{Đồ án} nghiên cứu công nghệ OFDM và ứng dụng

{Đồ Án} Nghiên Cứu Công Nghệ OFDM và Ứng Dụng, {Đồ Án} Nghiên Cứu Công Nghệ OFDM và Ứng Dụng,{Đồ Án} Nghiên Cứu Công Nghệ OFDM và Ứng Dụng{Đồ Án} Nghiên Cứu Công Nghệ OFDM và Ứng Dụng{Đồ Án} Nghiên Cứu Công Nghệ OFDM và Ứng Dụng{Đồ Án} Nghiên Cứu Công Nghệ OFDM và Ứng Dụng

LIST OF ACRONYMS

AWGN Additive White Gaussian Noise

CSI Channel State Information

FDM Frequency Division Multiplexing

ICI Inter-Carrier Interference

ISI Inter-Symbol Interference

M-PSK M-ary Phase-Shift Keying

OFDM Orthogonal Frequency Division Multiplexing

PDF Probability Density Function

SISO Single Input Single Output

TABLE OF CONTENT

LIST OF ACRONYMS i

TABLE OF CONTENT ii

ACKNOWLEDGEMENTS iv

LIST OF FIGURES v

LIST OF TABLES vii

ABSTRACT 1

CHAPTER 1 3

MOBILE RADIO CHANNEL CHARACTERISTICS 3

1.1 Introduction 3

1.2 AWGN 4

1.3 Path loss 5

1.4 Delay spread 7

1.5 Doppler shift 7

1.6 Fading 9

1.6.1 Flat fading versus frequency selective fading 10

1.6.2 Slow fading versus fast fading 11

1.7 Conclusion 12

CHAPTER 2 13

DIVERSITY TECHNIQUES 13

2.1 Introduction 13

2.2 Diversity 13

2.2.1 Frequency diversity 13

2.2.2 Time diversity 14

2.2.3 Space diversity 14

2.3 Diversity combining methods 15

2.3.1 Selection combining 15

2.3.2 Switched Combining 16

2.3.3 Maximal ratio combining method 17

2.3.4 Equal Gain Combining 18

2.4 Transmit diversity 19

2.4.1 Maximal ratio transmission 21

2.4.2 Delay transmit diversity 22

2.4.3 Alamouti Space-Time Coding 23

2.5 Conclusion 28

CHAPTER 3 29

ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING 29

3.1 Introduction 29

3.2 Block diagram of OFDM 30

3.3 Signal OFDM 33

3.4 Orthogonality condition 33

3.5 ISI in OFDM system 35

3.6 ICI in OFDM system 39

3.7 PAPR in OFDM system 42

3.7.1 Clipping 44

3.7.2 Selected mapping 45

3.7.3 Partial Transmit Sequences 46

3.8 Conclusion 47

CHAPTER 4 48

COMBINED OFDM AND TRANSMIT DIVERSITY SYSTEMS 48

4.1 Introduction 48

4.2 OFDM combined with transmitter diversity 49

4.2.1 Delay approach 49

4.2.2 Permutation approach 50

4.2.3 Space-time coding approach 52

4.2.3.1 System description 52

4.2.3.2 Maximum likelihood detection 55

4.3 Conclusion 63

CONCLUSION 64

APPENDIX 65

REFERENCES 73

ACKNOWLEDGEMENTS

First of all, I would sincerely like to thank my supervisor, Doctor Tran Xuan Nam for many discussion hours, valuable advice, and his continuous guidance

I would also like to acknowledgement Associate Professor Nguyen Quoc Binh for many useful and interesting information about wireless communication Thanks to lecturers in Military Technical Academy providing

me with full knowledge during 5 years

Most of all, I am especially grateful to my parents for their sacrifices and extreme love to help me complete this thesis

LIST OF FIGURES

Figure 1.1: An example of multi-path propagation in a wireless channel 3

Figure 1.2: AWGN noise characteristics 4

Figure 1.3: An illustration of power density on sphere 6

Figure 1.4: Delay spread 7

Figure 1.5: Doppler shift 8

Figure 1.6: An illustration of multi-path signal 9

Figure 1.7: Frequency selective fading and flat fading 10

Figure 1.8: An illustration of slow fading and fast fading 12

Figure 2.1: Frequency diversity 13

Figure 2.2: Time diversity 14

Figure 2.3: Space diversity 15

Figure 2.4: Selection combining 16

Figure 2.5: Switched combining 17

Figure 2.6: Maximal combining 18

Figure 2.7: Equal Gain Combining 19

Figure 2.8: Transmit diversity systems 20

Figure 2.9: Maximal ratio transmission 22

Figure 2.10: Delay transmit diversity 22

Figure 2.11: Alamouti Space-Time Coding 23

Figure 2.12: Receiver for Alamouti scheme 25

Figure 2.13: BER performance of the Alamouti systems 27

Figure 3.1: Block diagram of a typical OFDM system 31

Figure 3.2 Performance of OFDM with M-PSK modulation 32

Figure 3.3: Basic multi-carrier transmission system 33

Figure 3.4: Illustration of OFDM signals in time and frequency domain 34

Figure 3.5: Comparison of single carrier modulation and OFDM 36

Figure 3.6: OFDM symbol without cyclic prefix 37

Figure 3.7: OFDM symbol with cyclic prefix 37

Figure 3.8: OFDM-QPSK with Delay spread 38

Figure 3.9: Transmitted signal inserted guard interval 39

Figure 3.10: OFDM signal with cyclic prefix 40

Figure 3.11: Frequency offset error 41

Figure 3.12: Time error 41

Figure 3.13: PAPR in OFDM system 42

Figure 3.14: IBO and OBO 43

Figure 3.15: An example illustrates the clipped signal 44

Figure 3.16: Transmitter with clipping and filtering 45

Figure 3.17: Selected mapping 45

Figure 3.18: Partial Transmit Sequences 46

Figure 4.1: Delay transmit diversity 49

Figure 4.2: Permutation approach 51

Figure 4.3: Space time coding approach 52

Figure 4.6: STBC-OFDM over selective Rayleigh fading channel 59

Figure 4.7: The original image 59

Figure 4.8: Received images over flat fading channel using STBC-OFDM .61

Figure 4.9: Received images over flat and frequency selective fading channel

63

LIST OF TABLES

Table 2.1: Alamouti parameters with BPSK constellation 27 Table 3.1: OFDM parameters for simulation 32 Table 3.2: OFDM parameters for simulation in channel with delay spread .38 Table 4.1: Simulation parameters of STBC-OFDM system 57

Due to increased demand of human, multimedia services with high ratetransmission and quality are required Wired communication is an approachwhich brings good performance, high rate and reliability But it only supportsfixed access services In contrast, wireless communication is very attractivedue to its mobility, portability, and accessibility Fluctuations of radiochannels in wireless communication such as the fading, the shadowing, thepath loss phenomenon cause difficulties into transmission One effectiveapproach has been proposed to over this situation, is to combine OFDM andtransmit diversity techniques This approach not only provides high ratetransmission but also improves the overall system performance, significantlydue to achieving both path and space diversities

For this reason, I have chosen research topic “Combined OFDM and transmitdiversity for wireless communication” for my graduation thesis

This thesis consists of 4 chapters:

Chapter 1: Mobile radio channel characteristics

This chapter introduces problems in transmitting signal over radio channel.Main properties of radio channels such as effect of AWGN, path loss, delayspread, Doppler shift, fading phenomenon are described

Chapter 2: Diversity techniques

This chapter introduces an overview about diversity techniques The mainfocuses is about transmit diversity techniques Several approaches introducedare maximal ratio transmission, delay transmit diversity, and Alamouti space-time coding

Chapter 3: Orthogonal frequency division multiplexing

This chapter introduces principles of multi-carrier transmission, OFDM andadvantages and disadvantages of OFDM

Chapter 4: Combined OFDM and transmit diversity systems

This chapter introduces a combined approach of OFDM and transmitdiversity techniques to obtain both path and transmit diversities Matlabsimulation is used to evaluate efficiency of the combined STBC-OFDMapproach

CHAPTER 1 MOBILE RADIO CHANNEL CHARACTERISTICS

1.1 Introduction

In an ideal radio channel, received signal consists of only a single directpath so it can be recovered perfectly at the receiver In real channel, wirelesscommunication channel suffers from many impairments such as the thermalnoise, often modeled as Additive White Gaussian Noise (AWGN), path loss

in power, shadowing effects due to the presence of fixed obstacles in theradio path, fading due to the effect of multi-path propagation, and Dopplereffect due to movement of mobile units Consequently, signal copies undergodifferent attenuations, distortions, delays and phase shifts An example ofmulti-path propagation in a wireless channel is illustrated in Figure 1.1 Due

to these problems, the overall system performance is degraded significantly

Figure 1.1: An example of multi-path propagation in a wireless channel

1.2 AWGN

In practice, transmission is always effected by noise The appearance ofnoise reduces ability in detecting exact transmitted signal, so transmissionefficiency is reduced, too Noise is resulted from many different sources, such

as thermal noise, noise of electronic devices, man-made noise and othersources Superposition of many independent processes, noise can be modeled

as a Gaussian distributed random process with white spectral density Thepopular noise model in communication system is Additive White GaussianNoise This is a very good model for the physical reality as long as thethermal noise at the receiver Nevertheless, because of its simplicity, it is alsoused to model man-made noise or multi-user interference

The noise w t( ) is an additive random disturbance of the useful signal s t( ),therefore, the receive signal is given by

r t s t w t

The noise is white, in that, it has constant power spectral density (psd) overall range frequency The one-sided psd is usually denoted by N , so 0 N0/ 2 isthe two-sided psd Power spectral density is illustrated in Figure 1.2 (a) Weexpress power density function of white noise, with a sample functiondenoted by w t( ) as

The noise is a zero mean Gaussian random process This means that theoutput of every noise measurement is a zero mean Gaussian randomvariable that does not depend on the time instant when the measurement isdone.

Autocorrelation function of white noise which is described in Figure 1.2 (b),

is the inverse Fourier transform of the power spectral density given by

power density on sphere at a distance d from the source is related with

transmitted power as

2 2

4

t P

d



where p d( ) is power density at distance d, P is power density of the t

isotropic radiator, and d is the distance between source and viewed point.

Since 4 d 2is the area of sphere, the power extracted at receiver antennawhich is described in Figure 1.3, can be written as

Figure 1.3: An illustration of power density on sphere

Power density at the receiver when the transmitter antenna has gain G is t

substituting this formula for A into equation (1.6), we can express the r

receiver signal power in equivalent form

1.4 Delay spread

When signal is transmitted from one point to another, each sinusoidalcomponent of the signals arrive at the receiver with a phase and amplitudedifferent from other sinusoidal components This can be caused by differencepath length of signals The reflected signals arrive at a later time than thedirect signal, resulting in a spread of the received signals

Figure 1.4: Delay spread

Delay spread phenomenon is illustrated in Figure 1.4 Delay spread is thetime spread between arrival of the first and last signal If data is transmitted at

a high rate, then each signal spreads in time causing adjacent signalsoverlapped when they are transmitted through the air This phenomenon iscalled inter-symbol interference (ISI) and is a major concern for transmissionchannel with a limited bandwidth

1.5 Doppler shift

Due to the relative motion between transmitter and receiver, each

multi-path wave is subjected to a shift in frequency The frequency shift of receivedsignal caused by the relative motion is called the Doppler shift It isproportional to the speed of mobile unit Let us assume that, we have a signalwith a frequency f transmitted between the transmitter and the receiver and c

a mobile receiver moving with a velocity v Also, we define θ as the angle

between the motion direction of the mobile unit and the arrival direction ofthe signal In this case, the frequency change of the signal is known as theDoppler shift and denoted by f , is given by d

of light, f is frequency carrier, and θ is angle between the motion direction c

of the mobile and the arrival direction of the signal Since different pathsarrive from different angles, a variety of Doppler shifts corresponding todifferent multi-path signals are observed at the receiver The relativemotion between the transmitter and the receiver results in random frequencymodulation due to different Doppler shifts on each of the multi-pathcomponents

Figure 1.5: Doppler shift

The Doppler shift in a multi-path propagation environment spreads thebandwidth of the multi-path waves within the range of f c  f m to f c  f m as

Figure 1.6: An illustration of multi-path signal

Even when line of sight exists, fading still occurs due to reflections of groundand surround objects Incoming waves arrive from many different directionswith different propagation These signals are combined at the receiverantenna Consequently, signals can vary widely in amplitude and phase Anillustration of multi-path signal is expressed in Figure 1.6 Base on channelparameters and characteristics of signal to be transmitted, fading channels can

be classified as follows.

1.6.1 Flat fading versus frequency selective fading

Frequency selectivity is also an important characteristic of fadingchannels If all the spectral components of the transmitted signal are affected

in a similar manner, the fading is said to be nonselective fading or flat fading.This is case for narrowband systems, in which the transmitted signalbandwidth is much smaller than coherence bandwidth B This bandwidth c

measures frequency range over which fading process is correlated Inaddition the coherence bandwidth is related to the maximum delayspread max by

where max is maximum delay spread, and B is coherence bandwidth c

For frequency selective fading, spectrum of transmitted signal has abandwidth greater than coherence bandwidth B of channel c

Figure 1.7: Frequency selective fading and flat fading

Frequency selective fading is caused by multi-path delays Differentfrequency components will experience different phase shilfs and amplitudegains along different paths As path delays become large, close frequenciescan experience significantly different phase shifts Under this condition,channel introduces amplitude and phase distortion into signal Frequencyselective fading applies to wideband systems in which transmitted bandwidth

is bigger than coherence bandwidth An illustration of frequency selectivefading and flat fading is described in Figure 1.7

1.6.2 Slow fading versus fast fading

The distinction between slow and fast fading is important for mathematicalmodeling of fading channels and for the performance evaluation ofcommunication systems operating over these channels This notion is related

to the coherence time c of the channel, which measures the period of timeover which the fading process is correlated The coherence time is also related

to the channel Doppler spread f by m T s

1

c m f

 

where f is maximum Doppler spread, m c is the coherence time

The fading is said to be slow if the symbol time duration T is smaller than s

the channel’s coherence time c, slow fading often modeled as time invariantchannels over a number of symbol intervals Moreover, channel parameterscan be estimated with different estimation techniques Otherwise it isconsidered to be fast In general, it is difficult to estimate channel parameters

in a fast fading channel Figure 1.8 illustrates frequency selective fading andflat fading phenomenon

Figure 1.8: An illustration of slow fading and fast fading

1.7 Conclusion

Understanding of these effects on the signal is very important because theperformance of a radio system depends on the radio channel characteristics.From the basic knowledge about the channel, we will introduce differentapproaches to reduce the effect of channel characteristics and improve theoverall system performance

CHAPTER 2 DIVERSITY TECHNIQUES

2.1 Introduction

In wireless mobile communications, diversity techniques are widelyused to reduce the effects of multi-path fading and improve the reliability oftransmission without increasing the transmitted power The main idea behind

“diversity” is to provide different replicas of the transmitted signal to thereceiver If these different replicas fade independently then probability of allsignal copies which experience deep fade is small There will be only severalsignal copies undergo deep fade, while others experience less attenuation.Using diversity techniques help to reduce severity of fading, and improvereliability of transmission There are several kind of diversity techniques,which are commonly employed in wireless communication system

As illustrated in Figure 2.1, at the receiver, the independent copies arecombined to get a good decision Frequency diversity is used to combatfrequency selective fading.

2.2.2 Time diversity

Another approach to achieve diversity, which is illustrated in Figure 2.2, is

transmitting the desired signal in M different time slots

Figure 2.2: Time diversity

The intervals between transmissions of same symbol should be at least thecoherence time so that different copies of same symbol undergo independent

fading We notice that sending same symbol M times is applying the ( ,1)M

repetition code Error control coding together with interleaving can be aneffective way to combat time selecting fading

2.2.3 Space diversity

Figure 2.3 illustrates space diversity method This method has been apopular technique in wireless communications Space diversity is also calledantenna diversity It is typically implemented by using multiple antennas orantenna arrays arranged together in space for transmission and reception Themultiple antennas are separated physically by a proper distance so that theindividual signals are uncorrelated Typically, a separation of a fewwavelengths is enough to obtain uncorrelated signals Space diversity can beemployed to combat both frequency selective fading and time selectivefading

Figure 2.3: Space diversity 2.3 Diversity combining methods

The idea of receive diversity is to combine copies of transmitted signalwhich undergo independent fading In general, the performance ofcommunication systems with diversity techniques depends on how multiplesignal replicas are combined at the receiver to increase the overall receivedSNR Different diversity schemes require different diversity combiningmethods Here, we reviewed some common diversity combining methods For

a slow flat fading channel, the received signal of branch i is given by

the AWGN of branch i.

M replicas of the transmitted signal in M branches are

r t r t1( ), ( ) 2 r M1( )t 



r 2.3.1 Selection combining

Selection combining is a simple diversity combining method As shown in

Figure 2.4 Consider a receive diversity system with n receive antennas In R

this method, the signal with the strongest signal-to-noise ratio (SNR) atevery symbol interval is selected as the output In practically, the signalwith the highest sum of the signal and noise power (S N ) is used, since it isdifficult to measure the SNR

Figure 2.4: Selection combining 2.3.2 Switched Combining

In a switched combining diversity system, the receiver scans all thediversity branches and selects a particular branch with the SNR above acertain predetermined threshold This signal is selected as the output,until its SNR drops below the threshold When this happens, thereceiver starts scanning again and switches to another branch This scheme isalso called scanning diversity A scheme of switched combining is shown inFigure 2.5

Figure 2.5: Switched combining 2.3.3 Maximal ratio combining method

Maximum ratio combining is a linear combining method In a generallinear combining process, various signal inputs are individually weighted andadded together to get an output signal

The output signal is a linear combination of weighted received replicas It isgiven by





where r is the received signal at receive antenna i, and i i is the weighting

factor for receive antenna i

In maximum ratio combining, the weighting factor of each receive antenna

is chosen to be in proportion to its own signal voltage to noise power ratio.Let A and i i be the amplitude and phase of the received signal r , i

respectively Assuming that each receive antenna has the same average noisepower, the weighting factor i can be represented as

output SNR It is shown that the maximum output SNR is equal to the sum ofthe instantaneous SNRs of the individual signals.

In scheme as shown in Figure 2.6, each individual signal must be co-phased,weighted with its corresponding amplitude and then summed This schemerequires the knowledge of channel fading amplitude and signal phases So, itcan be used in conjunction with coherent detection, but it is not practical fornon-coherent detection

Figure 2.6: Maximal combining 2.3.4 Equal Gain Combining

Equal gain combining, which is illustrated in Figure 2.7, is a sub-optimalbut simple linear combining method It does not require estimation of thefading amplitude for each individual branch Instead, the receiver sets theamplitudes of the weighting factors to be unity

Figure 2.7: Equal Gain Combining 2.4 Transmit diversity

After considering diversity combining methods, we recognize that,diversity is an useful approach to reduce the effect of fading channels.Receiver diversity increases SNR ratio But in some case, deploying it at thereceiver meets many difficulties Consider an example, the situation of mobileradio Firstly, the mobile unit is required as small as possible and it isdifficult to mount two or more antennas at the receiver with a distance largerthan half the wavelength Secondly, multiple receiver antennas requiremultiple RF down converters, as a result, more processing power and the life-span of mobile batteries degrades In this case, we often use transmit diversity

to improve the system performance Multiple antennas are used at thebase station It is easy to install multiple transmit antennas in the basestation and provide the extra power for multiple transmissions Inpractical, a base station often servers hundreds to thousands of mobile unit.Therefore it is more economical to add equipment to base stations rather thanthe mobile unit For this reason, transmit diversity schemes are veryattractive But deploying transmit diversity is more complexity than receiverdiversity For the following reasons, firstly, the transmitted signals must becombined together before arriving the receiver This requires some additional

signal processing at both transmitter and receiver to separate the true signals.Secondly, unlike receiver diversity, transmit diversity can not usually estimatechannel status The transmitter has not instantaneous information about thechannel unless the information is fed back from the receiver to the transmitter.Transmit diversity can be divided into two types The first type has feedback,the other without feedback.

For transmit diversity systems without feedback, the transmitter does not haveany information about the channel In this case, the receiver may estimate thechannel and use the channel state information (CSI) for decoding

For transmit diversity systems with feedback, the receiver sends someinformation about the channel back to the transmitter through a feedbackchannel The transmitter can use this information to control some parameter.The systems with feedback require more signal processing, resulting in moredelays Further more, the imperfect channel estimation between the previouschannel state and current channel condition will decrease the received signalSNR and affect the system performance

Figure 2.8: Transmit diversity systems

feedback

The block diagrams of systems with feedback and without feedback areillustrated in Figure 2.8 (a), (b) respectively Transmit diversities are created

by using many antennas and combining with a suitable signal processingapproach Some transmit diversity approaches were proposed such asMaximal Ratio Transmit (MRT), Delay Transmit Diversity and Space TimeBlock Coding (STBC)

2.4.1 Maximal ratio transmission

This approach is similar to the receiver diversity with maximal ratiocombining as mentioned in the previous sections At the transmitter, thesignal over branches are multiplied by individual factors

1

1

h N

*

1

h N

2

1

h N

*

1

h N



1

N

h N

*

1

N

h N

To find w , it is necessary to know channel state information This can be n

known by feedback information from the receiver The scheme of themaximal ratio transmission is illustrated in Figure 2.9

2.4.2 Delay transmit diversity

With delay diversity as shown in Figure 2.10, the base station transmitscopies of the same signal from different transmit antennas The transmitpower is divided evenly among antennas At the receiver, the delays of thesecond up to the n -th transmit antennas can be seen as multi-path distortions T

with signal from the first antenna

Figure 2.10: Delay transmit diversity

Delay diversity can be seen clearly at the receiver, it similar to a longer delayspread The multi-path distortions can be resolved or exploited at thereceiver by using a maximum likelihood sequence estimator (MLSE) or aminimum mean square error (MMSE) equalizer to obtain a multi-pathdiversity gain This approach can be used directly to achieve path gain withmulti-path channels With systems have two antennas at the transmitter, toobtain full diversity benefit from the delay diversity, the transmit delaybetween two antennas must larger than delay channel spread Due to its

simplicity, delay diversity can be deployed without many modifications toexisting systems and standards

2.4.3 Alamouti Space-Time Coding

The Alamouti scheme which is illustrated in Figure 2.11, is historically thefirst space-time block code to provide full transmit diversity for systems withtwo transmit antennas

Let us assume that an M-ary modulation scheme is used In the Alamouti space-time encoder, each group of m information bits is firstly

modulated, where mlog2M Then, the encoder takes a block of twomodulated symbols x and 1 x in each encoding operation and maps them to2

the transmit antennas according to a code matrix given by

Figure 2.11: Alamouti Space-Time Coding

The encoder outputs are transmitted in two consecutive transmissionperiods from two transmit antennas During the first transmission period,two signals x and 1 x are transmitted simultaneously from two antennas In2

the second transmission period, signal *

x are the complex conjugates of

1

x , x , respectively.2

It is easy to understand that the encoding is done in both the space and timedomain Let us denote the transmit sequence from antenna one and two by x1

x x x x  x x 0Let us assume that one receive antenna is used at the receiver Thefading channel coefficients from the first and second transmit antennas to the

receive antenna at time t are denoted by h t and 1( ) h t , respectively.2( )Assuming that the fading coefficients are constant across two consecutivesymbol transmission periods, they can be expressed as follows

At the receive antenna, the received signals over two consecutive symbol

periods, denoted by r 1 and r 2 for time t and t + T, respectively, can be

where n 1 and n 2 are independent complex variables with zero mean and power

spectral density N 0/2 per dimension, representing additive white Gaussian

noise samples at time t and t + T , respectively For a coherent detection

scheme, the channel fading coefficients are assumed to know at the receiver,

h1 and h 2

Figure 2.12: Receiver for Alamouti scheme

The decoder will use them as the channel state information (CSI) Assumingthat all the signals in the modulation constellation are equiprobable, amaximum likelihood decoder chooses a pair of signals ( , )x x from theˆ ˆ1 2

signal modulation constellation to minimize the distance

over all possible values of ˆx and 1 ˆx Substituting (2.12) into (2.13), the2

maximal likelihood decoding can be represented as

where C is the set of all possible modulated symbol pairs ( , )x x , ˆ ˆ1 2 x and 1 x2

are two decision statistics constructed by combining the received signals withchannel state information The decision statistics are given by

For a given channel realization h 1 and h 2 , the decision statistics x , i i 1,2, is

only a function of x i , i = 1, 2 Thus, the maximum likelihood decoding rule (2.14) can be separated into two independent decoding rules for x 1 and x 2,given by

Simulation model of Alamouti system is expressed in table 2.1 following

Channel configuration Theory, SISO, Alamouti 2-1, Alamouti 2-2

Table 2.1: Alamouti parameters with BPSK constellation

Figure 2.13: BER performance of the Alamouti systems

Figure 2.13 shows bit error rate versus Eb/No for a system using BPSKmodulation As can be seen, performance of Alamouti 2x1 MISO system ismuch better than SISO system At Eb/No of 14 dB, the Alamouti codeprovides BER about 10-3, while the SISO system only provides BER about 10-

2 It is clear that, by using Alamouti code, system performance is improvedsignificantly With Alamouti systems, when we increases number of receiveantennas form 1 to 2, the Alamouti 2x2 MIMO system provides better quality

than the Alamouti 2x1 MISO system At BER of 10-3, the Alamouti 2x2MIMO system provides more than 7 dB improvement This means that, toachieve the same BER of 10-3, the transmitted power of the Alamouti 2x1MISO system needs 5 times higher than that of the Alamouti 2x2 MIMOsystem.

2.5 Conclusion

As analyzed above, transmit diversity techniques are an useful method toreduce the effect of fading According to domain, a special diversitytechnique is introduced or combined with another diversity techniques to getthe required system performance

CHAPTER 3 ORTHOGONAL FREQUENCY DIVISION

MULTIPLEXING

3.1 Introduction

High rate data transmission is desired in many applications However, thesymbol duration reduces together with the increasing of data rate, so thesystems using single carrier modulation at high rate are suffered inter-symbolinterference (ISI) OFDM is a special technology to mitigate ISI OFDM is aform of multi-carrier modulation (MCM) The principle of multi-carriermodulation is to transmit data by dividing input stream into several streams.Each stream has a much lower rate These lower rate streams are then used to

modulate a separate single carrier These carriers are referred to as

sub-carriers (SC).

The reasons to use OFDM are as follows

Firstly, OFDM is an approach which has high spectrum efficiency Since

OFDM sub-carriers have overlapped spectrums, thus OFDM utilizes thespectrum better than the conventional frequency division multiplexing (FDM)with non-overlapped spectrum

Secondly, OFDM is robust against frequency selective fading Theoretically,

a high rate data stream has narrow symbol duration With OFDM, the high

rate stream is converted into K low rate sub-streams which are transmitted in

parallel Thereby, each sub-stream has bandwidth smaller compared with thechannel coherence bandwidth, that is, the individual SCs experience a flatfading channel, which allows for simple equalization at the receiver Effect ofinter-symbol interference (ISI) is thus reduced Moreover, OFDM signals are

inserted with a cyclic prefix to mitigate ISI If the cyclic prefix length isselected larger than the maximum delay spread of the channel max, thenOFDM can perfectly mitigate ISI.

Thirdly, OFDM is robust against deep fades and narrow-band interference.

When the data are transmitted serially, a deep fade is caused by fading mayextend to the duration of several symbols, causing a burst of transmissionerrors By using OFDM, the symbol duration is increased Thus, a fade mayaffect a fraction of OFDM symbols

In a single carrier system, a single fade or interferer can cause the entirelink to fail, but in OFDM system, only a small percentage of sub-carriers will

be affected Error correction coding can be used to correct the erroneous carriers

sub-Due to the above-mentioned attractive merits, OFDM modulation has beenchosen for many applications and standards

Digital broadcasting such as in the European digital audio broadcasting

(DAB) and the terrestrial digital video broadcasting (DVB)

Wire line communications such as the asynchronous digital subscriber line

(ADSL) and high-bit-rate digital subscriber line (HDSL) systems

Wireless communication such as high performance local area networks

(HIPERLAN), the IEEE 802.11.a/g wireless local area network (WLAN) andthe IEEE 802.16 wireless metropolitan area network (WMAN) It is alsobeing considered a candidate for the wireless mobile broadband access withinthe IEEE 802.20 standard and the fourth generation of mobilecommunication

3.2 Block diagram of OFDM

To get some basic knowledge about OFDM system, we can consider andanalyze operation of a typical OFDM system illustrated in Figure 3.1

Firstly, the input bit stream is fed to coding and interleaving block to reduceburst errors and ensure security of information The output stream is

converted into K sub-streams by serial-parallel converter, each sub-stream has

2

log M bits (M is modulation order) These bits are then mapped into

complex numbers, this mapping is depended on modulation type Aftermapping block, these streams are fed to IFFT block where they modulateindividual carriers

Figure 3.1: Block diagram of a typical OFDM system

A guard interval often called cyclic prefix, is inserted to mitigate ISI betweensuccessive OFDM frames Next, the carriers are parallel to serial converted tocreate an OFDM signal Finally, they are amplified and converted into highfrequency before transmitting over space The receiver basically does thereverse operation of the transmitter Firstly, the signal is converted into lowfrequency, and the guard interval is removed The FFT of each symbol istaken to find original transmitted spectrum Then the signal is recovered at thereceiver by a de-mapper on each sub-stream

In general, the mapper in OFDM system often uses M-PSK or M-QAM.Figure 3.2 shows the overall system performance of OFDM system over

Length of cyclic prefix 64

Table 3.1: OFDM parameters for simulation

Figure 3.2 Performance of OFDM with M-PSK modulation

From the above Figure, it is easy to recognize that, when increasing M results

in increasing BER, too This is because, when M increases, distance between

the signal points decreases, leading to fault detection Consequently, BER

increases Depend on demand, we will use PSK with different M If the

signals experience a low noise link or we have a high transmitter power, using16PSK will increase capacity If we only have a low transmitter power or thesignals experience a high noise link, using QPSK will be an useful approach

3.3 Signal OFDM

A data stream s k with transmission rate n   R1T is splitted into K

sub-streams Then each sub-stream is modulated with a different sub-carrier

k

f , where k 0,1 ,K  1 is the sub-carrier index The source symbol

duration of the serial data stream is T After the serial-to-parallel converter,

symbol duration on sub-carrier is

s

T K T

Figure 3.3: Basic multi-carrier transmission system

Frequency separation between adjacent sub-carriers is