Adaptive of OFDM

Adaptive OFDM

1

CHAPTER 1

PREAMBLE

1.1 Introduction

The Wireless communications industry is in the midst of veritable explosion in Wireless

technologies. Once exclusively military, satellite and cellular technologies are now commercially

driven by ever more demanding consumers, who are ready for seamless communication from

their home to their car, to their office, or even for outdoor activities. With this increased demand

comes a growing need to transmit information wirelessly, quickly, and accurately. To address

this need, communications engineer have combined technologies suitable for high rate

transmission with forward error correction techniques and Adaptive Modulation methods [1].

The latter are particularly important as wireless communications channels are far more hostile as

opposed to wire alternatives, and the need for mobility proves especially challenging for reliable

communications. For the most part, Orthogonal Frequency Division Multiplexing (OFDM) is the

standard being used throughout the world to achieve the high data rates necessary for data

intensive applications that must now become routine[2]. Orthogonal Frequency Division

Multiplexing (OFDM) is a Multi-Carrier Modulation technique in which a single high rate data-

stream is divided into multiple low rate data-streams and is modulated using sub-carriers which

are orthogonal to each other. Some of the main advantages of OFDM are its multi-path delay

spread tolerance and efficient spectral usage by allowing overlapping in the frequency domain.

Also one other significant advantage is that the modulation and demodulation can be done using

IFFT and FFT operations, which are computationally efficient. Adaptation of Digital modulation

can be done as simple as multiplexing methods or using neural network. This chapter gives a

brief introduction on the motivation of this report work and the objectives on the work as well.

At end, detail project are explained.

Orthogonal Frequency Division Multiplexing (OFDM) data streams the orthogonally between

subcarriers can be maintained, even though the signal passes through a time-dispersive channel

by cyclically extending the OFDM symbols into guard interval. The main advantages of OFDM

are its multipath delay spread tolerance and efficient spectral usage by allowing overlapping in

Adaptive OFDM

2

the frequency domain. Another significant advantage is that the modulation and demodulation

can be done using inverse Fast Fourier Transformation (IFFT) and Fast Fourier Transformation

(FFT) operations, which are computationally efficient. In an OFDM transmission system, each

subcarrier is attenuated individually under the frequency-selective and fast fading channel. The

channel performance may be highly Fluctuating across the subcarriers and varies from symbol to

symbol. If the same fixed transmission scheme is used for all OFDM subcarriers, the error

probability is dominated by the OFDM subcarriers with highest attenuation resulting in a poor

performance. Therefore, in case of frequency selective fading the error probability decreases

very slowly with increasing average signal-to-noise ratio (SNR).This problem can be mitigated if

different modulation schemes are employed for the individual OFDM subcarriers. Unlike

adaptive serial systems, which employ the same set of parameters for all data symbols in a

transmission frame, adaptive OFDM schemes have to be adapted to the SNR of the individual

subcarriers. This will substantially improve the performance and data throughput of an OFDM

system. For example if the subcarriers that will exhibit high bit error probabilities in the OFDM

symbol to be transmitted can be identified and excluded from data transmission, the overall BER

can be improved in exchange for a slight loss of system throughput. However the potential loss

of throughput due to seclusion of faded subcarriers can be mitigated by employing higher order

modulation modes on the subcarriers exhibiting high SNR values. More detailed description of

OFDM will be given in chapter no 3.

In adaptive OFDM many adaptive transmission techniques have been presented in the literature.

The combination of adaptive modulation with OFDM was proposed as early as 1989 by Kalet

which was further developed by Chow and Czylwik .Specifically the results obtained by Czylwik

showed that the required SNR for the BER target 10E-3 can be reduced by 5dB to 15dB

compared to fixed OFDM depending on the scenario of radio propagation. The performance of

linear block coded modulation is investigated. Three different modulation mode allocation

algorithms were discussed and compared. Further studies on the application of interleave and

OSTBC modulation and coding is conducted. (Kwang et al. 2009) proposed a multi-user

multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)

system with adaptive modulation and coding to improve system capacity with maintaining good

error performance. The results of computer simulation show the improvement of system capacity

Adaptive OFDM

3

in Rayleigh fading channel. (Li Yanxinet al.2007) presented a novel method for demodulating

the QAM signals basing on adaptive filtering. The commonly used least mean square (LMS)

error adaptive filtering algorithm is employed for studying the demodulating procedure and the

performance of the novel adaptive QAM demodulation. The novel adaptive QAM demodulation

does not need the adaptive filter completing convergence. Therefore, the sampling rate and

processing speed are decelerated. Also, it is indicated that the demodulation method has many

advantages over conventional ones, such as the powerful anti-noise ability, the small transfer

delay, and the convenient implementation with DSP technology. (Kiyoshi Hamaguchi et al.)

proposed an adaptive modulation system for land mobile communications that can select one of

quadrature amplitude modulation levels as a suitable modulation for propagation conditions is

described. The main characteristics of the system are a mode in which information cannot be

transmitted under adverse propagation conditions and a buffer memory for maintaining the data

transmission rate. In the paper they confirmed that the basic performances of the adaptive

modulation system using the equipmentthey developed and they found the measured

performance was consistent with computer simulation results. Further in this work it was also

confirmed that the adaptive modulation system provided a noticeable improvement in spectral

efficiency and transmission quality.

K. Seshadri Sastry discussed an OFDM-CDMA system with adaptive modulation schemes for

future generation wireless networks are discussed [3]. Results presented there show that adaptive

systems can perform better than fixed modulation based systems both in terms of BER and

spectral efficiency. In this report Adaptive modulation is performed by multiplexing methods and combination of

OFDM and Adaptive modulation improves the BER and throughput. This work is conducted in

MATLAB version R2012a using Simulink.

1.2 Report outline

This report presents the simulation of coded AOFDM system and analyzes the performance of

this system under noisy environment is carried out.Next chapter discusses definition of WiMax,

wireless MAN technologies, and wireless MAN standards (IEEE 802.16, IEEE 802.16a, and

IEEE 802.16b, IEEE 802.16g) in detail. Technical layers like physical and mac layer are

Adaptive OFDM

4

discussed and then relation between other wireless technology their limitation and drawbacks

will be plotted i.e., .Development from 1G to 4G.

Next it introduces the theory behind OFDM as well as some of its advantages and Functionality

issues. This chapter discusses basic OFDM transceiver architecture, cyclic prefix, intersymbol

interference, intercarrier interference and peak to average power ratios. This chapter also

presents a few results in both Additive White Gaussian Noise, and impulsive noise

environments. OFDM model for WiMax Physical layer using Simulink is given at the end of the

chapter. Development of wireless channel is focused nextSincethis report is based on computer

simulation the channel used in simulation must match to that of practical free space. Taking into

consideration of multipath propagation, delay spread, free space resistance to electromagnetic

propagation good channel must be designed for this AOFDM model.

Then on Adaptive coding and modulation is discussed here the encoder and decoder architecture,

and decoding algorithms are explained. This will elaborate on the performance theory of the

codes and find out why they perform so well. Adaptive modulation model for WiMax Physical

layer using Simulink is given at the end of the chapter.simulated results of this work and a few

suggestions to improve are made in the end. Then, results on the combination of Adaptive coding

and OFDM simulation results are found here.

Adaptive OFDM

5

Chapter 2

Wireless Metropolitan Area Network

2.1 Features of WiMax

WiMax-Worldwide Interoperability for Microwave Access has a set of salient features: (i)

OFDM based physical layer: WiMax is based on orthogonal frequency division multiplexing that

offers multipath resistance and allow NLoS communication; (ii) High data rate: WiMax can

support very high peak data rate which is as high as 74 mbps; (iii) Quality of service (QoS):

WiMax MAC layer is responsible for QoS. WiMax MAC layer support real time, non-real time

and best effort data traffic and its high data rate, sub channelization, and flexible scheduling

improve the QoS; (IV) Flexible architecture: WiMax architecture is very flexible. It can support

point to point and point to multipoint connection according to its requirements. It also supports

IP-based architecture that is easily converge with other networks and takes advantage of

application development from the existing IP based application; (v) Mobility support: WiMax

offer optimized handover which support full mobility application such as voice over internet

protocol (VoIP). It has also the power saving mechanism which increases the battery life of

handheld devices; (VI) Scalability: WiMax offer scalable network architecture that support user

roaming indifferent networks. It also enhances the broadband access capability, and (vii) Strong

Security: WiMax support extensible security feature for reliable data exchange. It use Advanced

Encryption Standard (AES) encryption for secure transmission and for data integrity, it use data

authentication mechanism.

WiMax is a revolutionary wireless technology that has a rich set of technological improvements

compared to the other broadband access technology. The following table gives an overview on

the comparison between the mentioned systems, WiMax and its two closest competitors, Wi-Fi

and universal mobile telecommunications system (UMTS).[1]

Adaptive OFDM

6

Table1: Comparative table between Wi-Fi, WiMax and UMTS

Wi-Fi Wi-MAX UMTS

HSDPA

Standard IEEE

802.11 IEEE 802.16

IMTS

2000

Channel width 20Mbps Variable

≤28 Mbps

Variable

≤20Mbps

Fixed

5Mhz

Spectrum 2.4/5.2 GHz 10-66GHz 2-11GHz 2GHz

Data Rate 2/54Mbps 240Mbps 70Mbps 1/14Mbps

Range 100m 12-15Km 1-7Km 50Km

Multiplexing TDM FDM/TDM FDM/TDM FDM

Transmission OFDM SC AOFDM/OFDMA WCDMA

Mobility pedestrian NO Vehicular

802.16e Vehicular

Advantages Throughput and

costs

Throughput

and range

Throughput and

range

Mobility and

range

Disadvantages Short

range

Interference

Issues

Interference

Issues

Low rates and

expensive

In the IEEE 802.16e–2005, this layer has been modified to scalable OFDMA, where the FFT size

is variable and can take any one of the following values: 128, 512, 1,024, and 2,048.The variable

FFT size allows for optimum operation/ implementation of the system over a wide range of

channel bandwidths and radio conditions. Table below shows the IEEE 802.16 standard

developed in different years.

Adaptive OFDM

7

Table 2: Basic Data on IEEE 802.16 Standards

WiMax standards IEEE

802.16e-2005

IEEE

802.16e-2004

IEEE

802.16a

IEEE

802.16e-2001

Completed December 2001 January 2003 June 2004 December 2005

Spectrum 10-66GHz 2-11GHz 2-11GHz 2-6GHz

Propagation/Channel

conditions LOS NLOS NLOS NLOS

Bit rate Up to 134Mbps Up to 75Mbps Up to 75Mbps Up to 5Mbps

Modulation QPSK,16QAM,

64QAM

BPSK,QPSK,

16-QAM,64-

QAM,256-

QAM

256 sub carriers

OFDM

BPSK,QPSK,16

-

QAM,64QAM,2

56-QAM

Scalable

OFDMA,QPSK,

16-QAM,64-

QAM,256-QAM

Mobility Fixed Fixed Fixed/Nomadic Portable/mobile

2.2 STANDARDS

Nowhere in the modern computing field is the proliferation of acronyms and numerical

designators more prevalent than in wireless networking. Here is the short version of what you

need to know to bring some order to the chaos.

2.2.1 IEEE 802.11/16.

The IEEE (Institute of Electrical and Electronics Engineers) is the body responsible for setting

standards for computing devices. They have established a committee to set standards for Local

Area and Metropolitan Area Networking named the “802 LMSC” (LANMAN Standards

Committee). Within this committee there are workgroups tasked with specific responsibilities,

and given a numeric designation such as “11, 16”. In this case the 802.16 workgroup is tasked

with developing the standards for wireless networking .Within this 802.16 workgroup, there are

task groups with even more specific tasks, and these groups are designated with an alphabetic

Adaptive OFDM

8

character such as “a”, or “b”, or “e”. There is no apparent logic to the ordering of these

characters and none should be inferred. The specific groups and tasks concerning wireless

networking hardware standards are outlined below.

2.2.2 IEEE 802.16 WIMAX Standards

WiMax technology is based on the IEEE 802.16 standard, which is also called Wireless MAN.

The IEEE 802.16 group was formed in 1998 to develop an air interface standard for wireless

broadband. The group’s initial focus was the development of a LoS-based point-to-multipoint

wireless broadband system for operation in the 10–66GHz millimeter wave band. The first

version of the standard IEEE802.16 was approved on December 2001 and it has gone through

many amendments to accommodate new features and functionalities. The current version of the

standard IEEE 802.16, approved on September 2004, consolidates all the previous versions of

the standards. This standard specifies the air interface for fixed BWA systems supporting

multimedia services in licensee and licensed exempt spectrum. The Working Group approved the

amendment IEEE 802.16e-2005 to IEEE802.16-2004 on February 2006. The IEEE 802.16

standard contains the specification of Physical (PHY) and Medium Access Control (MAC) layer

for BWA. There are specific names for each physical layer interface . In the IEEE 802.16e–2005,

this layer has been modified to scalable OFDMA, where the FFT size is variable and can take

any one of the following values: 128, 512, 1,024, and 2,048 .The variable FFT size allows for

optimum operation/ implementation of the system over a wide range of channel bandwidths and

radio conditions; this PHY layer has been accepted by WiMax for mobile and portable

operations and is also referred to as mobile WiMax.

2.3 Relationship with other wireless technologies

Wireless access to data networks is expected to be an area of rapid growth for mobile

communication systems. The huge uptake rate of mobile phone technologies, WLANs and the

exponential growth that is experiencing the use of the Internet have resulted in an increased

demand for new methods to obtain high capacity wireless networks.

Adaptive OFDM

9

Figure 2.1: Convergence in wireless communications.

WiMax may be seen as the fourth generation (4G) of mobile systems as the convergence of

cellular telephony, computing, Internet access, and potentially many multimedia applications

become a real fact. The mentioned convergence between wireless and cellular networks is

illustrated in Figure 2.1 .In any case, both WLAN and cellular mobile applications are being

widely expanded to offer the demanded wireless access. However, theyexperience several

difficulties for reaching a complete mobile broadband access, bounded by factors such as

bandwidth, coverage area, and infrastructure costs. On one hand, Wi-Fi provides a high data rate,

but only on a short range of distances and with a slow movement of the user. On the other hand,

UMTS13 offers larger ranges and vehicular mobility, but instead, it provides lower data rates,

and requires high investments for its deployment. WiMax tries to balance this situation. As

shown in Figure 2.2, it fills the gap between Wi-Fi and UMTS, thus providing vehicular mobility

(included in IEEE 802.16e), and high service areas and data rates.

Adaptive OFDM

10

Figure 2.2: WiMax fills the gap between Wi-Fi and UMTS.

Therefore, while WiMax will complement Wi-Fi and UMTS in some of the possible scenarios

where these systems are not sufficiently developed, i.e. they face several problems in the

deployment and they do not offer enough capacity to serve all possible users, WiMax will

compete with Wi-Fi and UMTS also in other possible scenarios, where, in general, the costs in

the deployment, maintenance, or just the supply of the service would not be profitable.

2.4 WiMax vs. Wi-Fi

Wi-Fi or WLAN is the name with which the IEEE 802.11 standard-based products are known. It

includes the 802.11a specification, capable to offer data rates of 54 Mbps working in the

frequency band of 5.2 GHz; and the802.11b specification, in the 2.4 GHz frequency band, which

provides users with data rates of 11 Mbps. This technology has generally a coverage area of 100

meters and fixed channel bandwidths of 20MHz. WiMax appeared to fulfill the need for

delivering wireless access to MANs. It was designed to offer BWA services to metropolitan

areas providing users with larger coverage ranges and higher data rates. WiMax systems are able

to support users in ranges up to 50 km with a direct visibility to the base station and ranges from

1 to 7 km where no visibility is available. Rates from70 to 240 Mbps are offered and can be

achieved with this technology. However, WiMax does not create a conflict with the mentioned

Adaptive OFDM

11

Wi-Fi, asthey are complementary technologies. WiMax provides a low cost way to backhaul Wi-

Fi hot-spots and WLAN points in businesses and homes, offering a wireless last mile extension

for cable and DSL infrastructures.

2.5 WiMax vs. UMTS

UMTS is identified with the so-called third generation of cellular networks standardized by the

3GPP16. The frequency bands that are assigned to this technology are the licensed frequencies

from 1885 to 2025 MHz, and from2110 to 2200 MHz It uses wideband code division multiple

access (WCDMA) as the carrier modulation scheme, and it has been specified as an integrated

solution for mobile voice and data with wide coverage area, offering data rates that may decrease

while the velocity of the user increases. This system provides for theoretical bit rates of up to 384

kbps in high mobility situations, which rise as high as 2 Mbps in stationary user environments,

employing a5 MHz channel width. Moreover, HSDPA17 technology further increases the

throughput speeds, providing theoretical data rates as high as 14 Mbps [8].WiMax is becoming a

serious threat for 3G cellular networks because of its broadband and distance capabilities, as well

as its ability to effectively support voice with full QoS. WiMax is also able to offer higher data

rates than UMTS, but it does not allow the same grade of mobility. However, it is expected to be

set up as an alternative to cellular networks, as the investments the operators need to carry out for

its deployment are not so high.

This chapter briefs the WiMax characters. OFDM and Adaptive OFDM modulation are two main

concepts will be studied in chapter 3 and chapter 4.

Adaptive OFDM

12

Chapter3

Orthogonal Frequency Division Multiplexing Access

3.1 INTRODUCTION

The principle of orthogonal frequency division multiplexing (OFDM) modulation has been in

existence for several decades. However, in recent years these techniques have quickly moved out

of textbooks and research laboratories and into practice in modern communications systems. The

techniques are employed in data delivery systems over the phone line, digital radio and

television, and wireless networking systems. What is OFDM? And why has it recently become

so popular?

This chapter is organized as follows. Following this introduction, section 3.2, 3.3 gives brief

details about single carrier modulation, FDM modulation systems. Section 3.4 discusses

definition of orthogonality, and principle of OFDM. Section 3.5 discusses the how FFT

maintains orthogonality. Section 3.6 discusses the generation and reception of OFDM in detail.

Section 3.7

addresses about the guard period used in OFDM systems. Section 3.8 presents the Simulink

implementation of OFDM finally 3.10 explains advantages, disadvantages and applications of

OFDM.

3.2 THE SINGLE CARRIER MODULATION SYSTEM

A typical single-carrier modulation spectrum is shown in Figure 3.1. A single carrier system

modulates information onto one carrier using frequency, phase, or amplitude adjustment of the

carrier. For digital signals, the information is in the form of bits, or collections of bits called

symbols, that are modulated onto the carrier.

Adaptive OFDM

13

Figure.3.1 Single carrier spectrum

As higher bandwidths (data rates) are used, the duration of one bit or symbol of information

becomes smaller. The system becomes more susceptible to loss of information from impulse

noise, signal reflections and other impairments.

These impairments can impede the ability to recover the information sent. In addition, as the

bandwidth used by a single carrier system increases, the susceptibility to interference from other

continuous signal sources becomes greater. This type of interference is commonly labeled as

carrier wave (CW) or frequency interference.

3.3 FREQUENCY DIVISION MULTIPLEXING MODULATION SYSTEM

A typical Frequency division multiplexing signal spectrum is shown in figure 3.2.FDM extends

the concept of single carrier modulation by using multiple sub carriers within the same single

channel. The total data rate to be sent in the channel is divided between the various sub carriers.

The data do not have to be divided evenly nor do they have to originate from the same

information source. Advantages include using separate modulation demodulation customized to

a particular type of data, or sending out banks of dissimilar data that can be best sent using

multiple, and possibly different, modulation schemes.

Adaptive OFDM

14

Figure 3.2 FDM signal spectrum

Current national television systems committee (NTSC) television and FM stereo multiplex are

good examples of FDM. FDM offers an advantage over single-carrier modulation in terms of

narrowband frequency interference since this interference will only affect one of the frequency

sub bands. The other sub carriers will not be affected by the interference. Since each sub carrier

has a lower information rate, the data symbol periods in a digital system will be longer, adding

some additional immunity to impulse noise and reflections. FDM systems usually require a guard

band between modulated sub carriers to prevent the spectrum of one sub carrier from interfering

with another. These guard bands lower the system’s effective information rate when compared to

a single carrier system with similar modulation.

3.4 ORTHOGONALITY AND OFDM

If the FDM system above had been able to use a set of sub carriers that were orthogonal to each

other, a higher level of spectral efficiency could have been achieved. The guard bands that were

necessary to allow individual demodulation of sub carriers in an FDM system would no longer

be necessary. The use of orthogonal sub carriers would allow the subcarriers’ spectra to overlap,

thus increasing the spectral efficiency. As long as orthogonality is maintained, it is still possible

to recover the individual sub carriers’ signals despite their overlapping spectrums. If the dot

product of two deterministic signals is equal to zero, these signals are said to be orthogonal to

each other. Orthogonality can also be viewed from the standpoint of stochastic processes. If two

random processes are uncorrelated, then theyare orthogonal. Given the random nature of signals

in a communications system, this probabilistic view of orthogonality provides an intuitive

understanding of the implications of orthogonality in OFDM.

Adaptive OFDM

15

OFDM is implemented in practice using the discrete Fourier transform (DFT).Recall from

signals and systems theory that the sinusoids of the DFT form an orthogonal basis set, and a

signal in the vector space of the DFT can be represented as a linear combination of the

orthogonal sinusoids. One view of the DFT is that the transform essentially correlates its input

signal with each of the sinusoidal basis functions. If the input signal has some energy at a certain

frequency, there will be a peak in the correlation of the input signal and the basis sinusoid that is

at that corresponding frequency. This transform is used at the OFDM transmitter to map an input

signal onto a set of orthogonal sub carriers, i.e., the orthogonal basis functions of the DFT.

Similarly, the transform is used again at the OFDM receiver to process the received sub carriers.

The signals from the sub carriers are then combined to form an estimate of the source signal

from the transmitter. The orthogonal and uncorrelated nature of the sub carriers is exploited in

OFDM with powerful results. Since the basic functions of the DFT are uncorrelated, the

correlation performed in the DFT for a given sub carrier only sees energy for that corresponding

sub carrier. The energy from other sub carriers does not contribute because it is uncorrelated.

This separation of signal energy is the reason that the OFDM sub carriers’ spectrums can overlap

without causing interference.

3.5 MATHEMATICAL ANALYSIS

With an overview of the OFDM system, it is valuable to discuss the mathematical definition of

the modulation system. It is important to understand that the carriers generated by the IFFT chip

are mutually orthogonal. This is true from the very basic definition of an IFFT signal. This will

allow understanding how the signal is generated and how receiver must operate. Mathematically,

each carrier can be described as a complex wave:

( ) ( ) ( ( ) ( )) (3.1)

The real signal is the real part of Sc (t). Ac (t) and (t), the amplitude and phase ofthe carrier

can vary on a symbol by symbol basis. The values of the parameters are constant over the

symbol duration period t. OFDM consists of many carriers. Thus the complex signal

Ss(t) are represented by:

( )

∑ ( )

( ( ) ( )) (3.2)

Where

Adaptive OFDM

16

This is of course a continuous signal. If we consider the waveforms of each component of the

signal over one symbol period, then the variables Ac (t) and φc (t) take on fixed values, which

depend on the frequency of that particular carrier, and so can be rewritten:

( )

( )

If the signal is sampled using a sampling frequency of 1/T, then the resulting signal is

represented by:

( )

∑

( ) (3.3)

At this point, in equation 3.3 it has restricted the time over which analyzes the signal to N

samples. It is convenient to sample over the period of one data symbol. Thus the relationship:

t=NT. By simplifying the equation 3.3, without a loss of generality by letting ω0=0, then the

signal becomes:

( )

∑

( ) (3.4)

Now equation 3.4 can be compared with the general form of the inverse Fourier transform:

( )

∑ (

)

(3.5)

In Equation 3.4 the function

is no more than a definition of the signal in the sampled

frequency domain and s (kT) is the time domain representation. Eqns.4 and5 are equivalent if:

(3.6)

This is the same condition that was required for orthogonality Thus, one consequence of

maintaining orthogonality is that the OFDM signal can be defined by using Fourier transform

procedures.

Adaptive OFDM

17

3.6 OFDM GENERATION AND RECEPTION

The primary advantage of OFDM over single-carrier schemes is its ability to cope with severe

channel conditions (for example, attenuation of high frequencies in a long copper wire,

narrowband interference and frequency-selective fading due to multipath) without complex

equalization filters. Channel equalization is simplified because OFDM may be viewed as using

many slowly modulated narrowband signals rather than one rapidly modulated wideband signal.

The low symbol rate makes the use of a guard interval between symbols affordable, making it

possible to eliminate inter-symbol interference (ISI) and utilize echoes and time-spreading (that

shows up as ghosting on analogue TV) to achieve a diversity gain, i.e. a signal-to-noise ratio

improvement. This mechanism also facilitates the design of single frequency networks (SFNs),

where several adjacent transmitters send the same signal simultaneously at the same frequency,

as the signals from multiple distant transmitters may be combined constructively, rather than

interfering as would typically occur in a traditional single-carrier system. Figure below shows the

structure of transmitter and receiver for a typical OFDM system.

Figure3.3 OFDM Transmitter section

Adaptive OFDM

18

Figure 3.4 OFDM receiver section

OFDM signals are typically generated digitally due to the difficulty in creating large banks of

phase locks oscillators and receivers in the analog domain. Fig 3.3 shows the block diagram of

atypical OFDM transceiver [15]. The transmitter section converts digital data to be transmitted,

into a mapping of subcarrier amplitude and phase. It then transforms this spectral representation

of the data into the time domain using an Inverse Discrete Fourier Transform (IDFT). The

Inverse Fast Fourier Transform (IFFT) performs the same operations as an IDFT, except that it is

much more computationally efficiency, and so is used in all practical systems. In order to

transmit the OFDM signal the calculated time domain signal is then mixed up to the required

frequency.

Figure 3.5 OFDM spectrums and time domain signal

The receiver performs the reverse operation of the transmitter, mixing the RF signal to base band

Adaptive OFDM

19

for processing, then using a Fast Fourier Transform (FFT) to analyze the signal in the frequency

domain. The amplitude and phase of the sub carriers is then picked out and converted back to

digital data. The IFFT and the FFT are complementary function and the most appropriate term

depends on whether the signal is being received or generated. In cases where the signal is

independent of this distinction then the term FFT and IFFT is used interchangeably.

3.6.1 Serial to parallel conversion

Data to be transmitted is typically in the form of a serial data stream. In OFDM, each symbol

typically transmits 40 - 4000 bits, and so a serial to parallel conversion stage is needed to convert

the input serial bit stream to the data to be transmitted in each OFDM symbol. The data allocated

to each symbol depends on the modulation Scheme used and the number of sub carriers. For

example, for a sub carrier modulation of 16 QAM each sub carrier carries 4 bits of data, and so

for a transmission using 100 sub carriers the number of bits per symbol would be 400.At the

receiver the reverse process takes place, with the data from the sub carriers being converted back

to the original serial data stream. When an OFDM transmission occurs in a multipath radio

environment, frequency selective fading can result in groups of sub carriers being heavily

attenuated, which in turn can result in bit errors. These nulls in the frequency response of the

channel can cause the information sent in neighboring carriers to be destroyed, resulting in a

clustering of the bit errors in each symbol. Most Forward Error Correction (FEC) schemes tend

to work more effectively if the errors are spread evenly, rather than in large clusters, and so to

improve the performance most systems employ data scrambling as part of the serial to parallel

conversion stage. This is implemented by randomizing the sub carrier allocation of each

sequential data bit. At the receiver the reverse scrambling is used to decode the signal. This

restores the original sequencing of the data bits, but spreads clusters of bit errors so that are

approximately uniformly distributed in time. This randomization of the location of the bit errors

improves the performance of the FEC and the system as a whole.

3.6.2 An Introduction to Subcarrier Modulation

One way to communicate a message signal whose frequency spectrum does not fall within that

fixed frequency range, or one that is otherwise unsuitable for the channel, is to change a

transmittable signal according to the information in the message signal. This alteration is called

Adaptive OFDM

20

modulation, and it is the modulated signal that is transmitted. The receiver then recovers the

original signal through a process called demodulation. Modulation is a process by which a carrier

signal is altered according to information in a message signal. The carrier frequency, denoted Fc,

is the frequency of the carrier signal. The sampling rate, Fs, is the rate at which the message

signal is sampled during the simulation. The frequency of the carrier signal is usually much

greater than the highest frequency of the input message signal. The Nyquist sampling theorem

requires that the simulation sampling rate Fs be greater than two times the sum of the carrier

frequency and the highest frequency of the modulated signal, in order for the demodulator to

recover the message correctly.

3.6.3 Baseband versus Pass band Simulation

For a given modulation technique, two ways to simulate modulation techniques are called

baseband and pass band. Baseband simulation requires less computation. In this report, baseband

simulation will be used.

3.7 GUARD PERIOD

For a given system bandwidth the symbol rate for an OFDM signal is much lower than a single

carrier transmission scheme. For example for a single carrier BPSK modulation, the symbol rate

corresponds to the bit rate of the transmission. However for OFDM the system bandwidth is

broken up into NC sub carriers, resulting in a symbol rate that is NC times lower than the single

carrier transmission. This low symbol rate makes OFDM naturally resistant to effects of Inter-

Symbol Interference (ISI) caused by multipath propagation. Multipath propagation is caused by

the radio transmission signal reflecting off objects in the propagation environment, such as walls,

buildings, mountains, etc. These multiple signals arrive at the receiver at different times due to

the transmission distances being different. This spreads the symbol boundaries causing energy

leakage between them. The effect of ISI on an OFDM signal can be further improved by the

addition of a guard-period to the start of each symbol. This guard period is a cyclic copy that

extends the length of the symbol waveform. Each sub carrier, in the data section of the symbol,

(i.e. the OFDM symbol with no guard period added, which is equal to the length of the IFFT size

used to generate the signal) has an integer number of cycles. Because of this, placing copies of

the symbol end-to-end results in a continuous signal, with no discontinuities at the joints.

Adaptive OFDM

21

Figure 3.6Addition of a guard period to an OFDM signal

Thus by copying the end of a symbol and appending this to the start results in a longer symbol

time. Figure3.6 shows the insertion of a guard period. The total length of the symbol is TS=TG+

TFFT, where Ts is the total length of the symbol in samples, TG is the length of the guard period in

samples, and TFFT is the size of the IFFT used to generate the OFDM signal. In addition to

protecting the OFDM from ISI, the guard-period also provides protection against time-offset

errors in the receiver.

3.7.1 Protection against time offset

To decode the OFDM signal the receiver has to take the FFT of each received symbol, to work

out the phase and amplitude of the sub carriers. For an OFDM system that has the same sample

rate for both the transmitter and receiver, it must use the same FFT size at both the receiver and

transmitted signal in order to maintain sub carrier orthogonality. Each received symbol has TG +

TFFT samples due to the added guard period. The receiver only needs TFFT samples of the

received symbol to decode the signal . The remaining TG samples are redundant and are not

needed. For an ideal channel with no delay spread the receiver can pick any time offset, up to the

length of the guard period, and still get the correct number of samples, without crossing a symbol

boundary. Because of the cyclic nature of the guard period changing the time offset simply

results in a phase rotation of all the sub carriers in the signal. The amount of this phase rotation is

proportional to the sub carrier frequency, with a sub carrier at the nyquist frequency changing by

180° for each sample time offset. Provided the time offset is held constant from symbol to

symbol, the phase rotation due to a time offset can be removed out as part of the channel

Adaptive OFDM

22

equalization . In multipath environments ISI reduces the effective length of the guard period

leading to a corresponding reduction in the allowable time offset error. The addition of guard

period removes most of the effects of ISI. However in practice, multipath components tend to

decay slowly with time, resulting in some ISI even when a relatively long guard period is used.

3.7.2 Guard period overhead and sub carrier spacing

Adding a guard period lowers the symbol rate, however it does not affect the subcarrier spacing

seen by the receiver. The sub carrier spacing is determined by the sample rate and the FFT size

used to analyze the received signal.

(3.7)

In Equation (3.6), Δf is the sub carrier spacing in Hz, Fs is the sample rate in Hz, and NFFT is the

size of the FFT. The guard period adds time overhead, decreasing the overall spectral efficiency

of the system.

3.7.3 Inter-symbol interference

Assume that the time span of the channel is Lc samples long. Instead of a single carrier with a

data rate of R symbols/ second, an OFDM system has N subcarriers, each with a data rate of R/N

symbols/second. Because the data rate is reduced by a factor of N, the OFDM symbol period is

increased by a factor of N.

Figure 3.7: Example of inter-symbol interference.

By choosing a green symbol was transmitted first, followed by the blue symbol. Appropriate

value for N, the length of the OFDM symbol becomes longer than the time span of the channel.

Because of this configuration, the effect of inter-symbol interference is the distortion of the first

Lc samples of the received OFDM symbol. An example of this effect is shown in Figure 3.7. By

noting that only the first few samples of the symbol are distorted, one can consider the use of a

Adaptive OFDM

23

guard interval to remove the effect of inter-symbol interference. The guard interval could be a

section of all zero samples transmitted in front of each OFDM symbol . Since it does not contain

any useful information, the guard interval would be discarded at the receiver. If the length of the

guard interval is properly chosen such that it is longer than the time span of then channel, the

OFDM symbol itself will not be distorted. Thus, by discarding the guard interval, the effects of

inter-symbol interference are thrown away as well.

3.7.4 Intra-symbol interference

The guard interval is not used in practical systems because it does not prevent an OFDM symbol

from interfering with itself. This type of interference is called intra-symbol interference. The

solution to the problem of intra-symbol interference involves a discrete-time property. Recall

that in continuous-time, a convolution in time is equivalent to a multiplication in the frequency-

domain. This property is true in discrete-time only if the signals are of infinite length or if at least

one of the signals is periodic over the range of the convolution. It is not practical to have an

infinite-length OFDM symbol; however, it is possible to make the OFDM symbol appear

periodic. This periodic form is achieved by replacing the guard interval with something known

as a cyclic prefix of length Samples. The cyclic prefix is a replica of the last Lp samples of the

OFDM symbol where Lp>Lc. Since it contains redundant information, the cyclic prefix is

discarded at the receiver. Like the case of the guard interval, this step removes the effects of

inter-symbol interference. Because of the way in which the cyclic prefix was formed, the

cyclically-extended OFDM symbol now appears periodic when convolved with the channel. An

important result is that the effect of the channel becomes multiplicative. In a digital

communications system, the symbols that arrive at the receiver have been convolved with the

time domain channel impulse response of Length Lc samples. Thus, the effect of the channel is

convolution. In order to undo the effects of the channel, another convolution must be performed

at the receiver using a time domain filter known as an equalizer. The length of the equalizer

needs to be on the order of the time span of the channel. The equalizer processes symbols in

order to adapt its response in an attempt to remove the effects of the channel. Such an equalizer

can be expensive to implement in hardware and often requires a large number of symbols in

order to adapt its response to a good setting. In OFDM, the time-domain signal is still convolved

with the channel response . However, the data will ultimately be transformed back into the

Adaptive OFDM

24

frequency-domain by the FFT in the receiver. Because of the periodic nature of the cyclically-

extended OFDM symbol, this time-domain convolution will result in the multiplication of the

spectrum of the OFDM signal (i.e., the frequency- domain constellation points) with the

frequency response of the channel.

The result is that each sub carrier’s symbol will be multiplied by a complex number equal to the

channel’s frequency response at that sub carrier’s frequency. Each received sub carrier

experiences a complex gain (amplitude and phase distortion) due to the channel. In order to undo

these effects, a frequency- domain equalizer is employed. Such an equalizer is much simpler than

a time-domain equalizer. The frequency domain equalizer consists of a single complex

multiplication for each sub carrier. For the simple case of no noise, the ideal value of the

equalizer’s response is the inverse of the channel’s frequency response .

3.8 Simulink Implementation of OFDM IEEE 802.16

Figures 3.8(a)-(c) below shows the method of designing OFDM modulation in Simulink.52 sub

band data channel is given to IFFT block. Cyclic prefix are added in between data’s to avoid ISI.

And also Radix 2 FFT is easier to implement in DSP.

Figure 3.8(a) OFDM Modulator general block.

Adaptive OFDM

25

Figure 3.8(b) OFDM Modulator Vector operation.

Figure 3.8(c) OFDM Modulator Simulink parameters

Adaptive OFDM

26

3.9 OFDM Demodulator Block Simulink

OFDM Demodulator does reverse operation it discards the cyclic prefix and the perform FFT of

the received data given in Figure 3.9a-b.

Figure 3.9(a) OFDM demodulation general block.

Figure 3.9(b) OFDM demodulation Simulink block with parameters.

Adaptive OFDM

27

Figure 3.10 shows the comunication system model with OFDM modulation. This chapter briefs

the design parametr and mathematical discription of OFDM system. To match the practical

condition one must learn the channel properties and have to design the channel which should not

work only in the Ideal case. Adaptive modulation will be added in chapter 4 to this OFDM

model.

Figure 3.10 OFDM communication system Simulinkmodel.

Adaptive OFDM

28

Chapter 4

Adaptive Modulation and Coding

The growing demand of all types of services, not only voice and data but also multimedia

services, aims for the design of increasingly more intelligent and agile communication systems,

capable of providing spectrally efficient and flexible data rate access. These systems are able to

adapt and adjust the transmission parameters based on the link quality, improving the spectrum

efficiency of the system, and reaching, in this way, the capacity limits of the underlying wireless

channel. Link adaptation techniques, often referred to as adaptive modulation and coding

(AMC), are a good way for reaching the cited requirements. They are designed to track the

channel variations, thus changing the modulation and coding scheme to yield a higher throughput

by transmitting with high information rates under favorable channel conditions and reducing the

information rate in response to channel degradation.

This chapter is focused on the implementation of such techniques. A theoretical explanation,

necessary to understand the operation principles of adaptive modulation and coding, is briefly

given. Also a performance analysis of the AMC scheme implemented in the simulator is

furthermore presented in this chapter.

4.1 Theory on the AMC technique

4.1.1 Introduction to adaptive transmission mechanisms

Since the available radio spectrum for wireless communications is extremely scarce, there is a

rapid growth in the demand of services for portable and wireless devices, and, as these services

become more and more complex, the use of spectrally efficient transmission schemes supporting

higher information rates is needed. In traditional communication systems, the transmission is

designed for a “worst case" channel scenario thus coping with the channel variations and still

delivering an error rate below a specific limit. Adaptive transmission schemes, however, are

designed to track the channel quality by adapting the channel throughput to the actual channel

state. These techniques take advantage of the time-varying nature of the wireless channel to vary

the transmitted power level, symbol rate, coding scheme, constellation size, or any combination

Adaptive OFDM

29

of these parameters, with the purpose of improving the link average spectral efficiency, i.e. the

number of information bits transmitted per second per Hz bandwidth used. Adaptive modulation

and coding (AMC) is a promising tool for increasing the spectral efficiency of time-varying

wireless channels while maintaining a predictable BER. In AMC, not only the modulation order

but also the FEC schemes are varied by adjusting their code rate to the variations in the

communication channel. For example, in periods of high fade when the channel is in a poor state,

i.e. low SNR, the signal constellation size is reduced in order to improve fidelity, lowering the

effective SNR to make transmission more robust. Conversely, in periods of low fade or high gain

(high SNR); the signal constellation size is increased in order to allow higher data rate

modulation schemes to be employed with low probability of error, thus improving the

instantaneous SNR. An example of utilization of the cited AMC scheme is illustrated in Figure

4.1(a). It shows that as the range increases, the system steps down to a lower modulation, but as

closer to the base station, higher order modulations can be used for increased throughput.

Figure 4.1(a): Scheme for the utilization of AMC.

4.1.2 Performance of the AMC scheme

A good performance of AMC schemes requires accurate channel estimation at the receiver and a

reliable feedback path between that estimator and the transmitter on which the receiver reports

channel state information (CSI) to the transmitter. In order to perform a good implementation the

next steps must be followed:

Adaptive OFDM

30

Channel quality estimation the transmitter requires an estimate of the expected channel

conditions for the next transmission interval. Since this knowledge can only be gained by

prediction from past channel quality estimations, the adaptive system can only operate efficiently

in an environment with relatively slowly-varying channel conditions . Therefore, the delay

between the quality estimation and the actual transmission in relation to the maximal Doppler

frequency of the channel is crucial for the system implementation since poor system performance

will result if the channel estimate is obsolete at the time of transmission.

Although there are different ways to estimate the channel quality, the AMC scheme explained in

this section is related with the measurement of the SNR, as it is often used in many systems as

the channel quality information. Parameter adaptation the choice of the appropriate modulation

and coding mode to be used in the next transmission is made by the transmitter, based on the

prediction of the channel conditions for the next time interval. An SNR threshold such that it

guarantees a BER below the target BER, BER0, is defined by the system for each scheme

whenever the SNR is above the SNR threshold.

The SNR thresholds are obtained from the BER vs. SNR characteristics of a modulation mode on

an AWGN channel. As outlined in Figure 4.1(b), the method consists on splitting the SNR range

into N + 1 SNR regions by N + 2 SNR thresholds, * + with and .Each of the

N schemes is then assigned to operate within a particular SNR region. When the SNR falls

within the SNR region the associated channel state information is sent back to the

transmitter. Thetransmitter then adapts its transmission rate and coding and modulationschemes

by transmitting with a modulation scheme such that it guarantees a BER below BER0. This

enables the system to transmit with high spectral efficiency when the SNR is high, and to reduce

the spectral efficiency as the SNR decreases [4].

Adaptive OFDM

31

Figure 4.1(b): BER vs. SNR relationship and corresponding SNR thresholds

for N coding and modulation schemes employed by AMC.[21]

Feedback mechanism once the receiver has estimated the channel SNR, converted it into BER

information for each mode candidate, and, based on a target BER, selected the mode that yields

the largest throughput while remaining within the BER target bounds, it has to feed back the

selected mode to the transmitter in order that the adaptation can be performed. However, the

challenge associated with adaptive modulation and coding is that the mobile channel is time-

varying, and thus, the feedback of the channel information becomes a limiting factor. Therefore,

the assumption of a slowly-varying as well as a reliable feedback channel is necessary in order to

Achieve an accurate performance of the AMC scheme. In this way, no delay or transmission

error can occur in the feedback channel so that no discrepancy between the predicted and the

actual SNR of the next frame appears. Moreover, the receiver must also be informed of which

demodulator and decoding parameters to employ for the next received packet.

Adaptive OFDM

32

4.2 AMC implementation

This section explains the implementation of the cited adaptive modulation and coding scheme. A

few differences between this simulator version and the last one that was mentioned when

implementing the MIMO system are encountered. On one hand, besides the channel coefficients,

the channel estimator also estimates the corresponding SNR. On the other hand, a new block is

introduced with the aim of deciding the modulation and coding mode to be switched at the

transmitter. Furthermore, as appreciated in Figure4.2, not only the encoder and mapper in the

transmit side but also the decoder and demapper in the receiver are grouped into a unique block,

which implementation and function will be later discussed.

Figure 4.2: AMC mechanism in the WiMax system.

4.2.1 SNR estimation

The calculation of the SNR is performed in the channel estimator. As well-known, the SNR is

obtained from dividing the signal power between the noise powers. Thus, the instantaneous SNR

for each frame is calculated as

SNR=

Where are the average signals and noise power, respectively, in each frame.While the

signal power is obtained from the channel coefficients, the noise power is calculated from the

noise variance, the long training sequences are an interpolated version of the sequence PALL,

where either PEVEN or PODD use a subset of even or odd subcarriers, respectively, while keeping

null the remaining subset. However, the training symbols are received with some additive noise,

Adaptive OFDM

33

and thus, the previous null carriers has now a non-zero value. The noise is calculated from these

carriers [5]. Furthermore, as it happened with the channel estimation, it has to be taken into

account whether the system uses one or two transmit antennas. When only one transmit antenna

is used the noise is obtained from PEVEN. If the system has two transmit antennas, both PEVEN and

PODD are used instead.

4.2.2 The AMC block

This block deals with the task of finding the decision thresholds, deciding which of the

modulation and coding schemes employed in the next frame transmission, and feeding back this

information not only to the transmitter but also to the receiver. As explained before, the SNR

thresholds are calculated from the BER vs. SNR curves. The curves depicted in Figure 4.2 (b)

have been obtained from simulations performed with a perfect knowledge of the channel

coefficients in an AWGN scenario. The figure shows seven curves corresponding to the seven

differentmodulations and coding schemes allowed by the WiMax system, defined from AMC1 to

AMC7[6]. The set of adaptation/switching thresholds is obtained by reading the SNR points

corresponding to a target BER. The implementation of this method is performed in a Matlab file.

The obtained coded BER values for a given SNR, for each of the different AMC schemes, are

programmed. According to a target BER specified by the user a selection function calculates the

SNR thresholds, and the AMC scheme to use in the next frame is decided by comparing these

thresholds with the estimated SNR of the channel.

4.2.3WiMax Adaptive Modulation model using Simulink

In this report three types of Modulation BPSK, QPSK and QAM are used with Convolution

encoder and Reed Solomon as Forward error correction codes as shown in Figure 4.3 U is the

Rate ID calculated using SNR. 7 different cases are implemented by trial and error method for

best result. Figure 4.4 shows the internal Mask of the BPSK ½ of figure 4.3.

Next chapter provide the result of WiMax model which was designed using all the technique that

are studied till now.it goes with simulation parameter design, digital modulation, different

coding, interleaving first Constellation diagram variation is discussed then it goes with

performance of PSD plot for increasing SNR finally improvement technique for BER are

given[7].

Adaptive OFDM

34

Figure 4.3: Forward Error correction and Modulator bank

Figure 4.4: Forward error encoder and BPSK modulation block

1

Out

In1 Out1

RateID6 - 64QAM 3/4

In1 Out1

RateID5 - 64 QAM 2/3

In1 Out1

RateID4 - 16QAM 3/4

In1 Out1

RateID3 - 16QAM 1/2

In1 Out1

RateID2 - QPSK 3/4

In1 Out1

RateID1 - QPSK 1/2

In1 Out1

RateID0 - BPSK 1/2

Merge

u==6

u==5

u==4

u==3

u==2

u==1

u==0u-1

0-based rate

2

RateID

1

txBits

1

Out1

Pad

Zero pad

tail byte

Pad

Select

Bits

Interleaver

Interleaver

Convolutional

Encoder

Convolutional

Encoder

BPSK

BPSK

Modulator

Enable

1

In1

Adaptive OFDM

35

Chapter 5

RESULTS

5.1 Introduction

An AOFDM system was modeled using Matlab to allow various parameters of the system to be

varied and tested. The aim of doing the simulations was to measure the performance ofAOFDM

under AWGN channel and Rician channel conditions, for different modulation schemes like

BPSK, QPSK, and QAM used in IEEE 802.16e wireless MAN standard.

Following this introduction, section 5.2 discusses model used in simulation, steps in AOFDM

simulation, modulation schemes. Section 5.3 presents the parameters used in simulation. Section

5.4 provides OFDM design using Simulink the simulation 5.5 provides results of AOFDM

system for different modulation schemes their constellation diagrams and PSD of modulation. It

also shows the results to compare the performance of AOFDMand Non AOFDM.

5.2 simulation model

Since the main goal of this report was to simulate the AOFDM system by utilizing different

modulation.

The block diagram of the entire system is shown in Figure 5.1.

5.3 simulation parameters

Digital Modulation: BPSK QPSK, QAM

Convolution code rate: ½

Reed Solomon code rate: 1/2, 3/4, 2/3.

Data Source: Bernoulli.

Inter-leaver : pseudo random inter-leaver

The simulation follows the procedure listed below:

1. Generate the information bits randomly.

2. Encode the information bits using a convolution encoder or Reed Slomon with the specified

generator matrix.

3. Use BPSK, QPSK or different QAM modulation to convert the binary bits, 0 and 1, into

Adaptive OFDM

36

complex signals (before these modulations use zero padding) depending on Rate ID.

4. Performed serial to parallel conversion.

5. Use IFFT to generate OFDM signals, zero padding is being done before IFFT.

6. Use parallel to serial convertor to transmit signal serially.

7. Introduce noise to simulate channel errors. We assume that the signals are transmitted over an

AWGN (Additive White Gaussian Noise) and Rician channel.

8. At the receiver side, perform reverse operations to decode the received sequence.

9. Count the number of erroneous bits by comparing the decoded bit sequence with the original

one.

10. Calculate the BER and plot it.

Figure 5.1WIMAX SIMULINK MODEL

IEEE 802.16 WirelessMAN-OFDM PHY Downlink

BER

#Bits

#Errors Double-click to set

channel parameters

Double-click to

set model parameters

BER

Signal To

Workspace

Rx Constellation

RateID

Bernoulli

Binary

Random Data

Source

OFDM

Receiver

OFDM

Transmitter

Model

Parameters

SNR

Estimation

IFFT Input

Packing

[rateID]

[rateID]

[rateID]

[rateID]

FEC &

Modulator Bank

Extract

Data Carriers

Est. SNR (dB)

Digital

Pre-Distortion and

Nonlinear

Amplifier

Demodulator

& FEC Bank

Gain & Phase

Compensator

FFT

Channel

Spectrum

Multipath Fading

Channel with

AWGN

Bit Error Rate

Display

Bit Error Rate

Calculation

Adaptive

Rate

Control

u-1

Adaptive OFDM

37

All the simulations are done to achieve BER at .For simulation results two channel are AWGN

and Rician are used. The BER performance of AOFDM system is compared with individual

OFDM system. As mentioned before, bursty errors deteriorate the performance of the any

communications system. The burst errors can happen either by impulsive noise or by deep

frequency fades. Fig 5.2 shows the performance of the uncoded OFDM system with AWGN.

The graphs shows the BPSK nodulation perform well in low SNR whereas 64QAM perform well

in 25dB signal to noise ratio. Existing wireless channel has much resistance to the flow of signals

i.e., multipath fading, Doppler effect so AWGN alone cannot be considered as channel model

here channel is designed using the combination of AWGN and Rician model.

Fig 5.2 BER vs. SNR plot for OFDM using BPSK, QPSK, 16 QAM, 64 QAM

Adaptive OFDM

38

5.4 OFDM design using Simulink

1. let the number of carrier be the same as the FFT length, i.e.

N=NF;

2. Use Differential QPSK Encoding and Decoding, so that we do

not need to estimate the channel’s frequency response.

Initial Callback function:

% OFDM parameters (IEEE802.16)

Fs=20e6; % symbol data rate (uncoded) in Hz

N=256; % FFT sample size

L=56; % Cyclic Prefix sample length

% Channel Parameters

% 1. Doppler Spread

FC=3.5; % carrier freq. in GHz

v=50; % speed in km/h

FD=v*FC; % Dopplerfreq in Hz

%2. Time Spread

tau= [0, 0.1, 0.4]*1e-6; % time delays in seconds

P= [0, -2, -4]; % attenuations in dB

% Additive Noise

SNRdB=0:30; % dB

Adaptive modulation using Simulink is shown in figure 4.3. It uses case loop to switch between

different methods. This switching technique can be improved by using fuzzy logic or genetic

algorithm which will be the future scope of this result.

5.5 Simulation result

Figure below shows the different Constellation diagram for varying SNR. A constellation diagram is

a representation of a signal modulated by a digital modulation scheme. It displays the signal as a

two-dimensional scatter diagram in the complex plane at symbol sampling instants. In a more

abstract sense, it represents the possible symbols that may be selected by a given modulation scheme

as points in the complex plane. Measured constellation diagrams can be used to recognize the type of

interference and distortion in a signal. Result Shows the Adaptive Modulation of 4 cases out of six

Adaptive OFDM

39

different Modulations. It is observed that as SNR increases Throughput (Mbps) increases and also

the sharpness of Constellation diagram increased it indicates BER is reduced.

Figure 5.3a shows the constellation diagram for BPSK with SNR 2dB here only two symbols are

used for representation of message signal, negative axis is for logic symbol zero and positive for

logic symbol 1. So BPSK has more area for the detector to demodulate the messages.

In figure 5.3b QPSK divides constellation graph to four parts because it is having 4 symbol

messages hence area present for demodulation is less than that of BPSK but Throughput is more

compare to BPSK but must be operated in higher SNR or else more symbol will end up with error.

Similarly 64QAM in figure 5.3 c and d message are represented by 16 symbol so constellation are

divided into 16 parts operates in higher SNR and gives good throughput because 64 QAM transmits

six bits per symbol compare to 2 bits in BPSK.

.

Figure 5.3(a)WiMax BPSK Constellation

diagram for SNR 2dB

Figure5.3(b)WiMax QPSK CD SNR 15dB

and Rate ID 3Mbps

Figure5.3(c)WiMax64QAM3/4Constellatio

nDiagramSNR 30dB Rate ID 4Mbps

Figure5.3(d)WiMax64 QAM

2/3ConstellationDiagramSNR 35dB

Rate ID 5Mbps

Adaptive OFDM

40

5.5.1 Power spectral density

Power spectral density is the very important term represents the operation bandwidth in

communication system. The spectrum of a time-series or signal is a positive real function of a

frequency variable associated with a stationarystochastic process, or a deterministic function of

time, which has dimensions of power per hertz (Hz), or energy per hertz.

Figure 5.4 shows the energy spectrum at the transmitter end it shows that transmitted power

spectrum is -60dB and bandwidth is 1.5GHz and figure 5.5 shows the PSD for different SNR

initially for low SNR received power is around -80dB and also it is very difficult to identify the

BW of the spectrum as SNR is increased the power received improves and so bandwidth. See

figure 5.5e the received power is -80dB and BW match with that of transmitted end.

Figure 5.4 PSD at transmitter end

Figure 5.5(a) PSD Reciver side 5dB SNR

Figure 5.5(b) PSD Reciver side 10dB

SNR

Figure 5.5(c) PSD Receiver side 15dB

SNR

Adaptive OFDM

41

Figure 5.5(d) PSD Receiver side 20dB

SNR

Figure 5.5(e) PSD Receiver side 30dB SNR

5.5.2 BER Performance

Bit Error Ratio (BER) is the number of bit errors divided by the total number of transferred bits

during a studied time interval. BER is a unitless performance measure, Figure 5.6 shows the

BER vs. SNR plot for different modulation scheme. There is the gradual decrease in BER in case

of Adaptive modulation as shown in figure 5.6b. The performance can be further improved by

speeding up the switching methods and using adaptive MIMO antennas.

Figure 5.6(a) BER of different modulation scheme without adaptation

Adaptive OFDM

42

Figure 5.6(b) BER of Adaptive and non-adaptive modulation

Figure 5.6(c) BER performance of Adaptive OFDM with and without coding

Figure 5.6(c) shows the importance of forward error correction coding and interleaving. Blue color dot

shows the non-coded adaptive OFDM it performance poorly right from 3dB to 30dB. In Adaptive

OFDM modulation the performance varies with respect to SNR but overall BER is always less than that

of non-Adaptive.

Adaptive OFDM

43

Table 3, 4 and 5 are the observation of the result obtained in figure 5.2 and 5.6. The table 4 and table 5

provide the important of Adaptive modulation and coding in wireless communication. The performance

of the system can be improved by adaptation of the technology for different environmental conditions.

Table 3: Result Comparison from Figure 5.2

Modulation Best BER performance at SNR

BPSK For all SNR

QPSK 10dB

16QAM 18dB

64QAM 23dB

Table 4 : Adaptive and Non Adaptive Modulation with coding

Modulation with coding Best BER performance at SNR

Non adaptive Poor performance

Adaptive Good for 10dB to 25dB

Table 5 : Adaptive Modulation with coding and without coding

Modulation Best BER performance at SNR

Adaptive without coding 0 to 5dB

Adaptive with coding Good for 10dB to 25dB

Adaptive OFDM

44

Chapter 6

CONCLUSION AND FUTURE WORK

This report is devoted to the study of the WiMax system. More specifically, it examines the

implementation of a WiMax simulator, targeted to the 256-point FFT OFDM PHY layer, built with

Matlab Simulink. With this purpose, the different parts of the simulator have been analyzed.

From table 5 it is found that Adaptation of digital modulation and coding gives the good result of BER

with respect to non-adaptive methods, figure 5.3 explains how Throughput can be increased by high

SNR and hence increases spectral efficiency. Because of the use of OFDM for modulation mobility is

also increased. By working under this project it is found that Adaptive modulation and coding provides

the best QOS in terms of BER and throughput. This project implements the physical layer of IEEE

802.16e which was designed by IEEE standards for mobile WiMax in 2004, literature gives the hint of

adaptation of antennas MIMO and also in power allocation.

There is lots of scope in future for improving the speed of adaptive algorithm by using

Neural network,

Fuzzy logic

Genetic algorithm for these modulation coding

Using smart antennas and power allocation algorithm like beam-forming can improve the

existing systems.

Adaptive OFDM

45

REFERENCE

[1]. M.A.Mohamed, F.W.Zaki, R.H.Mosbeh“Simulation of WiMAX Physical Layer: IEEE

802.16e”IJCSNS,VOL.10.NO.11,November 2010.

[2]. J.Faezah1, and K.Sabira2 “Adaptive Modulation for OFDM Systems” International Journal of

Communication Networks and Information Security (IJCNIS) Vol. 1, No. 2, August 2009.

[3]. K.SeshadriSastry, “SNR Estimation for QAM signals Using Fuzzy Logic Interface”.

International Journal of Engineering Science and Technology Vol. 2(6), 2010, 2384-2392

[4]. Abdul Rehman1 “Study of WiMax Physical Layer under Adaptive modulation” International

Journal of Scientific Research Engineering & Technology (IJSRET) Volume 1 Issue 5 pp. 005-

011 August 2012.

[5]. Atta-ur-rahman, IjazMansoorQureshi, M. ZeeshanMuzaffar“Adaptive Coding and Modulation

for OFDM Systems using Product Codes and Fuzzy Rule Base System” International Journal of

Computer Applications (0975 – 8887) Volume 35– No.4, December 2011.

[6]. Thomas Hunziker, “Adaptive OFDM Transmission for Broadband Fixed Wireless Access

System” Dirk Dahlhaus Communication Technology Laboratory, Swiss Federal Institute of

Technology (ETH) ETH Zentrum, Sternwartstr. 7, 8092 Zurich, Switzerland

[7]. Seshadri K. Sastry“Adaptive Modulation for OFDM System Using Fuzzy Logic

Interface”.EIILMuniversity,Sikkim 2007.

[8]. Chia-Chang Hu and Shih-Chang Lee, Hu and Lee “Adaptive low-rank channel estimation for

multiband OFDM ultra-widebandcommunications”EURASIP Journal on Advances in Signal

Processing 2011, 2011:64.

Adaptive OFDM

46

[9]. Ki-Hwan Eom, Kyo-Hwan Hyun, Kyung-Kwon Jung“The Performance Improvement of BASK

System for Giga-Bit MODEM Using the Fuzzy”, Int. J. Communications, Network and System

Sciences, 2010, 3, 441-445.

[10]. Vineeta, Asst. Prof. J. K. Thathagar, Asst. Prof. V. K. Patel, Prof. A. R. Charania,“Performance

Evaluation of WiMAX System OFDM Physical Layer for Flat-Fading and Multipath-Fading

Channel Environments”International Journal of Engineering Research and Applications (IJERA)

Vol. 2, Issue 3, May-Jun 2012, pp.1426-1432.

[11]. Mohammed AboudKadhim and Widad Ismail “Implementation of WIMAX IEEE 802.16e

Baseband Transceiver on Multi-Core SoftwareDefined Radio Platform”, International Journal of

Computer Theory and Engineering, Vol. 2, No. 5, October, 2010 1793-8201.

[12]. K.Harikrishna, T. Rama Rao, Vladimir A. Labay“FPGA Implementation of FFT Algorithm for

IEEE 802.16e”, International Journal of Computer Theory and Engineering, Vol. 3, No. 2, April

2011 ISSN: 1793-8201.

[13]. “WIMAX, New Developments”Textbook by DrMsUpena D Dalal and Dr Y P Kosta, published

December 2009 India.

[14]. Anita Garhwal, ParthaPratim Bhattacharya “Fuzzy Logic Based Channel Estimation and

Performance Analysis of WiMAX Systems”Journal of Emerging Trends in Computing and

Information Sciences, VOL. 3, NO. 3, March 2012.

[15]. Pengfei Xia, Shengli Zhou, and Georgios B. Giannakis“Adaptive MIMO-OFDM Based on

Partial Channel State Information”,IEEE TRANSACTIONS ON SIGNAL PROCESSING,

VOL. 52, NO. 1, JANUARY 2004.

Adaptive OFDM

47

[16]. Andreas Zalonis, Natalia Miliou, IoannisDagres, Andreas Polydoros, and Hanna

Bogucka“Trends in Adaptive Modulation and Coding”, ADVANCES IN ELECTRONICS AND

TELECOMMUNICATIONS, VOL. 1, NO. 1, APRIL 2010.

[17]. Hisham A. Mahmoud, TevfikY¨ucek and H¨useyinArslan“OFDM for Cognitive Radio: Merits

and Challenges” Department of Electrical Engineering, University of South Florida 4202 E.

Fowler Avenue, ENB-118, Tampa, FL, 33620.

[18]. “SIMULATION OF WIRELESS COMMUNICATION SYSTEM USING OFDM PRINCIPLE”

Master Thesis submitted by D Jagdeesh NIT Rourkela 2007.

[19]. Amalia Roca “Implementation of a WiMAX simulator in Simulink” Thesis work Vienna 2007.