’s
where this technique got its name (OFDM). The close spacing allows transmitting the
same amount of data (as in FDM case) using less bandwidth thus making this
technique spectral efficient.
15
Frequency
Figure 2.3: Orthogonal Frequency Division Multiplexing (OFDM)
Sub-carriers are closely spaced until overlap. [9]
2.8.2 OFDM Advantages and Drawbacks
Here highlighted few advantages and disadvantages of OFDM. Some of the
advantages of OFDM technique are:
¾
Spectral Efficiency:
OFDM
utilizes less
amount
of
bandwidth
as
compared to its predecessor FDM while transmitting same amount of data
and this really makes OFDM a spectral efficient technique.
¾
Cyclic Prefix: OFDM uses Cyclic Prefix to minimize the effects of Inter
Symbol Interference (ISI).
¾
Guard Interval: OFDM provides a better mechanism against multi-path
propagation by using Guard Interval (GI).
¾
Modulation: OFDM is a very diverse technique and thus provides a huge
variation when it comes to signal modulation, each sub-carrier can be
modulated using different modulation techniques such BPSK, QPSK or QAM
depending upon our requirements.
¾
Forward Error Correction: FEC provides a good mechanism for recovery
of signals that have suffered from fading thus making this technique a
good resistance against channel and path fading.
On the other hand OFDM presents certain drawbacks as well, these are:
¾
Peak–to-Average Power Reduction (PAPR): OFDM produces high
PAPR which in results garbles the transmitted and this ratio increases with
a presence of power amplifier at the transmitter and this further cause’s
increase in Channel interference as well as increase in Bit Error Rate.
¾
Frequency Offset Effect: OFDM technology is very sensitive to frequency
offset, so to over come these issues proper planning is required while
designing the receiver
16
2.9 Smart Antennas
2.9.1 What are Smart Antennas?
The increase in use of Wireless Broadband Systems (WBS) has put promoters of WBS
in a competitive race with their counter parts. It’s a well known fact that wireless
systems are way ahead with their counter parts when it comes to deployment and
ease of installation thus reaching places where one cannot even think of deploying a
wired solution for broadband communication. However wireless systems have been
unable to tackle bandwidth issues for the past many years and therefore remained
unable to address QoS parameters until now. In past recent years considerable
amount of research work has been conducted to improve the performance of the
system in terms of increasing the capacity and range. One such technology that is
proving to be very useful to cater these issues is “S
Smart Antenna Systems” (SAS)
[27] [33].
Smart Antenna System uses advanced signal processing techniques to construct the
model of the channel. Using the knowledge of the channel, SAS uses beamforming
techniques in order to steer or direct a radio beam towards desired users and null
steering towards the interferers [16]. It works by adjusting the angles and width of
the antenna radiation pattern.
SAS consist of set or radiating elements capable of sending and receiving signals in
such a way that radiated signals combine together to form a switch able and movable
beam towards the user. However it may be noted that the hardware of the smart
antenna does not make them “smart”, in fact it is the signal processing technique
that is used to focus the beam of the radiated signals in the desired direction. This
process of combining the signal and then focusing the signal in particular direction is
called Beamforming [16].
Two common beamforming approaches used in SAS are:
•
Switched Beam Antenna System
•
Adaptive Array Antenna System
17
2.9.2 Switched Beam Antenna System
In this technique a pre-determined set of beams is used; a beam is determined and
then directed towards a particular user on basis of appropriate phases of each
antenna. In switched beam systems, the 360 degrees interval are divided into equal
spaced beams. Then the system aligned the "closest beam to the users and if the
users move away from that beam and beam hand off must be preformed. Switched
beam system coverage pattern is shown in the figure 2.4 [16].
Figure 2.4: Switched beam System
2.9.3 Adaptive Array Antenna System
On the other hand Adaptive Array System acts in a different manner as compared to
switched beam Antenna system. It works by keep a constant track of the mobile user
by focusing a main beam towards the user and at the same time jamming the
interfering signals by forming nulls in direction towards them. A brief comparison of
these two approaches can be best observed from the figure 2.5 which shows
beamforming lobes and nulls. It can be seen that for the Adaptive Array the main
beam is towards users and nulls to interferer [16].
Figure 2.5 Switched Beam (Left) and Adaptive Array (Right)
A BS can serve multiple subscriber stations with higher throughput by using AAS. For
that space Division multiplex is used to separate (in space) multiple SSs that are
transmitting and receiving at the same time over the same sub-channel as shown in
18
figure 2.6. By using AAS, Interference can be severely reduced that is originated
from the other Subscriber Stations or the multipath signals from the same SS by
steering the nulls towards the desired interference [31].
An adaptive antenna system performs the following functions. First it calculates the
direction of arrival of all incoming signals including the multipath signal and the
interferers using the Direction of Arrival (DOA) algorithms with for example MUSIC
and ESPIRIT [27]. This is just two of many used algorithms. DOA information is then
fed into the weight updating algorithm to calculate the corresponding complex
weights. For that adaptive beamforming algorithm like Least Mean Square (LMS),
Recursive Least Squares (RLS) or Sample Matrix Inversion (SMI) can be used
[21,27].
Figure 2.6: WiMAX BS with multiple antennas and AAS [31]
The calculated weight is then multiplied by the signal from the antenna array and
required radiation pattern is formed. The block diagram of an antenna array system
is shown in figure 2.7.
19
Weights
Antenna 1
Antenna 2
Output
Antenna M
Weight
Estimation
Desired Signal
Direction
Figure 2.7: Block diagram of an Antenna Array System [21]
So a beam is steered in the direction of the desired signal and the user is tracked as
it moves while placing nulls at interfering signal directions by constantly updating the
complex weights by using any of the beamforming algorithms.
2.9.4 SAS Benefits and Drawbacks
In mobile communications antenna arrays when used in an appropriate configuration,
at the base station offer significant benefits in system performance by increasing
channel capacity / spectral efficiency [29]. Arrays can also help reduce multipath
fading and increasing range coverage.
Like all other modern technologies the Smart Antenna Systems have also certain
benefits and drawbacks that are highlighted below:
Benefits
SAS was introduced to deal with two most important factors that play a vital role in
wireless communications, these two factors are:
¾
Range: Primarily SAS were designed to improve the range of wireless
communication systems, one type of SAS is adaptive beamforming that
directs a radio beam towards the desired user and throws null towards
20
interferers which results in increase range. Experiments have shown that
SASs is capable of increasing the range of Base Station from 20 to 200
percent as compared to conventional sectored cells [16].
¾
Capacity: Bandwidth issues imposed on wireless communication systems &
technologies such WiFi are significantly addressed by SAS. Multiple antenna
arrays are used to provide enhanced bandwidth to number of users in a given
cell thus increasing the over all system capacity.
¾
Efficient use of Multipath: Before SAS multipath was considered a problem
in wireless communications but with the help of AAS multipath turned out to
be a benefit instead of a drawback as same multipath phenomenon can used
to increase the desired signal strength.
Drawbacks
Although SAS is a very captivating technology but still it has few drawback that are
highlighted below:
¾
Complex Calculations: AAS determines the position of the desired user with
the help of DOA and then dynamically throws a beam towards it, all this
requires set of complex calculations to be performed before the beam can
actually be diverted towards the user. This requires deployment of high level
digital signal processor (DSP) to be installed at the receiver (in our case it is
BS) and algorithms that are used for directing beams towards desired users in
AAS are very complex and complicated.
¾
High Cost: As mentioned earlier that the deployment of SAS requires high
level DSPs and incorporating such type processors with current systems can
be very costly.
21
Chapter – 3
System Model
In this thesis work a System Model is created and then implemented in MATLAB. The
basic of creating this model was to understand OFDM system in general and to
evaluate the performance of 802.16d OFDM PHY layer and to use the optional feature
of the standard that is Adaptive Antenna system at the receiver end. Channel coding
will not be considered.
In this chapter a system model will be introduced along with the detailed steps
performed during implementation i.e. creating and simulating the Transmitter,
Channel and Receiver Modules.
IEEE 802.16d OFDM PHY layer is configured into two parts: baseband and RF band.
In this system model only the baseband part of the OFDM PHY layer is considered.
The baseband PHY transmitter/Receiver consists of three major parts: Channel
Coding/Decoding, Modulation/Demodulation and OFDM transmitter/Receiver [2].
Explanation of each module is presented in the next section.
3.1 Transmitter Module
At the transmitter, the input random data is serial to parallel converted and then
mapped to either of the IEEE 802.16d modulation types (BPSK, QPSK, 16QAM,
64QAM). The obtained N samples are then passed through the IFFT block. As a result
an OFDM symbol is generated consisting of a block of N samples. So the frequency
domain signal consisting of data symbol, pilot symbols and virtual symbols are
transformed by IFFT into time domain signal and then transmitted over the radio
channel.
22
3.1.1 OFDM Symbol Description
The IEEE 802.16d PHY layer is based on OFDM modulation. OFDM wave form is
created by Inverse Fast Fourier transforming: this time duration is referred to as
useful symbol time
Tb [2]. A copy of the last of the useful symbol period T g , termed
CP, is used to collect multipath, while maintaining the orthogonality of the codes.
Figure 3.1 shows the OFDM symbol representation in the time-domain.
Figure 3.1: Time domain structure of OFDM Symbol [2]
Data is sent in the form of OFDM symbols. The basic structure of an OFDM symbol is
represented in frequency domain. Generally the OFDM symbol is made up from
carriers, the number of these carriers determine the FFT to be used. Three sub
carrier types are used [2]:
¾
Data subcarriers: For data transmission
¾
Pilot subcarriers: For various estimation purposes
¾
Null subcarriers: no transmission at all, for guard bands and DC carrier
The purpose of the guard bands is to enable the signals to naturally decay and create
the FFT ‘brick wall’ shaping. [2]. It can also be used for canceling the Inter-channel
interference. Figure 3.2 shows the OFDM symbol representation in frequency domain:
Figure 3.2: Frequency domain structure of OFDM Symbol [13]
23
OFDM symbol consists of number of parameters that can be found in the table 3.1.
Parameters
Value
Antenna Type
BW- This is the Nominal Channel
Bandwidth
Omnipresent
N used - Number of used subcarriers
200
1.75 MHz
n- Sampling Factor. Determines the sub
carrier spacing
G- Ratio of CP time to the useful time
8/7
¼
N FFT - Smallest power of two greater
than N used
256
Sampling Frequency, Fs
floor(n.BW/8000)*8000=2MHz
Sub carrier spacing, Δf
Fs/ N FFT = 7.8125e+003=7.8KHz
Useful Symbol Time,
1 / Δf = 1.2800e-004=12.8ms
Tb
CP Time, T g
G. T g = 3.2000e-005=320ms
OFDM symbol time,
Tb + T g = 1.6000e-004=16ms
Ts
Tb / N FFT = 5.0000e-07=5000ms
Sampling Time
Used Bandwidth
N used * Δf =1.5MHz
Data sub carriers
192
Left Virtual Guard
Right Virtual Guard
Pilot Number
28
27
8
Modulation
BPSK, QPSK, 16QAM, 64QAM
Channel Model
Burst Size, Nsymbols
SUI-3 Channel
3 OFDM symbols
Sub Frame length
3*16ms=48ms
TSubF
Total number of bits per OFDM symbol:
OFDM Sub frame for BPSK:
The Raw Transfer Rate:
192 bits
576 bits
576/48ms=0.0120 bits/s
Total number of bits per OFDM symbol:
OFDM Sub frame for QPSK:
The Raw Transfer Rate:
384 bits
1152 bits
1152/48ms=0.0240 bits/s
Total number of bits per OFDM symbol:
OFDM Sub frame for 16QAM:
The Raw Transfer Rate:
768 bits
2304 bits
2304/48ms=0.0480 bits/s
Total number of bits per OFDM symbol:
OFDM Sub frame for 64QAM:
The Raw Transfer Rate
1152 bits
3456 bits
3456/48ms=0.0720 bits/s
Table 3.1: OFDM Symbol Parameters
24
The transmitted signal voltage to the antenna, as a function of time during any OFDM
symbol can be written as [2]:
s(t ) = Re{e j 2πf ct
N used / 2
∑
ck e
j 2πkΔf ( t −Tg )
(3.1)
}
k = − N used / 2
k ≠0
wh er e t i s th e ti m e elapsed since the beginning of the OFDM symbol with
0 < t < Ts and ck is a complex number; the data to be transmitted on the subcarrier
whose frequency offset index is k during the subject OFDM symbol. It specifies a
point in a QAM constellation [2].
3.1.2 The Block Diagram
Figure 3.3 shows how the transmitted signal is generated and the functions of the
sub-modules are briefly described below.
Random Data
Generator
Modulator
(BPSK, QPSK, 16QAM,
64QAM)
IFFT
Cyclic Prefix
Insertion
Figure 3.3: Transmitter Module
a. Random Data Generation
The input data is generated in the form of random numbers i.e. series of ones and
zeros (110000111001). The length of the information bits depends upon the type of
the modulation scheme used to map the bits to symbols (BPSK, QPSK, 16QAM,
64QAM). The generated data is then passed to the Modulation sub-module for symbol
mapping.
b. Modulation
The generated data is then passed to the constellation mapper, where depending
upon its size the data was modulated using the following four different modulation
schemes: BPSK, Gray-mapped QPSK, 16 QAM and 64 QAM to form an OFDM
frequency-domain signal [2]. Modulation is done by dividing the incoming bits into
groups of i bits to represent a modulated signal. As a result there are
25
2 i points, and
the total number of points represents a constellation. The size of i for BPSK, QPSK,
16QAM and 64QAM are 1, 2, 4 and 16 respectively. Normalize the constellation by
multiplying the constellation point with the indicated factor c to achieve equal
average
is 1, 1 /
power.
The
value
of
c
for
BPSK,
QPSK,
16QAM
and
64QAM
2, 1 / 10 and 1 / 42 . Where b0 denotes the Least Significant Bit (LSB) for
each modulation [2]. The constellations are presented in the I-Q plane, where I and
Q denote the in-phase and quadrature component as shown in figure 3.4.
Q
b1b0
Q
b0
1
0
-1
1
1
0
3
00
1
I
-1
-3
-1
1
01
b1
10
-1
11
-3
10
11
Figure 3.4 (a): QPSK constellations
1
3
00
01
I
b3b2
Figure 3.4 (b): 16QAM constellations
Q
b2b1b0
011
7
010
5
000
3
001
1
-7
-5
-3
1
-1
101
-1
100
-3
110
-5
111
-7
111 110 100 101
3
5
7
001 000 010 011
I
b5b4b3
Figure 3.4 (c): 64 QAM constellations
c. Pilot Modulation
Pi l ot sy mb ol s al l oca t e sp e ci fi c su b ca r ri e r s i n all OF DM d at a sy mb ol s . To
constitute the symbol, the pilot subcarriers are inserted into each data burst and they
26
are modulated according to their carrier location within the OFDM symbol. For that,
pseudo
random
binary
sequence
(PRBS)
generator
is
used
sequence wk as shown in figure 3.5. PRBS generator polynomial is X
to
11
produce
a
+ X 9 + 1 . Both
downlink and uplink side uses a different string. The value of pilot modulation for
OFDM symbol k is derived from
wk [2].
MSB
Initialization DL:
Sequences UL:
LSB
1
1
1
0
1
1
1
0
1
1
1
0
1
1
1
0
1
1
1
0
1
1
1
2
3
4
5
6
7
8
9
10
11
Wk
Figure 3.5: PRBS for Pilot Modulation.
d. IFFT
After successful data Modulation Inverse Fast Fourier Transform was applied on the
modulated data to convert it from frequency domain into time domain. IFFT is simple
to use and it guarantees that the carriers signal ready to be sent towards the
receiver are orthogonal in nature.
The t-th time domain sample at the n-th subcarrier at the output of IFFT is given by
[23].
N −1
xt = ∑ X n exp{ j
n =0
2πtn
}
N
0 ≤ t ≤ N −1
Where N is the number of subcarriers and
(3 .2 )
X n is the data symbol on the n-th
subcarrier. As a result an OFDM symbol is generated. FFT is just a computationally
fast way to calculate the DFT. We can move back and forth between the time domain
and frequency domain without loosing information [17].
e. Cyclic Prefix Insertion
Cyclic Prefix (CP) was added to the data once the data was converted into time
domain and ready to be transmitted. The addition of CP to the data before it was
actually transmitted helped the data to cater the problems related to the multipath
propagation and provided a resistance against ISI. IEEE 802.16d allows the insertion
27
of Cyclic Prefix of various lengths such 1/4, 1/8, 1/16, and 1/32, here a CP of length
1/4 is added to the OFDM symbol before it was transmitted. The transmitted data is
then fed into the SUI-3 channel.
3.2 Channel Module
The basic aim of wide area networks using broadband wireless channels is to achieve
high data rates with reasonable bandwidth and power consumption. Maintaining high
coverage and quality of service standard was something that was considered
unachievable until the arrival of WiMAX which promises to deliver high data rates
with improved coverage.
One of the main problems faced by the wireless networks is deal with the
environmental challenges once the signal is on air from its way towards the receiver.
So in this section, the immense challenges presented by time-varying broadband
wireless channels are presented.
Whenever a deployment of a wireless communication system is considered, the first
thing that must be addressed properly is the design of a channel model. Once the
signal is sent from the transmitter towards the receiver it has to encounter various
environmental effects or conditions. These effects play a significant role in wireless
communication technology and this is where the design of the channel model comes
into play. In order to design an efficient wireless channel model following things must
be kept in mind [18]:
¾
Multipath delay spread
¾
Fading characteristics
¾
Path loss
¾
Doppler spread
¾
Co-channel interference
Whenever a channel model is designed all the above factors are considered and they
are natural occurring conditions so during channel simulation a statistical calculation
of these environmental parameters is possible.
28
a. Multipath delay spread
The channel impulse response of a wireless channel looks like a series of pulses,
because of the multipath reflections. The number of pulses that can be eminent is
very large, and depends on the time resolution of the communication or
measurement system [28]. So due to the non line of sight propagation nature of the
WiMAX OFDM, we have to address multipath delay spread in this channel model. To
analyze the effect of multipath propagation, the delay spread parameter is used. It
depends on terrain, distance, antenna directivity and other factors. Figure 3.6 shows
a LOS and multipath scenario. It shows that at different time, multiple reflections of
the same signal arrive at the receiver. This can result in an Inter symbol Interference
(ISI) causing noticeable degradation in signal quality [16].
Figure 3.6: LOS and Multipath Scenario.
b. Fading Characteristics
In multipath fading, the received signal experiences variation in its amplitude, phase
and angle of arrival in a multipath propagation environment. As a result they may
add either constructively or destructively leading to a complex envelope [18]. Small
scale fading has also been addressed in this channel model due to the fixed
deployment of transmit and receive antenna. If there is no line of sight signal
component and there are multiple reflective paths that are large in number then
small scale fading is called Rayleigh fading. When there is a line of sight component
along with the multiple reflective paths then small scale fading is described by a
Rician pdf, so in this channel model Rician distribution is considered. The key
29
component of this distribution is the k factor, which is the ratio of the direct
component power and the scatter component power [15].
c. P at h L o ss
When an electromagnetic wave propagates through a free space, there is a reduction
in the power density of the wave, which results in path loss or signal attenuation
[15]. There are certain factors which affect path loss such as terrain contours,
different environments, propagation medium, distance between the transmitter and
the receiver, the height and location of their antennas, etc. Path loss is a major
component that plays a vital role in analysis and design of the link budget. So it is
not considered in this model.
d. D o pp l er s pr e a d
Doppler spread is basically due to the movement of the communicating devices or
due to the relative motion of objects in the environment. As a result Doppler
frequency shift is bringing about. There is a difference between the Doppler spectrum
of fixed and mobile channel. In this case of fixed wireless channels, the Doppler PSD
of the scatter (variable) component is mainly distributed around f=0Hz [15].
e. Co- c h an n el I nt e rf e re nc e
One of the main obstacles that engineers have to deal with during wireless
communication scenario is co-channel interference. It occurs when a same frequency
from two different transmitters reaches the same receiver simultaneously, thus
creating problem for the receiver to determine that the signal actually came from
which user as shown in figure 3.7.
Earlier on broadcast antennas were used with lot of signals being scattered thus
resulting in wastage of signal strength and bandwidth so with the passage of time
research was made on the use of more focused antennas (sectorized antenna).
However problem with both these approaches were that signal intended for one user
present in a cell could cause interference for the other user present in same or
adjacent cell. Thus the use of sectorized antennas although increase the use of
bandwidth by resulting in increase in number of channels, however they were still not
effective in dealing with co-channel interference problem as the use sectorized
antenna promotes the co-channel interference.
30
Figure 3.7: Co-channel Interference Scenario [16]
To deal with this problem of co-channel interference smart antennas are used. Smart
antennas provide strong resistance against co-channel interference by throwing NULL
towards unwanted users and directing a beam towards the desired user, thus
resulting in increased bandwidth and signal strength [16].
In order to design and analyze wireless communication systems, channel models
should be developed that incorporate their variations in time, frequency and space.
Models are classified as either statistical or empirical. Statistical models are simpler
and useful for analysis and simulations as compared to empirical models that are
more complicated but usually represent a specific type of channel more accurately.
Empirical channel model called the Stanford University Interim (SUI) channel model
is considered for simulation [15]. To understand the basic performance of the
system, the ideal channel model called Additive Wide Gaussian Noise (AWGN) is
used.
3.2.1 Additive White Gaussian Noise (AWGN) Channel
This channel adds white Gaussian noise to the transmitted signal.
In this channel
model fading, Interference and dispersion are not considered. The mathematical
model for the received signal passed through an AWGN channel is shown in figure
3.8 and is represented by equation 3.3.
r (t ) = s (t ) + n(t ).
(3.3)
31
Transmitted
Signal
Received
Signal
Channel
s(t)
r(t)=s(t)+n(t)
+
Noise
n(t)
Figure 3.8: Received Signal passed through an AWGN channel
3.2.2 Stanford University Interim (SUI) Channel Models
This model can be used for simulations, design, and development and testing of
technologies suitable for fixed broadband wireless applications [15]. The parameters
for the model were selected based upon some statistical models.
This model is designed particularly for the cell size of 7km, Base Transceiver Station
(BTS) antenna height of 30m, receive antenna height of 6m, BTS antenna beamwidth
of 120
0
and receive antenna beamwidth of 360
0
for omni directional antennas or
30 0 for directional antennas. Considering the above mentioned scenario, six specific
SUI channel models are defined. The SUI-3 channel model is considered here and the
general structure for the SUI-3 channel model is shown in figure 3.8 [15].
Tx
Input Mixing
Matrix
Tapped Delay
line Matrix (TDL)
Output Mixing
Matrix
Rx
Figure 3.9: SUI channel model structure
This is a general case for Multiple Input Multiple Output (MIMO) channels but also
includes other configurations like Single Input Single Output (SISO) and Single Input
Multiple Output (SIMO) as subsets. The SUI-3 channel structure is the same for the
primary and interfering signals [15].
32
a. I np ut M ix i n g M at r ix
If multiple transmitting antennas are used then this part models the correlation
between the input signals. This is considered for the case of MIMO systems. Detailed
description of antenna correlation is presented in section 3.2.2.
b. T a pp e d D el a y L i ne M at rix
Modeling of multipath fading of the channel is done by this part. The multipath fading
is modeled as a tapped delay line with 3 taps with non-uniform delays. The gain
associated with each tap is characterized by a distribution (Ricean with a K-factor>0,
or Rayleigh with K-factor=0) and the maximum Doppler frequency.
c . O ut p ut M ix i n g M a t r ix
If multiple receiving antennas are used then this part models the correlation between
output signals. This is considered for the case of SIMO and MIMO systems.
The SUI-3 channel parameters used in this model are shown in table 3.2 [15].
SUI-3 Channel
Tap1
Tap2
0
0.4
0
-5
1
0
0.4
0.3
Delay
Power (omni ant.)
90% K Factor (omni ant.)
Doppler
Tap3
0.9
-10
0
0.5
Units
μs
dB
Hz
ρ ENV = 0.4, Normalization Factor: Fomni = -1.5113dB,
Terrain type: B, Omni antenna: τ RMS = 0.264 μs , K-Factor: Low,
Antenna Correlation:
Over all K: K=0.5(90%), Low delay spread, Doppler Effect Low.
Table 3.2: SUI-3 Channel Parameters
3.2.3 SUI-3 Channel model Simulation
The channel can be setup to simulate channel coefficients.
a. Po w e r D i st ri b ut io n
To generate channel coefficients with specified distribution and spectral power
distribution, the method of filtered noise is used. For each tap a set of complex zeromean Gaussian distributed numbers is generated with a variance of 0.5 for the real
and imaginary part, so the total average power of this distribution is one [15]. This
way a normalized Rayleigh distribution (equivalent to Rice with k=0) is achieved for
33
the magnitude of the complex coefficients. In case of Rician distribution (K>0
implied), a constant path component m has to be added to the Rayleigh set of
coefficients. The K factor implies the ratio of the power between the constant part
and variable part. The distribution of power is shown below. The total power of each
tap is given as [15]:
P = m +σ 2,
2
(3.3)
where “m” is the complex constant and
σ 2 the variance of complex Gaussian set.
The ratio of powers is:
K = m /σ 2 .
2
(3.4)
In equation 3.3 and 3.4, the power of the complex Gaussian is given as
σ 2 = P.
1
,
K +1
(3.4a)
and the power of the constant part is given as
m = P.
2
K
.
K +1
(3.4b)
b. D o pp l er Sp ec tr um
The power spectral density (PSD) functions for these scatter component channel
coefficients is given by:
⎧⎪1 − 1.72 f 0 2 + 0.785 f 0 4
S( f ) = ⎨
0
⎪⎩
where
f 0 ≤ 1⎫⎪
⎬
f 0 > 1⎪⎭
(3.5)
f0 = f / fm .
To get a set of channel coefficients with this PSD function, correlate the original
coefficients with filter which amplitude frequency response derived from (3.5) as
H ( f ) = S( f )
.
(3.6)
A non recursive filter and frequency-domain over-lap method has been used. Since
there are no frequency components higher than
with a minimum sampling frequency of
f m , the channel can be represented
2 f m according to the Nyquist theorem. It is
considered that coefficients are sampled at a frequency of
filter is also normalized to 1 [15].
34
2 f m and the power of the
Figure 3.9 shows a signal magnitude plot of channel coefficients over time.
Figure 3.10: Magnitude plot
c. An te n n a C o rr e l a ti on
If there are multiple transmit or receive antennas, then antenna correlation must be
considered in the SUI channel model. Antenna correlation is the envelope correlation
coefficient between the signals received at two antenna elements. The random parts
of the channel are of interest because the envelope correlation coefficient is
independent of the mean [15].
A tapped delay line channel model is considered for frequency selective (delayspread) propagation.
L
g (t ,τ ) = ∑ g l (t )δ (τ − τ l )
,
(3.7)
l =1
where L is the number of taps,
g l (t ) are the time varying coefficients and τ l are the
tap delays that corresponds to the specific multipath component. Here a case is
considered where there are two receive antennas which can be then further
generalized for more antennas. So the correlation coefficient between two receive
signals
r1 (t ) and r2 (t ) is calculated, which are the results of a normalized, random,
35
white transmitted signal s(t) propagating through two channels with channel impulse
responses
g1 (t ) and g 2 (t ) :
3
g i (t,τ ) = ∑ g il (t )δ (τ − τ l ), i ∈ [1..2]
(3.8)
l =1
Number of taps is L=3, tap delays τ l are fixed. It is assumed that equivalent taps in
both channels have equal power:
σ 12l = σ 22l = σ l2 , l ∈ [1..3] .
(3.9)
Taps having different delays are uncorrelated with in a channel as well as between
channels:
E{g ik (t ) g *jl (t )} = 0, ∀k ≠ l ,
where
k , l ∈ [1..3]; i, j[1..2].
The antenna correlation coefficient becomes:
ρ1σ 12 + ρ 2σ 22 + ρ 3σ 32
,
σ 12 + σ 22 + σ 32
ρ env =
where
ρl
(3.10)
are the correlation coefficients between each of the three pairs of taps
g1l (t ) and g 2l (t ) :
ρl =
E{g1l (t ) g 2*l }
σ 12l σ 22l
, l ∈ [1..3].
(3.11)
The antenna correlation can be related to the individual tap correlation, where
σ l2
are the individual tap gains. All tap correlations set equal to the antenna correlation
for the simulation of SUI channel [15].
Following transformation can be used to generate a sequence of random state
vectors with specified first order statistics (mean vector
μ and correlation matrix R)
[15]:
~
1
V = R 2V + μ ,
(3.12)
where V is a vector of independent sequences of circularly symmetric complex
Gaussian distributed random numbers with zero mean and variance, the correlation
matrix R is:
⎡1
~~H
R = E {V V } = ⎢⎢ r12*
⎢⎣ M
r12
1
M
L⎤
H
1
1
1
L ⎥⎥ = R 2 R 2 = R 2 R 2
O ⎥⎦
36
(3.13)
In this model SISO and SIMO systems are considered. In SIMO the implementation
of antenna correlation by 6 correlated channels and 9 correlated channels is carried
out. So independent but equally distributed channel coefficients were created and
then correlated the random signals of equivalent taps in these channels by using
equation (3.12) and (3.13). The correlation coefficient
r12 is set to the specified
antenna correlation.
3.3 Receiver Module
As SUI-3 channel is a multipath channel with L discrete paths consisting of unfaded
LOS path and two Rayleigh components. As a result transmitted OFDM signal suffers
from multipath effects and other channel effects. At the receiver an array of antennas
is deployed. The different sub modules at the receiver are now explained.
3.3.1 Antenna Array Model
A general case of linear antenna array with uniformly spaced sensors is considered as
shown in figure 3.10. It is assumed that the entire signals incident on the antenna
array is composed of plane waves and the transmitted signal is in the far field of the
antenna array [21] [35]. Let M be the number of antenna elements. Usually an
array has a reference element, here the left most element is supposed to be
considered as the reference element. So the plane wave first reaches the reference
element and then it propagates all the way to the
M th antenna element. There are L
incoming signals due to L paths. In our case there are 3 incoming signals one is the
LOS signal and other two reflected signals. The Direction of Arrival (DOA) algorithm
is not used here to calculate the incoming direction, infact the incoming direction is
supposed for simplicity. When the signals travel across the array they suffer a phase
shift. The phase shift between the signal received at the reference element and the
same signal received at element m is given by [35]
Δα m = −kd (m − 1) cos φ
where k
(3.15)
= 2π / λ is the propagation constant in free space and d = Δx = λ / 2 is the
element spacing aligned along the x-axis.
37
Y
− (m − 1)Δx cosφ
Incident
Angle
.....
Reference
Element
1
2
w
w
1
X
M
3
x
w
2
w
3
M
Array
Output
Figure 3.11: Antenna Array Model
Please note that Δα 1
= 0 . Lets now define the incoming signal at the array element
1(reference element) due to the
lth path by
s l ,t = x l , t e j 2 Π f 0 t ,
(3.16)
Where x l , t is the modulating function of the l path and
th
f 0 the frequency of the
carrier signal. The incoming signal at element m will be in that case
rm , t = x l , t e
j ( 2 Π f0t + Δα m )
+ n m ,t
= s l , t a m (φ ) + n m , t
(3.17)
where
a m (φ l ) = e jΔ α m = e − jkd ( m −1) cos φ l
38
(3.18)
and n m ,t is the random noise component on the m
th
background noise and electronic noise generated in the m
element, which includes
th
be temporarily white with zero mean and variance equal to
channel. It is assumed to
σ n2 .
Steering Vector describes the phases of the signal received at each antenna element
as compared to the phase of the signal at reference element [36]. The steering
vector can be represented as
⎛1
⎞
⎜
⎟
⎜ a 2 (φl ) ⎟
⎜L
⎟
⎟
a(φl ) = ⎜
⎜ a m (φl ) ⎟
⎜
⎟
⎜L
⎟
⎜ a (φ ) ⎟
⎝ M l ⎠
(3.19)
Now considering all the paths simultaneously, the signal at the
mth element will be
L
rm ,t = ∑ xl ,t e j ( 2Πf 0t + Δα m ) + n m ,t
l =1
L
= ∑ sl ,t a m (φ l ) + nm ,t ,
(3.20)
l =1
where L is the number of incoming signals,
φl is
th
the angle of arrival of the l path as
th
shown in figure 3.10, s l ,t is the transmitted signal for the l path and n m ,t denotes the
M x1 vector of the noise at the array elements.
Array factor is used to calculate the radiation pattern. Array factor can be
represented with the following equation
AF (φ ) =
M
∑w
m =1
where
φ
m
e jkd ( m −1) cos φ ,
(3.21)
is the look direction, M the number of elements in the array,
wm the
weights, k the wave number, d the inter-element spacing. The Normalized Array
factor is defined like this
NAF (φ ) =
AF (φ )
max( AF (φ ))
.
(3.22)
39
Equation (3.20) is then passed through a beamformer which is located before the FFT
stage and the channel effects will be eliminated before passing the signal to FFT and
then demodulation steps as shown in figure 3.11.
3.3.2 Pre-FFT Beam-forming
Beam-forming is used to separate the desired signal from the interfering signals
given that they have same frequencies but different spatial locations. Interference
signals can be other user signals or can be the signals from multipath environment
[21]. Pre-FFT beam-forming also called time-domain beamforming setup at receiver
is illustrated in figure 3.11. Here beamforming is applied before the FFT operation.
Adaptive beamforming involves two steps: First the weight calculation and then
beamforming by applying the weights to the received signal. Consider a Narrowband
beam-former, where signals from each element are multiplied by a complex weight
and summed to form the array output [27].
M
y t = ∑ wm* rm ,t = w H rt
(3.23)
m =1
where subscript H denote complex conjugate transposition of a vector or matrix and
w is called the array weight vector.
w = [ w1 , w2 , LL , wM ]T
rr = [r1 (t ), r2 (t ),LL, rM (t )]T
where subscript T denote the transpose of a vector or matrix.
The output of the array system becomes
y t = w H rt
(3.24)
Note that to obtain the array output; multiply the signals induced on all elements
with the corresponding weights. In vector notation this operation is carried out by
taking the inner product of the weight vector with the signal vector as given by
equation (3.24).
3.3.3 Adaptive beamforming Algorithm used
There are many types of Adaptive beamforming algorithms. The minimum mean
square error (MMSE) criterion algorithms such as the least mean squares (LMS) and
recursive least square algorithm (RLS) are often used for updating weights in
adaptive beamforming [21, 27]. Due to complex multiplications per update for the
40
RLS algorithm, the LMS algorithm is generally employed [22]. LMS which is iterative
makes use of past information to minimize the computations required at each update
cycle is used in this simulator [23].
The antenna array and LMS beamformer configurations are shown in figure 3.9. The
LMS algorithm is based on the steepest-decent method which recursively computes
and updates the sensor array weight vectors [37]. The output of the array is
compared with the reference signal generated at the receiver. Here the reference
signal is assumed to be identical to the incoming signal and have similar statistical
properties as the transmitted signal.
The error signal is put into the weight updating algorithm. The gradient approach
which provides an iterative update solution for the MMSE criteria is given by [23]:
wm ,i +1 = wm ,i −
1
μ∇j ( wm ,i ) ,
2
(3.25)
The convergence characteristic of the algorithm depends on the parameter μ . ∇ j ( w)
is the gradient of function j ( w) which is given by:
2
j ( wm ) = E[ wmH ri − d m ,i ]
(3.26)
The function j (w) is the cost function. By solving the gradient of the cost function in
above equation and we can get the approximated solution for the instantaneous
squared error and Sub. So the Least Mean Square algorithm is found by [23]:
wm ,i +1 = wm,i + μri ei*, m ,
where
(3.27)
ei ,m = wmH,i ri − d m,i is the error between the array output and reference signal.
3.3.4 The Block Diagram
After applying the adaptive beamforming algorithm the desired LOS signal was
obtained, thus filtering it out from unwanted (null and interfering signals). The
desired output signal from the beamformer as shown in the figure 3.11 i.e. Y (t) is
then processed in the following way so that the original signal can be extracted.
41
Y(t)
r1(t)
w
1
Cyclic Prefix
Removal
r2(t)
FFT
De-Modulator
(BPSK,QPSK,
16QAM,64QAM)
w2
rM(t)
OutPut BER
wM
Error Signal
e(t)
Control For
Weight Estimation
-
+
Reference Signal
d(t)
Figure 3.12: Receiver Module
The process starts with the removal of the cyclic prefix that was initially added to the
transmitted signal as earlier on explained in the transmitter module. After cyclic
prefix removal, the data was converted back into frequency domain from the time
domain using the FFT. Once the data conversion is completed the data is passed to
the De-Modulator where the data is De-modulated according to modulation schemes
applied on the data during the transmission. The De-modulation of the data marks
the end of the receiver module where the data obtained from De-modulator was
compared to original data in form Bit Error Rate (BER).
42
3.4 The Complete Simulation Block Diagram
Random Data
Generator
Modulator
(BPSK, QPSK,
16QAM, 64QAM)
Sub
Carrier
Allocation
Transmitter
IFFT
S/P
P/S
Cyclic
Prefix
Insertion
Pilot Symbol
Generator
Wireless Channel
SUI-3
AWGN+Multipath
Channel
DeModulator
Cyclic
Prefix
Removal
S/P
FFT
P/S
Sub
Carrier
Demapping
Pilot
Signal
LMS Weights
Control
Receiver
BER
Calculation
Figure 3.13: Simulation Block Diagram
43
Chapter – 4
Simulation Results
In this chapter the simulation results along with the underlying assumptions are
presented. The basic aim of this thesis work is to study the physical layer of WiMAX
802.16d and the corresponding results. First the performance of the system is
investigated for SISO case by using AWGN channel and then SUI-3 channel [15]
[19]. The worst performance of the SUI-3 channel is due to multipath effect, delay
spread and Doppler effects. Although the impact of the delay spread and the Doppler
effect is low so the major degradation in the performance is due to the multipath
effects. There are various methods to reduce the multipath effect. However in this
model it is done by implementing AAS. AAS has the feature that requires only
multiple antennas at the BS and thus putting whole burden on the BS. As AAS is
known
to
reduce
intercell
interference
and
multipath
fading
by
providing
beamforming. So multiple antennas are installed at the receiver and performance is
investigated in the presence of six and nine antennas.
4.1 Simulation Environment
The simulations implemented in this thesis are all done in MATLAB. The whole system
was tested using Monte Carlo based simulations [38]. The Monte Carlo simulation is
used to estimate the BER which the system can achieve. In this model the simulation
of the system is repeated and the number of transmitted bits and bit errors are
calculated for each simulation. In the end BER rate is estimated as the ratio of the
total number of observed errors and the total number of transmitted bits [38]. Let us
consider the case for SISO system using QPSK as a modulation scheme and AWGN
as a channel. The total number of transmitted bits for 3 OFDM symbols is 1152 bits.
If the simulation is repeated 500 times then the total number of transmitted bits is
576000 and the total numbers of bits that are in error are 62768. In the end BER
rate is estimated from the above calculations. Same method is adopted for each
simulation considered in this system model. The parameters that can be set are:
44
number of simulated OFDM symbols, modulation scheme, channel type, number of
antennas at receiver and range of Eb/No (Bit Energy-to-Noise Density) values.
4.2 Uncoded BER Plots
Uncoded BER vs. Eb/No plot for the different modulation schemes have been
presented. Uncoded BER considered here because channel coding has not been
considered. Uncoded streams are available in output and BER rate curves as function
of Eb/No are plotted. First using only AWGN channel and then with SUI-3 channel. It
has been concluded that lower modulation schemes provides better performance with
less Eb/No as shown in table 4.1.
All blocks have been tested; in particular our simulation campaign consisted of the
following steps:
1. Single blocks
2. Entire simulator with/without noise(AWGN channel)
3. Entire simulator with SUI-3 channel and AWGN
4. Entire simulator with Adaptive Antenna System
4.2.1 SISO (Single Input Single Output) case
In this section, the simulation results when using a single antenna both at the
transmitter and the receiver have been shown.
a. Performance in AWGN Channel
Performance of the system model tested using different modulation schemes i.e.
BPSK, QPSK, 16QAM and 64QAM with an AWGN channel which is considered as an
ideal communication channel. Figures 4.1-4.4 shows the simulation results for BPSK,
QPSK, 16QAM and 64QAM. In all of the figures a comparison is shown between the
simulated BER for this system model and theoretical BER for the serial systems. It
has been concluded from the simulation results that theoretical BER and simulated
BER for AWGN are in good accordance with each other.
45
Figure 4.1: BPSK under AWGN
Figure 4.2: QPSK under AWGN
46
Figure 4.3: 16QAM under AWGN
Figure 4.4: 64QAM under AWGN
47
b. Performance in SUI-3 Channel
Now the performance of the system model tested using different modulation schemes
i.e. BPSK, QPSK, 16QAM and 64QAM with an SUI-3 channel. When the incoming
signal is passed through the SUI-3 channel, then the performance of the system
degrades due to fading effect and Doppler spread. According to the characteristics of
the SUI-3 channel, Rician distribution is used here. So the channel has three paths
consisting of unfaded LOS path and two Rayleigh components. The required signal is
corrupted by the previous multipath model and AWGN. Figure 4.5-4.8 shows the
simulation results for BPSK, QPSK, 16QAM and 64 QAM. Table 4.1 shows the
comparison between different modulation schemes. Eb/No required to attain BER
level at 10
−0.34
Modulation
are given in table 4.1.
BPSK
Channel
SUI-3
QPSK
16QAM
Eb/No (dB) at BER level 10
7 dB
9 dB
−0.34
16 dB
Table 4.1: Eb/No required to attain BER level at 10
Figure 4.5: BPSK under SUI-3
48
64QAM
19 dB
− 0 . 34
Figure 4.6: QPSK under SUI-3
Figure 4.7: 16QAM under SUI-3
49
Figure 4.8: 64QAM under SUI-3
4.2.2 SIMO (Single Input Multiple Output) case
In this section the simulation results are shown using a single antenna at the
transmitter and multiple antennas at the receiver. Again the whole system was
tested using Monte Carlo based simulations. The
simulations are performed for
implementing the AAS using Pre-FFT beamformer in WiMAX OFDM system: QPSK
modulation is used in the system and half-wavelength spacing is employed and all
the results are relative to smart antenna composed by K=9 and K=6 sensors.
Different aspects of the complete system model were investigated such as angle of
arrival of the incoming signals and number of array elements. The value of
μ used is
0.001. When applying the channel conditions (fading and AWGN) to adaptive array
fixed WiMAX system, the receiver separated the desired LOS signal from the
multipath signals, the array factor or radiations pattern for the beamformer is
illustrated in figure and nulls are formed at the interfering rays. The error function for
each scenario is also shown in the figures. The direct ray is properly recovered while
the reflected signal is strongly attenuated. Also the bit error rate (BER) performances
are evaluated. So it can be seen that the performance of the system improves if we
increase the number of antennas at the receiver as shown in table 4.2.
50
a. Performance in SUI-3 Channel (6-antenna elements)
°
°
°
The arrival directions are 60 for the direct ray, 90 and 120 for the reflected rays.
Figure 4.9 shows the BER performance of the systems having 6 antenna elements at
the receiver along with the SISO system performance. It can be concluded that in
SIMO system, BER performance of the systems improves as compared to that of
SISO system. Figures 4.10, 4.11, 4.13, 4.14, 4.16 and 4.17 shows the linear and
polar plot for the Array Factor of 6 element antenna array in the presence of LOS
signal and reflected rays. These curves are obtained for Eb/No=20dB. It can be seen
that the main beam is directed towards the LOS signal and nulls are directed towards
the reflected signals. Figures 4.12, 4.15 and 4.18 show the LMS absolute error value
for each scenario, which is the difference between the array output and the reference
signal. It can be seen that the LMS algorithm begins to converge and the absolute
error decreases.
Figure 4.9: Uncoded BER for receiver having 6 antennas and employing QPSK modulation
51
Figure 4.10: Array Factor of a 6 element antenna array in presence of LOS signal and reflected rays
Linear Plot
Figure 4.11: Array Factor of a 6 element antenna array in presence of LOS signal and reflected rays
Polar Plot
52
Figure 4.12: LMS Absolute Error vs. Time
The robustness of the system is evaluated by changing the angles of the incoming
signals. Now the arrival directions are 90
°
°
for the direct ray, 60 and 120
°
for the
reflected rays. It can be concluded from figures 4.13 and 4.16 that the system is
robust against the direction of arrivals.
53
Figure 4.13: Array Factor of a 6 element antenna array in presence of LOS signal and reflected rays
Liner Plot
Figure 4.14: Array Factor of a 6 element antenna array in presence of LOS signal and reflected rays
Polar Plot
54
Figure: 4.15 LMS Absolute Error vs. Time
Again by changing the angles of the incoming signals, the arrival directions are 120
120 ° for the direct ray, 60 ° and 80 ° for the reflected rays.
Figure 4.16: Array Factor of a 6 element antenna array in presence of LOS signal and reflected rays
55
0
Figure 4.17: Array Factor of a 6 element antenna array in presence of LOS signal and reflected rays
Polar Plot
Figure 4.18: LMS Absolute Error vs. Time
56
b. Performance in SUI-3 Channel (9-antenna elements)
The performance of the system is now investigated by using 9 antenna elements at
the receiver. The arrival directions are 60
°
for the direct ray, 90
°
and 120
°
for the
reflected rays. Figure 4.19 shows the BER performance of the systems having 9
antenna elements at the receiver along with the 6 antenna element system
performance. It can be concluded that in this scenario, BER performance of the
systems improves as compared to that of the 6 antenna elements. Figure 4.20 and
4.21 shows the linear and polar plot for the Array Factor of 9 element antenna array
in the presence of LOS signal and reflected rays. It can be seen that the main beam
is directed towards the LOS signal and nulls are directed towards the reflected
signals. Figure 4.22 shows the error signal for this scenario.
Figure 4.19: Uncoded BER for receiver having 9 antennas and employing QPSK modulation
57
Figure 4.20: Array Factor of a 9 element antenna array in presence of LOS signal and reflected rays
Linear Plot
Figure 4.21: Array Factor of a 9 element antenna array in presence of LOS signal and reflected rays
Polar Plot
58
Figure 4.22: LMS Absolute Error vs. Time
Table 4.2 shows the comparison between all of the above scenarios. BER level
required to attain Eb/No=15dB are given in table 4.2.
No. Of Antenna
Elements at
Receiver
BER level at
Eb/No=15dB
1 (SISO)
6 (SIMO)
9 (SIMO)
10 −0.37
10 −2.4
10 −3.5
Table 4.2: BER level required to attain Eb/No=15dB
It can be seen from the results that BER curve is improved after using an AAS at the
receiver and the desired signal at the receiver is detected with low bit rate. The BER
with 9 antenna elements is lower than that of 6 antenna elements and the gap
between the two curves increase as Eb/No increases [23]. The BER curve further
improves if the number of antennas is increased. The array factor shows that the
beam is steered towards the LOS signal and nulls towards the interferers. As a result
receiver separated the LOS signal from the multipath signals. In the end most of the
channel effects and errors are reduced by using AAS in Fixed WiMAX OFDM system.
59
Chapter – 5
Conclusions and Future Work
5.1 Conclusions
This thesis concentrates on WiMAX PHY layer, which can be summarized in following
steps:
¾
Study of the WiMAX physical layer, features and the techniques used in the
technology
¾
Study of the SUI-3 channel and its implementation in MATLAB
¾
Study of the OFDM technology and its implementation according to the IEEE
802.16d standard, however channel coding module was not considered
¾
Study of Smart Antenna System including DOA algorithms and adaptive beam
forming algorithms. In the end selected the best suitable algorithm and then
its implementation in MATLAB
As it is a well known fact that fading is one of the major limiting factors in wireless
communications. So the problem which is discussed in this thesis is multipath fading
in fixed WiMAX scenario. According to the characteristics of the SUI-3 channel, Rician
distribution is used here. The channel has three paths consisting of unfaded LOS path
and two Rayleigh components. The required signal is corrupted by the previous
multipath model and AWGN. To reduce the effect caused by fading, AAS is
implemented
at
the receiver module by
using LMS
algorithm and
pre-FFT
beamforming. The whole system was tested with and with out AAS using different
modulation schemes. However with AAS only QPSK modulation scheme was
considered.
Finally performance in terms of Uncoded BER has been driven. It can be concluded
from the simulation results that, if there is an AAS installed at the receiver then
performance of the system drastically increases.
60
5.2 Future Work
In the foreground of above mentioned context a lot of related work can be done in
future for the optimization of wireless communications systems. The future work can
be summarized in following points:
¾
This thesis primarily focuses on the physical layer of Fixed WiMAX; on basis of
all the material described in this thesis one can also elaborate this work to
Mobile WiMAX and explore its various features as well
¾
This thesis still needs some improvements or some of the features which were
not considered and need to be implemented. Channel coding can be used to
further improve the performance of the system. Other channel estimation
methods can be used to obtain an interpretation of the channel state to
overcome the effects of the channel using an equalizer
¾
While using AAS, linear array has been considered along with LMS algorithm
and pre-FFT beamformer also called time-domain beamformer. So keeping all
that in mind circular array or planar array can also be used. Different adaptive
beamforming algorithms can also be used for example RLS and SMI [21] [27].
Post-FFT beamforming can be used in place of Pre-FFT beamforming, which of
course is a bit more complicated and requires more computation. This thesis
work explained how to reduce the effect of fading in single user environment.
However there could be a situation where there is more than one user and
then one has to reduce the fading effect caused by the different users.
Incoming directions are supposed in the system model, DOA algorithms can
be implemented to calculate the incoming directions and then steering of the
beam towards the desired user in fully adaptive and dynamic manner
¾
In this thesis perfect time and frequency synchronization is assumed.
Different frequency offsets and timing error compensation techniques can also
be discussed and their comparison can provide a way for advanced future
developments in this field
¾
In this thesis Smart Antenna System is considered and it’s a well known fact
that SAS encompasses various amazing features and is used more and more
in today’s environment to overcome coverage and bandwidth related issues.
So this model can be elaborated to study different diversity techniques like
Space time Coding (STC), Antenna Switching and Maximum Ratio combining
[32] [39]. Also Multiple Input Multiple Output (MIMO) systems could have
been deployed, which can further enhance the performance of the system
[32] [39].
61
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64
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