router in wireless sensor network

8/13/2019 router in wireless sensor network

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Combined Multi-Input Multi-Output and

Network Coding for Wireless Networks

A Thesis

Submitted to the College of InformationEngineering

at Al-Nahrain University in Partial Fulfillments of

theRequirementsfor the Degree of Master of Science in

"Networks Engineering and Internet Technology"

By

Alza Abduljabbar Mahmood(B.Sc. 2006)

Ramadhan 1433

July 2012


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I

Abstract

The search for high data rates and throughput are the main demands of

modern wireless communication networks. Conventional methods relied on

the use of more bandwidth or larger modulation levels which have some

limitations. Thus more advanced techniques are introduced such as network

coding (NC) and multiple antenna system in the form of multiple inputs

multiple outputs (MIMO). Each of these usually improves the obtained data

rate and throughput. In this work, network coding is used in conjunction

with MIMO system in order to gain the advantages of both techniques.

A simple packet based network coding is introduced for network scenario

with MIMO system. Wireless networks with proposed combined techniques

(NC & MIMO) are modeled and simulated. The aim here is to use both

techniques in a way to improve the performance of the system. An

intermediate node known as coding or relay node is used to perform the

network coding within the whole network. All transmissions among the

nodes in the networks considered the use of MIMO technique.

The proposed structure is tested over different wireless channel models.

These models represent different channel conditions and environments. The

results of the tests have shown that combined MIMO-NC system improved

throughput over original MIMO system by about (33%) on the expense of

negligible loss in error performance at relatively high signal-to-noise power

ratios (SNRs). Similar improvement in BER is also achieved over the

original network coded system, which is equivalent to less than 1dB in

tolerance of the systems to additive white Gaussian noise under moderate

channel environments.


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UContents

Page No.

IAbstractIIContents

VList of Abbreviations

IXList of Symbols

Chapter One: Introduction

11.1 Motivation

21.2 Literature Review51.3 Aim of the Work

61.4 Outline of the Thesis

Chapter Two: Multi Input Multi Output system

72.1 Introduction

82.2 Main Benefits and Drawbacks of MIMO Technology

8 2.2.1 Benefits of MIMO Technology

10 2.2.2 Drawbacks of Multiple-Antenna Systems

102.3MIMO Diversity Techniques

142.4 MIMO Channel Model

152.5 Channel Capacity

172.6 Space Time Coding

17 2.6.1 Space Time Block Coding (STBC)

18 2.6.2 Space Time Trellis Code (STTC)

Chapter Three: Network Coding

203.1 Introduction

213.2 Types of Network Coding

213.3 Advantages of Network Coding


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22 3.3.1 Throughput

24 3.3.2 Minimizing Energy per Bit

26 3.3.3 Minimizing Delay

27 3.3.4 Security

273.4 Disadvantages of Network Coding

293.5 Linear Network Coding

29 3.5.1 Encoding

31 3.5.2 Decoding

323.6 Applications of Network Coding

32 3.6.1 Network Coding in the Internet33 3.6.2 Network Coding in Wireless Networks

Chapter Four: Model of MIMO_NC Suggested

System

364.1 Introduction

374.2 Transmission System Model

394.3 Network Coding Model42 4.3.1 Encoding Process

44 4.3.2 Decoding Process

Chapter Five: Simulation Tests and Results

455.1 Introduction

465.2 BER Performance Tests

46 5.2.1 AWGN Channel

46 5.2.2 Single Path Fading Channel

48 5.2.3 SUI-3Fading Channel

485.3 Throughput Performance Tests

48 5.3.1 AWGN Channel

49 5.3.2 Single Path Fading Channel

50 5.3.3 SUI-3 Fading Channel515.4 Assessments of Results


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Chapter Six: Conclusions and Future Work

536.1 Conclusions

546.2 Future Work55References


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V

List of Abbreviations

AF Amplify-and-Forward

ARQ Automatic Repeat reQuest

AWGN Additive White Gaussian Noise

BER Bit Error Rate

BC BroadCast

BS Base Station

BPSK Binary Phase Shift Keying

CA Collision Avoidance

CDMA Code Division Multiple Access

CSI Channel State Information

CSMA Carrier Sense Multiple Access

DF Decode-and-Forward

DNC Distributed Network Coding

EGC Equal Gain Combining

EM ElectroMagnetic

EP Equal Power

FIFO First In First Out

GF Galois Field

i.i.d. Independent and identical distributed

IM Instant Messaging


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VI

ISP Internet Service Provider

LNC Linear Network Coding

MAC Medium Access Control

MC Multiple Access

MIMO Multi Input Multi Output

MINEC MIMO NEtwork Coding

MISO Multi Input Single Output

MRC Maximum Ratio Combining

MRC-L Maximum Ratio Combining-Like

MU-MIMO Multi User-MIMO

NC Network Coding

NCR Network Coding Relaying

NLOS Non Line Of Sight

P2PPeer-to-Peer

PHY PHYsical layer

PNC Physical Layer Network Coding

QoS Quality of Service

RF Radio Frequency

RN Relay Node

RNC Random Network Coding

RS Relay Station

SC Selection Combining

SIMO Single Input Multi Output


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VII

SINR Signal-to-Noise-plus-Interference Ratio

SISO Single Input Single Output

SNR Signal to Noise Ratio

STBC Space Time Block Coding

STTC Space Time Trellis Code

SUI Stanford University Interim

TDMA Time Division Multiple Access

UE User Equipment

V-MIMO Virtual-MIMO

WCDMA Wideband Code Division Multiple Access


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List of Symbols

A Average SNR at each path

a Packet from S1

b Packet from S2

C Channel Capacity

E Edges

ER

bR

Average signal energy per bit

d Dimension of linear code

F Finite Field

Encoding Vector

SNR at any M antenna

Gain coefficient

H M*N channel matrix

Identity Matrix size M

i, j integer number

M Number of received antennas

n Additive white Gaussian noise vector

Additive white Gaussian noise samples

N Number of transmitted antennas

NRoR Single sided PSD of noise

q Prime Number

P No. of transmitted packets

Original packets generated by source S


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T No. of transmissions per packet

V Set of nodes

W Connectivity matrix

, Coefficients of the matrix W

Information Vector

Signals from each branch

Average SNR at output

y Received signal

P2

P Noise variance


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Chapter One Introduction

1

Chapter One

Introduction

1.1 Motivation

The main idea of network coding was introduced in 2000 by Ahlswede et al. [1].

With network coding, an intermediate node (router) can not only forward its

incoming packets but also encode them to improve network throughput.

It has been shown [1], that the use of network coding can enhance the

performance of wired networks significantly. Recent works have indicated that

network coding can also offer significant benefits for wireless networks.

Communications over wireless channels are error-prone and unpredictable due to

fading, mobility, and intermittent connectivity. Moreover, in wireless networks,

transmissions are broadcasted and can be overheard by neighbors, which istreated in current systems as interference [2]. Multiple transmit and multiple

receive antennas can improve the performance in fading environments by means

of spatial diversity [3]. This is known as multi-input multi-output (MIMO)

system.

The approach of the present work is to combine network coding with MIMO

technology. This scheme is expected to provide throughput enhancement offered

by network coding as well as spatial multiplexing and spatial diversity gains due

to MIMO implementation. Moreover, Space Time Block Coding (STBC)-MIMO


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transmission also used to provide higher reliability in MIMO-network coding [3-

5].

1.2 Literature Review

Chen et al. in 2006 [6] has shown the application of network coding (NC) in

wireless networks that either contain distributed antenna systems or support user

cooperation between user terminals. In both cases, improved diversity gains are

achievable.

Fasolo et al. in 2008 [5] proposed a scheme which jointly combines NC and

MIMO in order to achieve more robustness with respect to packet losses. The

basic idea comes from the fact that NC and MIMO systems can be described by

similar equations and so they can be easily integrated. Nevertheless, to achieve

this goal, NC functionalities are to be moved towards the physical layer and

implement a more sophisticated decoding phase in order to exploit spatial

diversity.

This was followed by the work of Ono, and Sakaguchi in 2008[7] where he has

presented MIMO network coding, co-channel interference cancellation and

efficient bi-directional transmission that can be realized simultaneously with

lower complexity in multi-hop networks.

Lee and Hanzo in 2009 [8] introduced two types of new decoding algorithms for

a network coding relaying system (NCR), which adopts multiple antennas at

both the transmitter and receiver. They considered the realistic scenario of


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encountering decoding errors at the relay station (RS), which results in erroneous

forwarded data.

Zhang and Liew in 2010 [9] proposed a new scheme called MIMO-PNC, which

rely on the extractions of the summation and difference of the two end packets.

These are then converted to network coded form with linear MIMO detection

method at the relay node.

XU et al. in 2010 [10] presented a two-step communication protocol combined

with virtual MIMO and network coding technique. The protocol is therefore

termed MINEC (MImo NEtwork Coding). A three-node network with multi-

antennas on relay node is taken as an illustrative example of MINEC. Theoretical

and simulative performance analyses show that MINEC protocol outperforms

other relay schemes and provides more robust and efficient transmission.

Tran et al. in 2010 [11] considered MIMO network coding as an alternativephysical/ multiple access control (PHY/MAC) protocol of carrier sense multiple

access/collision avoidance (CSMA/CA). The paper provided details of the

protocol and developed network simulators for performance evaluation.

Furthermore, an efficient retransmission scheme for transmission system

employing network coding is proposed. The paper showed that MIMO network

coding achieves significant network performance improvement with respect to

CSMA/CA mesh networks. The proposed retransmission scheme is also shown

to be effective in terms of resource usage as well as QoS guarantee.


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Lee et al. in 2010 [12] solved the problem of network information flow in

wireless networks using multiple antenna systems which is called the MIMO Y

Channel, where three users exchange messages by sharing an intermediate relay.

They also proposed two efficient signaling methods: signal space alignment for

network coding and network-coding-aware interference nulling beam-forming.

This scheme achieves the optimal degrees of freedom for the MIMO Y channel

and it provides an achievable total rate of about 300% better than that of the

TDMA scheme and about 200% better than that of the multi user (MU)-MIMO

scheme at the high SNR regions.

Zhu and Burr in 2011 [13] present a novel distributed space time block coding

(DSTBC) scheme with the aid of an error detection code based on selection of

relaying protocol for multi-relay assisted two-way cooperative communication

systems and PNC. This cooperative communication scheme for two-way relay

channels can achieve significant throughput and spectral efficiency

improvements.

Gao et al. in 2011 [14] proposed a PNC aided two-way relay scheme with a

multiple-antenna relay node has been proposed, which consists of only two

transmission phases, multiple access (MA) phase and the broadcast (BC) phase.

Maximum ratio combining like (MRC-L) scheme used to achieve receive

diversity in the MA phase, and the STC is adopted to achieve transmit diversity

in the BC phase.

Yoon et al. in 2011 [15] carried out performance analysis using a combination of

MIMO techniques and network coding in wireless relay-based cooperative


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system. The overall error probability and system capacity of multi-source node

uplink relay system having multiple source nodes has been analyzed.

Gupta and Choudhary in 2012 [16] exploited a combination of MIMO and

network coding, which is based on a two-step communication protocol, termed

as MIMO Network Coding. A comparison of the probability of error versus SNR

characteristic is given when the channel is assumed to be known or estimated at

the receiver.

In the present work, the combination of both MIMO and NC is considered. The

latter is implemented at network layer. The incoming packets from source nodes

are added together at coding node to perform the coded packet. The transmission

is considered over models of wireless fading channels.

1.3 Aim of the Work

The aim of this thesis is to combine both techniques (NC and MIMO) to achieve

the most promising advantages such as throughput and error performance of

both. The aim here is to investigate the use of NC together with MIMO and to

see the possible improvements in throughput. The performance is to be

investigated in the presence of wireless channel environment. Error and

throughput performances are to be used in the assessment of the usefulness of the

suggested system.


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1.4 Outline of the Thesis

Chapter two covers MIMO technology and the benefits expected by its

implementation. Chapter three represents the basics of network coding, and its

possible advantages. A full description of network and communication system

model considered in the work is given in Chapter four. The performance of the

suggested MIMO-NC arrangement is presented in Chapter five. Finally, Chapter

six covers the concluding remarks and suggestions for future work.


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Chapter Two Multi Input Multi Output System

7

Chapter Two

Multi Input Multi Output System

2.1 Introduction

The use of multiple antennas at the transmitter and receiver in wireless systems,

known as MIMO technology or smart antenna, take a wide spread due to its

powerful performance-enhancing capabilities.

Communication in wireless channels is impaired by multi-path fading. Multi-

path is the arrival of the transmitted signal at an intended receiver through

differing angles and/or differing time delays and/or differing frequency (i.e.,

Doppler) shifts due to the scattering of electromagnetic waves in the

environment. Consequently, the received signal power fluctuates in space (due to

angle spread) and/or frequency (due to delay spread) and/or time (due to Dopplerspread) through the random multi-path components. This random fluctuation in

signal level is known as fading, can reduce the power of the received signal. In

addition to the time and frequency dimensions that are exploited in conventional

single-antenna (single-input single-output) wireless systems, the leverages of

MIMO are realized by exploiting the spatial dimension (provided by the multiple

antennas at the transmitter and the receiver). MIMO system has a great attention

in wireless communications, because it provides many benefits in terms of

throughput and link range without additional bandwidth or power at the

transmitter [16].


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An overview of MIMO is provided in this chapter. First the advantages of

MIMO system are presented followed by illustrations the types of diversity and

combining methods. Then, the drawbacks of MIMO system are introduced. The

MIMO channel model and the capacity of the channel are also given. Finally,

STBC (Space Time Block Coding) explanation is provided.

2.2 Main Benefits and Drawbacks of MIMO Technology [17]

2.2.1 Benefits of MIMO Technology

The benefits of MIMO technology are array gain, spatial diversity gain, spatialmultiplexing gain and interference reduction and avoidance. These gains can be

described briefly in the following:

a. Array Gain

Increasing in the signal to noise ratio (SNR) at the receiver is called array gain.

This comes from the coherent combining effect of the signals. The coherentcombining may be achieved through spatial processing at the received antenna

array or spatial pre-processing at the transmit antenna array. In MIMO systems,

array gain means a power gain of transmitted signals that is achieved by using

multiple antennas at transmitter or receiver or both. The array gain is provided

by knowledge of the CSI (channel state information), for example MRC

maximal ratio combining (MRC) at the receiver [18].


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b. Spatial Diversity Gain

In wireless system, the signal level at the receiver has been fluctuated or fades.

Spatial diversity gain alleviates fading by providing the receiver with multiple

(independent) copies of the transmitted signal in space, frequency or time. An

increasing number of independent copies (these copies are known by diversity

order), leads to increasing the probability of at least one of the copies is not

affected by a deep fade, as a result the quality and reliability of the received

signals are improved. The higher diversity order the better combat fading.

c. Spatial Multiplexing Gain [19]

Spatial multiplexing is the transmitting of independent information sequence

from multiple antennas at the same time. By spatial multiplexing, MIMO

provides an increasing in data rates with the same bandwidth and no additional

power. The receiver can separate the data streams using signal processing

techniques. The capacity of wireless network can be increased by using spatial

multiplexing. It is enhanced by a multiplicative factor equal to the number of

streams.

d. Interference Reduction and Avoidance [17]

The wireless medium in contrast to other links such as copper and optical fibers,

suffer from interference due to sharing time and frequency resources by multiple

users. Interference may be mitigated in MIMO systems by exploiting the spatial

dimension to increase the separation between users, hence improving the signal

to interference noise ratio (SINR). In addition, the spatial dimension may be

leveraged for the purposes of interference avoidance, i.e., directing signal energy


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towards the intended user and minimizing interference to other users.

Interference reduction and avoidance improve the coverage and range of a

wireless network.

2.2.2 Drawbacks of Multiple-Antenna Systems

Clearly, the various benefits offered by multiple-antenna techniques do not come

for free. For example, multiple parallel transmitter/receiver chains are required,

leading to increased hardware costs. Moreover, multiple-antenna techniques

might entail increased power consumptions and can be more sensitive to certain

detrimental effects encountered in practice. Finally, real-time implementations of

near-optimum multiple antenna techniques can be challenging. On the other

hand, (real-time) testbed trials have demonstrated that remarkable performance

improvements over single-antenna systems can be achieved in practice, even if

rather low-cost hardware components are used [20].

2.3 MIMO Diversity Techniques

There are three types of diversity schemes in wireless communications [18, 21].

a)Temporal Diversity: In this type of diversity, the signal transmitted indifferent periods, for example each symbol is transmitted many times. The

intervals between the transmissions of the same symbol could be at least the

coherent time. Thus the same symbol falls under independent fading.

b)Frequency Diversity: The replicas of the original signals are modulated usingdifferent carriers. The coherence bandwidth of the channel must be small


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compared to the bandwidth of the signal. Frequency diversity can be used to

combat frequency selective fading.

c)Spatial Diversity: This type is also called antenna diversity or space diversity.In this case, copies of the same transmitted signal are transmitted from multiple

antennas. The space between antennas should be far enough apart so that

different copies of the signal undergo independent fading. Spatial diversity is

different from temporal and frequency diversity in that no extra work is needed

for transmission and no additional bandwidth or transmission time is required.

Space-time codes exploit diversity across space and time. Basically the

effectiveness of any diversity scheme is that the receiver must be provided by

independent samples of the basic signal that was transmitted.

Diversity can also be categorized based on whether it is applied to the transmitter

or to the receiver.

Receive Diversity:Maximum ratio combining is a scheme that represents onetypes receive diversity to improve signals quality. It is the best to use transmit

diversity in cellular network at the base station. Because in cellular networks, the

most data traffic occurs in the downlink, thus multiple antennas are used at the

base station. Taking into account the size, cost and the weight of mobile terminal

therefore using transmit diversity.

Transmit Diversity:In this case controlled redundancies are introduced at thetransmitter, which can be then exploited by appropriate signal processing

techniques at the receiver. Generally this technique requires complete channel


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information at the transmitter to make this possible. But with the advent of

space-time coding schemes like Alamoutis scheme [18], which will be

discussed in section 2.6, it became possible to implement transmit diversity

without knowledge of the channel. This was one of the fundamental reasons why

the MIMO industry began to rise. Space-time codes for MIMO exploit both

transmit as well as receive diversity schemes, yielding a high quality of

reception.

The traditional types of diversity schemes are [17, 18, 21]:

a. Selection Combining

This is the simplest combining method as shown in Fig.2-1. Selection combining

(SC) or called antenna selection is selecting the signal with the highest SNR and

then do the detection. This signal is used until its SNR becomes below a

determined threshold. Selection combining used the highest power, error rate, etc

rather than SNR. SC only requires one RF (radio frequency) chain, therefore it is

less complex and low cost.

2 1

2

1

2

1

Select

Best

Antenna

Figure 2-1Selection Combining


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b. Maximal Ratio Combining [22]

In MRC, used in single input multiple output (SIMO), the signals from all of the

M branches are weighted according to their individual SNRs and then summed.

It is the optimal method to maximize the SNR. represents the transmittedsignal from each branch and each branch has a gain , then the received signalis: = =1 + (2.1)Where is the noise as in Fig. 2-2 and * represents complex conjugate. Theaverage SNR at the output of the maximum ratio combiner is =MA,whereAisthe average SNR at each path andM is the number of received antennas.MRC

requiresMradio frequency, where RF chains are implemented by analog circuit.

This makes it large size and high cost. In some cases, the place is not large

enough to use multiple RF chains; therefore the combiner with one RF chain is

used.

1

2 2

1

21

21

Figure 2-2Maximum ratio combining


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c. Equal Gain Combining [23]

Equal gain combining (EGC) is a special case of maximum ratio combining with

equal weights as shown in Fig. 2-3. It does not require estimation of the complex

channel gains for each individual branch. Instead, the receiver sets the

amplitudes of the weighting factors to be unity (| | = 1), where i= 1, 2, ,M[21]. It is less complex than MRC. The improvement in the average SNR at the

output of the equal gain combiner is [18]: =1 +4

( 1) (2.2)WhereMis the number of the received antenna.

2.4 MIMO Channel Model [24]

The design of the MIMO system is shown in Fig. 2-4. In this section, the general

picture about MIMO system model with Mreceived antennas andN transmitter

antennas is given. This channel can be described by the following matrix

equation:

1

2

2 1

21

21

Figure 2-3Block diagram of EGC technique


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= + (2.3)or;

1

2 =1

2+1

2 (2.4)Where =11 1 1 (2.5)Here,Xis the transmitted symbol vector and Y is the received vector. Both can

be real or complex signals. ni is the real or complex additive Gaussian noise

sample,HdenotesM *Nmatrix, and represents gain coefficients.

2.5 Channel Capacity [25]

For a memoryless single input single output (SISO) system, the channel capacity

(in bits/sec/Hz) is given by:

Receiver

T

ransmitter

1

2

2

1

Figure 2-4MIMO system

MIMOChannel


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= log2(1 +||2) (2.6)Where refers to the capacity of the system and h represents the channelcoefficient. In Eq.2.6 and subsequent equations, is the SNR at any antenna. Asmore antennas are deployed, the capacity is improved. Thus the capacity ofSIMO system can be given by: =2( 1 + | |2=1 ) ...(2.7)Where is the gain for the receiver antenna. Note that the crucial feature of Eq.(2.7) in that increasing the value of Monly results in a logarithmic increase in

the average capacity. Similarly for the transmit diversity, a multiple input single

output (MISO) system withN transmit antennas, the capacity is given by:= log2(1 + | |2)=1 (2.8)Where the normalization byN ensures a fixed total transmitter power and shows

the absence of array gain in that case (compared to the case in Eq. (2.7) where

the channel energy can be combined coherently). Again, note that capacity has a

logarithmic relationship withN.

Now, the use of diversity can be considered at both transmitter and receiver

giving rise to a MIMO system. For N transmitting antennas and M receiving

antennas, the capacity equation will be represented by:

= log2[det

+

] (2.9)

Where is identity matrix of size M. Note that both Eq. 2.8 and Eq. 2.9 arebased onN equal power (EP) uncorrelated sources.


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2.6 Space Time Coding [3]

Space-time coding represents the channel coding technique for transmissions

with multiple transmit and receive antennas. Fig. 2-5 shows how this fit in the

system model. Generally, there are two classes of space-time coding, Space-

Time Block Codes (STBC) and Space-Time Trellis Codes (STTC).

.

2.6.1 Space Time Block Coding

STBC is a technique that is used in wireless communication for both indoor and

outdoor systems to transmit multiple copies of data across many antennas. It is

less complex than space time trellis coding (STTC) and provides only diversity

gain but no coding gain as in STTC. With STBC the transmitted bits are

modulated first, then mapping it to coding matrix, by exploiting both temporal

and spatial diversity (where a signal is transmitted from one antenna, then

delayed one time slot, and transmitted from the other antenna) [17, 25]. STBC

improves the reliability of data transfer by exploiting different received copies of

data. In fact, while the signals are transmitted, it becomes under the influence offading. Thus some of the received copies are better than others. STBC should

combine all received replicas to extract the useful information as possible [5].

Figure 2-5Space time coding

Information

source

Space -Time Encoder

() ()

1() 1()

(

)

Receiver

.

.

.

.


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Alamouti [3] discovered an ingenious spacetime block coding scheme for

transmission with two transmit antennas and two receive antennas. According to

this scheme, input symbols are grouped in pairs where symbols

and

+1are

transmitted at time kfrom the first and second antennas, respectively. Then, at

time k+1, symbol +1 is transmitted from the first antenna and symbolistransmitted from the second antenna. This imposes an orthogonal spatio-

temporal structure on the transmitted symbols. Alamouti's STBC has been

adopted in several wireless standards such as wideband code division multiple

access (WCDMA) and code division multiple access (CDMA2000) due to the

following attractive features: First, it achieves full-diversity at full transmission

rate for any (real or complex) signal constellation. Second, it does not require

CSI at the transmitter (i.e. open-loop). Third, maximum-likelihood decoding

involves only linear processing at the receiver [17].

2.6.2 Space-Time Trellis Coding (STTC)

Space-time trellis coding combine modulation and trellis coding to transmitinformation over multiple transmit antennas and MIMO channels. It became

extremely popular because STTC can simultaneously offer coding gain with

spectral efficiency and full diversity over fading channels [18].

For two transmitting antennas (for example), there are two symbols that are

transmitted from these two antennas for every path in the trellis. There is no

parallel path in the STTCs. Therefore, the STTC can be represented by a trellis

and a pair of symbols for each trellis path. The corresponding indices of the

symbols are used to present the transmitted symbols for each path [19].


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Although, the space time block coding (STBC) does not provide a coding gain as

compared with space time trellis code (STTC), but its simplicity was the main

reason to use it in this thesis.


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Chapter Three Network Coding

20

Chapter Three

Network Coding

3.1 Introduction

In this chapter the advantages and disadvantages of network coding are

introduced. Then linear network coding (encoding and decoding) is provided.

Finally, the applications of network coding are explained.

A graph G= (V, E), with V sets of nodes and E sets of edges can represent any

network, where devices such as router, access point and others are represented

by nodes and the links or channels are represented by edges [1].

Network coding has been suggested to combat the limitations on these devices

and channels in classical networks. With network coding, the router will be able

to do more processing on incoming packets, it will combine the packets instead

of only store-and-forward the output messages by routing [26], thus maximizing

the overall system performance. Network coding can improve throughput for

several wireless topologies in general and cooperative relaying networks in

particular, robustness, complexity, reliability and security. In wireless networks

further improvement in resources such as energy efficiency, delay, wireless

bandwidth and interference also can be obtained [27, 28].


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3.2 Types of Network Coding [29]

Network Coding can be divided into several categories depending on the

network topology, characteristic and the location (i.e. communication layer) of

the NC operation. In particular, NC can be divided into the following families:

Single-or multiple source:indicating if there is a single or multiple source atthe origin.

Cyclic or Acyclic: referring to whether there is a directed cycle or not,between the source and the destination. In the acyclic case all the nodes in the

network simultaneously receive all their inputs and produce their outputs

(implying a memoryless channel). In the cyclic case there is delay by

construction and has to be considered, hence it can be modeled as convolutional

codes [30].

Analog or Digital: indicating whether the NC operation is conducted on thesignal or finite field at the NC node. It should be noted that analog NC is also

called Physical Network Coding or superposition coding. The digital NC can be

executed at any layer (Link, Application etc.).

3.3 Advantages of Network Coding

The notion of coding at the packet level, which is known as Network Coding,

has attracted a lot of attention after the work of Ahlswede et, al [1]. At first NC

appeared useful for multicast in wire-line communication but soon its benefits

increased. In fact, because of the broadcast nature of wireless channels, NC


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turned out to be a very promising scheme for wireless communication

applications. Network Coding has the potential to improve energy efficiency,

enhance link performance, and improve system throughput in various wireless

networks. This is very attracting to network researchers, engineers, and

businesses that wireless in its various forms will be the dominant medium of

communication in the future [29].

3.3.1 Throughput [28]

The most well-known benefit of network coding and the easiest to illustrate is

increase of throughput. This throughput benefit is achieved by using packet

transmissions more efficiently, i.e., by sending more information with fewer

packet transmissions. The most famous example of this benefit was proposed by

Ahlswede et, al [1], who considered the problem of multicast in a wireline

network. Their example, which is commonly referred to as the butterfly network

as shown in Fig. 3-1, features a multicast from a single source to two

destinations. In this network, every arc represents a directed link that is capable

of carrying a single packet reliably. Both receivers want to know the message at

the source node. In the capacitated network that they consider, the desired

multicast connection can be established only if one of the intermediate nodes

(i.e., a node that is neither source nor sink) breaks from the traditional routing

paradigm of packet networks, where intermediate nodes are allowed only to

make copies of received packets for output, and performs a coding operation. Ittakes two received packets, forms a new packet by taking XOR of the two

packets, and outputs the resulting packet. Thus, if the contents of the two

received packets are the vectors a and b, the output packet of these two input


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vector is ab. The sinks made a decoding operation on the received packet.Sink D1 recovers b by taking the XOR of a and ab, and likewise sink D2recovers a by taking the XOR of b and a

b. With routing, we could

communicate, for example, a and b to D1, but we would then only be able to

communicate one of aor btoD2. The butterfly network illustrated that network

coding can increase throughput for multicast in a wireline network. The nine

packet transmissions that are used in the butterfly network communicate the

contents of two packets. Without coding, these nine transmissions cannot be

used to communicate as much information, and they must need additional

transmissions (for example, an additional transmission from nodeBto node T).

Figure 3-1 Butterfly network


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Network coding can also be extended to wireless networks and, in wireless

networks, it becomes even easier to find examples where network coding yields

a throughput advantage over routing. Indeed, the wireless counterparts of the

butterfly network in Fig.3-1 and the modified butterfly network in Fig.3-2

involve fewer nodes seven and three nodes, respectively. As before, these

examples show instances where the desired communication objective is not

achievable using routing, but is achievable using coding. These wireless

examples differ in that, rather than assuming that packet transmissions are from a

single node to a single other node, they allow for packet transmissions to

originate at a single node and end at more than one node. Thus, rather than

representing transmissions with arcs, hyper-arcs can be used instead (arcs that

may have more than one end node).

3.3.2 Minimizing Energy per Bit [31]

There are advantages to network coding beyond maximizing throughput. In

particular, network coding can minimize the amount of energy required per

packet (or other unit) of information multicast in a wireless network.

2

1

1

Figure 3-2 The modified wireless butterfly network


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Fig. 3-3 shows a wireless network with nodes arranged in a square, with radio

ranges such that the nodes can directly communicate with neighbors horizontally

and vertically, but not diagonally. There is a single multicast session with a

sender Sat the top center and receivers D1and D2at the bottom left and right

corners. Assuming that each transmission takes one unit of energy, the number

of transmissions can be used as an approximation for the amount of energy

required to multicast each packet. If only routing is permitted, then it is possible

to show that a minimum of five transmissions is required to multicast a packet

from StoD1 andD2. (For example, the first transmission broadcasts the packet

to the senders two neighbors, and four other transmissions move the packet to

the two receivers, as illustrated in Fig.3-3a.

However, if network coding is permitted, then only 4.5 transmissions per packet

are required on average, using nine transmissions for two packets and . (For

(a) Without NC (b) With NC

Figure 3-3Network coding minimizes energy per packet. Sender S,

receiversD1andD2

a

a

a

Broadcast a

a

D1 D2

43

1 2S

5

a

a

a b

b

b

ba

Broadcast

a+b

3 4

1 S 2

D1 D25


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26

example, three transmissions can move packet to receiver D1, threetransmissions can move packet to receiver D2, two transmissions can movepackets

and

to an intermediate node, and a final transmission can broadcast

+back out to the receivers, as illustrated in Fig. 3-3b. It can be shown thatunder this model of a wireless network, linear network coding can always

achieve the minimum energy per packet and the required coding coefficients can

be computed in polynomial time [32].

3.3.3 Minimizing Delay [31]

Network coding can also minimize the delay, as measured, for example, by the

maximum number of hops for a packet to reach a receiver. Fig. 3-4 shows a

network of four nodes arranged in a tetrahedron, with unit-capacity edges

running down the sides and around the bottom in a cycle. There is a single

sender at the top and three receivers at the bottom. It is easy to verify that the

between the sender and any receiver is 2.

Edmonds theorem therefore guarantees the existence of two edge-disjoint

spanning trees (minimum set of edges that connect all vertices) along which the

S

3

21

ab

a

ab

b

Figure 3-4Network coding minimizes delay

a+b

S

3

21

ab

a+ba

b

(a) Without NC (b) With NC


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27

sender can route two unit-rate streams to the three receivers. Figure 3-4a shows

that the depth of the blue tree is three, which is therefore the minimum possible

overall delay if only routing can be used to communicate at rate two. In contrast,

Fig. 3-4b shows that if network coding can be used, it is possible to reduce the

delay 2, by routing stream along the red path, stream along the blue path, andtheir mixture +along the green path.3.3.4 Security [28]

From a security viewpoint, network coding can offer both benefits and

drawbacks. Consider again the butterfly network Fig. 3-1. Suppose an adversary

manages to obtain only the packet ab. With the packet ab alone, theadversary cannot obtain either a or b; thus we have a possible mechanism for

secure communication. In this instance, network coding offers a security benefit.

Alternatively, suppose that node B is a malicious node that does not send out

ab, but rather a packet masquerading as ab. Because packets are codedrather than routed, such tampering of packets is more difficult to detect.3.4 Disadvantages of Network Coding [33]

The major problem with network coding is that the loss of one packet could

affect many other packets and renders some information useless at the receiver.

In Fig. 3-5D2needs four bits a, a+c, b, and b+dto recover cand d. If ais lost in

the network, D2 cannot recover c even if a+c is received correctly. In this

situation the encoded information received correctly (i.e. a+c) is regarded as a

loss because the encoded information itself is invalid. In other words, in network


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28

coding, one bit loss in the network results in several bits losses for the receivers.

Also, whenD2received a+cat first and received a after a certain period of time,

c is delayed and cannot be recovered until all the information necessary to

recover are received (i.e. a).

Synchronization is a problem that needs to be considered when network coding

is implemented in computer or satellite networks. Since, usually there is more

than one incoming data stream at the input of an intermediate node, it is

necessary to acquire synchronization among these data streams. This is not a

problem for non real-time applications (e.g. file transfer), but it can be a serious

problem for real-time applications (e.g. voice and video transmission). However,

certain types of networks like switching networks are inherently fully

synchronized. These types of networks are perfect candidates for the application

of network coding [34].

Figure 3-5Packet loss in network coding

S

D1

321

D2 D3

a, b

a,b

c,d

a+c,

b+d

a,b,c,d a,b,c,da,b,c,d

c,dc,d

a+c ,

b+d


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29

3.5 Linear Network Coding

It has been shown that intermediate nodes in a network, when performing NC,

are able to combine a number of packets received into one or more new outgoing

packets. LNC [34, 35], permits, instead of binary field operations, moving to

larger field sizes, being able to perform more complex operations when

combining incoming packets in intermediate nodes, becoming one of the most

successful network coding algorithms as it permits achieving network capacity

when multicasting, with relatively low complexity. In LNC each data unit is

processed using finite fields

with qas a prime number or, considering a GF,

q=2 for some integer m, where 2 refers to [0, 21].Having a graph G= (V, E), where data is transmitted, the butterfly network is

once again the chosen example to best understand LNC operations within a

network, as illustrated in Fig. 3-6 [36].

3.5.1 Encoding

As shown in Fig. 3-6, we have (1 , , )original packets generated by sourceS, where d=2in the previous example (2-dimensional linear network code). Each

packet is associated to a sequence of encoding vectors(1, ,) in field 2 and equals to an information vector:

=

=1 (3.1)


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30

Being the sum executed (over the finite field2 ) in each symbol position so: = =1 (3.2)Where and is the symbol of X and . Updating Fig. 3-6, the fieldassociated is 2= {0, 1} being 1= a and2= b, consecutively we have the resultin Fig.3-7.

Figure 3-6LNC: Local/ global coding vectors of 2-dimensional linear network code


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31

Concluding, with LNC, addition and multiplication are performed over the finite

field2 , where the resulting packets are linear combinations of the originalones, still with the same length as the original packets [36].

3.5.2 Decoding

At LNC, the original packets (1 , , )are retrieved at the receiver nodes byexecuting a simple Gaussian elimination (decoding). The encoded packets,

carries two kinds of information: encoding vectors(1, ,)and informationvectorX.

Figure 3-7 LNC: local/ global coding vectors updated


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32

When a node receives mnumber of Xs (m d), it is able to decode them and

retrieve successfully the original packets. Therefore the rank of the matrix in

Eq. (3.3) has to be d, in other words, the vectors belonging to matrix have to be

linearly independent and these vectors can be given by [36]:

1 1 1 (3.3)

3.6 Applications of Network Coding [29]

3.6.1 Network Coding in the Internet

The primary example of the application of NC is in the Internet, both at the

network layer (routers in ISPs) and at the application layer (dedicated

infrastructure such as content distribution networks and ad-hoc networks such as

peer-to-peer networks). For NC to be practical, random coding could be used to

allow the encoding, among the nodes, to proceed in a distributed manner.

Packets need to be tagged to allow the decoding to proceed in a distributed

manner (i.e. the nodes decode independently). Buffering is also needed to allow

for asynchronous packet arrivals and departures. Avalanche is a widely known

application that uses NC [37]. In a peer-to-peer content distribution network, a

server usually splits a large file into a number of blocks. Peer nodes try to

retrieve the original file by down loading blocks from the server but also by

distributing downloaded blocks among them. To this end, peers maintain

connections to a random number of neighboring peers, with which they

exchange blocks. In Avalanche, the blocks sent out by the server are random


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33

linear combinations of all original blocks. Similarly, peers send out random

linear combinations of all the blocks available to them.

3.6.2 Network Coding in Wireless Networks

NC has gained a lot of interest within the wireless communications research

community [34, 38]. The most relevant areas within wireless networks where

NC is applied are explained in the following overview.

a. Unicast Communication

Let us consider the case when a Base Station (BS) wants to transmit a file to a

UE within the cell. Let us assume that the BS hasPpackets to deliver to the UE.

With an Automatic Repeat-reQuest (ARQ) protocol of todays wireless

networks, each packet needs to be transmitted until an acknowledgement is

successfully received at the BS. With an average of Ttransmissions per packet, T

* P transmissions will be needed to transfer the file completely, which,

depending on the reliability of the radio channel, can be quite large. Network

coding, however, provides the ideal solution in this case, with very low

complexity. For instance, the BS can transmit random linear combinations of the

packets instead of individual packets. In particular, the BS transmits packets of

losses, providing efficient reliability. This scheme increases the system

throughput with a throughput gain that increases with the number of destinations

(UEs) [39].


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34

b. Network Coded Cooperation in Wireless Networks

The form of NC is well suited for cooperation in wireless networks. When such

a union (between NC and cooperative networks) occurs, those wireless networks

are called Diversity Network Codes (DNC). The cooperation can be obtained via

a fixed Relay Node (RN) within the cell, where two or more UEs can cooperate

when communicating with the BS. There can also be cooperation between UEs,

where each UE forwards the information of the other UE by applying NC.

Where the pioneer work in this topic, Xiao, et al. [40] considers a scheme for

two-user cooperation, in which each UE transmits the binary sum of its own

source message and partner messages, resulting in spectrally efficient

transmission.

c. Physical Network Coding (PNC)

PNC is a smart physical layer technique that transforms the superposition of the

electromagnetic (EM) waves as an equivalent NC operation that mixes the radio

signals in the air. A form of NC is then created at the physical layer, and works

on the EM waves in the air rather than on the digital bits of data packets or

channel codes [41].

d. Video on Demand, Live Media Broadcast, Game Spectating, and Instant

Messaging [31]

Video on demand is essentially a file download in which the pieces of the

downloaded file must arrive in order and be decoded in real time, after some

small delay. Network coding can be applied in this case by breaking the file into

generations, which can be downloaded sequentially. A similar technique can be


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35

applied to live media broadcast. Earliest decoding can be used to further reduce

delay. Similar to live media broadcast is game spectating, in which spectators

can watch other gamers play. Instant messaging (IM) is also similar to live

broadcast, but with typically low bit rate (and bursty) text messages and a softer

delay constraint. However, IM is increasingly including larger messages such as

images and audio clips. Flooding, which is usually used for IM in P2P networks,

is inefficient when used on larger files. Network coding can be applied in all of

these cases to improve efficiency in overlay networks.


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Chapter Four Model of MIMO-NC system

36

Chapter Four

Model of MIMO-NC System

4.1 Introduction

In this chapter the model of the system which combines the MIMO system (at the

physical layer) and NC (at the network layer) is introduced. Moreover all the

required parameters, channel models, and different stages in processing of signal

are presented. Finally the simulation scenario of each step for the system model is

also described. The whole system model is shown in Fig.4-1.

To simplify the description of the above system model, the system is divided into

physical layer (transmission system) model and network coding model processed

by the network layer or by the lower layers. The former describes the whole

processing applied to the signal starting with the modulation process, MIMO

transmission of the channel, and the detection process applied at the receiver of

the reception nodes (relay or destination node). The network coding model deals

Figure 4-1The model of the system for combined MIMO-NC

techniques

Source NetworkCoding (NC)

BPSK

Modulation

MIMO

Transmission

Destination NCDecoding

BPSKDetection

MIMODecoding

Channel


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37

with the processing of the packets and the coding/decoding processes applied at

the given node (relay or destination).

4.2 Transmission System Model

As the network layer node produces packets for transmission, the bit content of

the packets is transmitted by the modulator one by one. Since PSK modulator is

modeled by its corresponding baseband equivalent signal representation, each

binary digit is converted to baseband signal +1 or -1 depending on the binary digit

value 0 or 1, respectively. The modulator output is then fed to the antenna to be

transmitted to the destination node. If the system considers MIMO transmission

the signal is fed to the antenna-set. The considered MIMO system uses two

antennas at both transmitter and the receiver as shown in Fig. 4-2.

The used channels are AWGN, Rayleigh fading, and Stanford University Interim

(SUI-3) channels. Rayleigh channel is assumed to be flat and remains constant

over two successive symbol periods. The Stanford University Interim (SUI-3)

2221

11

12

1 ,2

2, 1

1

2

1, 2Transmitter

Receiver

Figure 4-2 2x2 MIMO system considered in the system


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38

channel model represents broadband fixed wireless channel normally considered

for IEEE802.16.a3 and WiMAX with three paths channel. The maximum

Doppler shift for all paths is 0.4 Hz. Path delays are 0, 0.4, and 0.9 s for path

one, two, and three respectively. Gains of paths are 0, -5, and -10 dB for path one,

two, and three respectively. The 22 channel matrix can be expressed as [42]:

=11 1221 22 (4.1)Where denotes the channel coefficients between receivermand transmittern. Further, the channel is assumed known at the receiver for all reception nodes.

The Rayleigh model assumes non-line of sight (NLOS), and it can be used forenvironments with a large number of multipaths. The Rayleigh model has

independent identically distributed (i.i.d.) complex, zero mean, unit variance

channel elements and is given by [43]:

= 12 ((0,1) + 1 . (0,1)) (4.2)Where Normal(0,1) is normally (Gaussian) distributed random variable with

zero mean and unit variance.The transmitted signals are encoded by space time

block coding (STBC) that represent one type of transmit diversity considered in

MIMO coding [17, 19], so the coding matrix X can be represented by the

following form [20, 42]:

=1 2

2 1 (4.3)

The equation that describes the received signals at the relay node and destination

nodes is [42]:

11 1221 22 =11 1221 22

1 22 1 +

11 1221 22


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40

N

(a) Transmission part

Are there packets

in each queue to

be coded?

1

BPSK Modulation

MIMO Transmission

Network encoding of packets

Packets to bits construction

Start

Parameter setting

(Network, No. of nodes,

packets, and bits)

Select (source, destination

nodes randomly, then

generate random packet)

Destination

is the next

Store the packet in coding node

N

Figure 4-3The Flowchart of the suggested system


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41

Fig. 4-3 illustrates the basic model used in this work. Each circle represents a

node. S, D and R stand for source, destination and relay node, respectively. S1

and D2 (S2and D1) out of each other's communication range, thus they have a

data to be exchanged through the relay (coding) nodeR. The coding node has two

queues one for the first source and the other for the second source.

Figure 4-3The Flowchart of the suggested system

(b) Reception part

Network decoding

Is the reception

node: final

destination or relay

node

Uncodedreception

N

1

Y

MIMO reception

BPSKdemodulation/detection

End

Packet reconstruction


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42

The connectivity matrix of the network in Fig. 4-3 can be expressed as:

W=

0 0 1 1 0

0 0 1 0 1

1 1 0 1 1

1 0 1 0 0

0 1 1 0 0

(4.7)

Where wi,j, the coefficient of the matrix, represents the chance of available

connectivity among the node ito the nodej. Since these coefficients are either 1

or 0, this means that the node either connected (with probability of 1 for

connection) or else not connected (zero probability for connection).

4.3.1 Encoding Process

The process of encoding for the incoming packets to the relay node is described

in this section. S1 and S2 broadcast their packets as explained in section 4.2

which then presented by 2x2 MIMO system to the channel. The relay node Rand

destination nodeD1 will receive the transmitted packets from S1. The transmitted

packets from S2are received by the relay node Rand the destination node D2.

D1

S1

D2

R

S2

Figure 4-3Network model


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43

The relay node pick one packet from the first queue (for S1) and another packet

from the second queue b (for S2) and encode these two packets by using XOR

operation to produce r (coded packet):

= (4.8)Whereand are the transmitted packets from S1and S2respectively. The relaynode broadcasts the coded packet r to all destinations. The format of the coding

packet is shown in Fig. 4-4.

The Generation of packets for each node is performed as follows:

1.The first bit is set as a flag to determine the coded packet, if the flag is equal to"1" then packet is encoded; else if it is 0 then the packet is processed without

coding.

2.Random payload generation (data bytes).3.The sequence number for the generated packet in the transmitter.4.Hop count.5.Source node address.6.Destination node address.

Flag

1 byte

Source

Address

4 byte

Destination

Address

4 byte

Hop

Count

1 byte

Packet

Sequence

4 byte

Payload

111 byte

Figure 4-4Packet Format


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44

4.3.2 Decoding Process

The destination node D1 and D2 receive the uncoded packets from direct

transmission, it also stores the packets that can be used in decoding of future

packets. The destination nodes make a reply to the relay node to announce that it

have a specified packet. After the reception of the coded packet at the destination

nodes, these nodes begin the decoding process. The decoding process is simple

and can be represented by XOR operation as in the following equations:

atD2 = = (4.9)atD1 = = (4.10)

Eqs. 4.9 and 4.10 prove thatD1can obtain the packet sent by S2, and similarly

D2can get the packet sent by S1.


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Chapter Five Simulation Tests and Results

Chapter Five

Simulation Tests and Results

5.1 Introduction

In this chapter, the simulation test results of the proposed MIMO-NC system

are presented. First, the bit error rate (BER) performance of different systems

is presented. The throughput evaluation of both NC and MIMO-NC is then

introduced. On the other hand, all systems are tested over AWGN channel,

one path fading channel and Stanford University Interim (SUI-3) standard

channel models. The simulation was done using MATLAB (version 7.10.0).

The BER is given by the ratio of incorrect detected data bits at the

destinations to the total number of transmitted data bits. The x-axis represents

the signal-to-noise power ratio defined by ERbR/NRoR(in dB). ERbRis the average

signal energy per data bit and NRoR is the single sided power spectral density

(PSD) of noise. This is taken here to be equivalent to the noise variance 2.

Thus, the signal to noise power ratio (SNR) is taken to be;

/() = 1010(

2) (5.1)

Where = 1, for the case of equal probable data bit with +1 and -1 values.

The SNR is changed in the tests and the corresponding variance 2value is

determined by inverse of Eq.5-1 using the following expression;

2 = 10

10 (5.2)

The number of transmitted bits per packets are 1000 bits, two antennas at both

the transmitter and receiver are used. The modulation type considered is

BPSK. The basband equivelant of the BPSK is actually used and the BER rate

is determined after transmission of about 1000 packets.


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The throughput performance tests are also presented in this chapter. The

general definition of throughput is given by the ratio of useful transmissions

divided by the number of all transmissions involved. Thus, an equivalentexpression can also be used here to show the advantage of network coding

given by:

= .

. (5.3)

5.2 BER Performance Tests

5.2.1 AWGN Channel

Fig. 5-1 shows the BER performances of different systems over AWGN

channel. The results show that SISO coding and MIMO coding are the same

and the BER for SISO without coding and MIMO without coding are almost

identical as well. This is due to the fact that AWGN channel dose not

introduce any distortion (fading) and thus the advantages of MIMO

implementation is not explored.

5.2.2 Single Path Fading Channel

Fig. 5-2 represents the BER performances of different systems over single

path fading channel (as described in chapter 4). It is clear that the systems

using MIMO techniques have much improvement over those with SISO

systems. These results are clear whether the system used NC or not. Theimprovement in SNR is well above 15 dB for BER values lower than 10 P

-3P. It

is clear that the improvement due to coding (whether the reference system is

SISO or MIMO) is very small. This is true since the advantage of NC can be

noticed clearly in throughput rather than the improved BER performance.


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Figure 5-2 BER Performance of different systems over single

path fading channel

Figure 5-1BER Performance of different systems over AWGN channel


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5.2.3 SUI-3 Fading Channel

The BER performance over SUI-3 fading channel with three paths is shown in

Fig. 5-3. As it is clear from this figure, the performances of all systems havesuffer great deal of degradation due to the channel effects. Without MIMO

(i.e. SISO) systems, the performances are very poor. The range of BER is

above 10P-2

P for all SNR values presented in Fig.5-3. On the other hand, with

MIMO systems the performances are acceptable, where there is clear decay in

BER as SNR increased.

5.3 Throughput Performance Tests

5.3.1 AWGN Channel

Fig.5-4 shows the throughput test results in the case of AWGN channel. The

definition of throughput is given in Eq.5-3. It is clear that the use of network

Figure 5-3BER Performance of different systems over multi-path

fading channel


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coding increase the throughput of the system. This gain in throughput is

independent on whether the MIMO technique is used or not, since the channel

has no distortion (no fading). At very low SNR the throughput is almostvanished due to high error rate. The throughput is increased suddenly as the

SNR increased beyond 8 dB for all the systems considered here. It reaches a

fixed value above 14 dB. The use of network coding gives better throughput.

The percentage gain in throughput due to coding is about 33% at relatively

high SNR. This factor may vary from one channel to another.

5.3.2 Single Path Fading Channel

The throughput performance of all systems over single path fading channel is

presented in Fig. 5-5. As compared to the case of AWGN channel (Fig.5-4),

the throughput of uncoded systems is relatively poor. This is due to the high

Figure 5-4Throughput performance of different systems over AWGN

channel


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level of distortion caused by the fading channel and hence the large number of

errors in the detection at the receiver side. In the case where the MIMO

technique is used, better throughput performances are resulted. The effects offading are dealt with by the use of MIMO. MIMO-NC system gives better

throughput performance as compared to the case of MIMO technique without

network coding. Again the percentage advantage in throughput of MIMO-NC

as compared to that of MIMO without coding is about 33%. This is similar to

the case of AWGN channel.

5.3.3 SUI-3Fading Channel

Throughput performance over SUI-3 model of multi-path fading channel is

presented in Fig.5-6. The throughput of the SISO system with network coding

Figure 5-5Throughput performance of different systems over

single path fading channel


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is slightly improved. The general behavior of the systems is almost the same

as in the previous fading channel (Fig.5-5). Also, the percentage

improvement in throughput of MIMO-NC over MIMO without coding isabout 33%.

5.4Assessments of Results

The results show that both BER and throughput for SISO coding and MIMO

coding are the same and for SISO without coding and MIMO without coding

are similar also. This is due to the fact that AWGN channel does not provide

any distortion (fading) and thus the advantages of MIMO implementation is

not explored. 0TFrom the obtained results, we can conclude 0T that the MIMO

system provides more benefits than SISO system. The performances of all

Figure 5-6Throughput performance of different systems over multi-

path fading channel


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systems in the case of multipath channel suffer from a great of degradation

due to the channel effects. The percentage advantage in throughput of MIMO-

NC as compared to that of MIMO without coding is about 33%. Finally, asmall improvement in the throughput of the SISO system with network

coding was observed.


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Chapter Six Conclusions and Future Work

Chapter Six

Conclusions and Future Work

6.1 Conclusions

Multi-input multi-output (MIMO) system is considered as an important

technique used in wireless communications to limit the effect of multipath

fading. Network coding, on the other hand, provide throughput improvement

and some other advantages. In this thesis both techniques have been used incombined form to show the integration of both benefits.

The main points concluded from the simulation tests are;

1- The uses of SISO-NC over channel having great deal of fading does notimprove the throughput unless very high SNR is considered.

2- The use of MIMO-NC is essential when the fading level is high and theoperating SNR is relatively low to gain some improvement in

throughput.

3- The improvement in throughput cannot be met without MIMO in thecase of wireless fading environments. The butterfly network considered

in the work give about 33% improvement in throughput with MIMO-

NC at moderate and high SNR over the fading channels.

4- Finally, MIMO-NC system reserves the advantages of both MIMO andNC techniques (i.e BER and throughput improvements) at moderate

and high SNR over wireless fading channels.


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Chapter Six Conclusions and Future Work

6.2 Future Work

The possible future works are:

1.Study more sophisticated channel coding with the MIMO-NC system.2.Using various ranges of modulation schemes with MIMO system to find

optimal selection under different channel conditions.

3.Diversity and multiplexing modes of MIMO system can be used. Wherefirst step of the transmission at the source nodes uses diversity mode and

second step transmission by relay node uses multiplexing mode or vice

versa.4.Adaptive bit loading, power control, and routing algorithms can be studied

in the presence of MIMO-NC coding to assess the suitability of the

techniques in such conditions.


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[2] H. N. Lee, S.-Y. Chung, C. Fragouli, and Z.-H. Mao, NetworkCoding for Wireless Networks, EURASIP Journal on Wireless

Communications and Networking Volume 2010, Article ID 359475,

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