Design For Mobile Communication Computer Science Essay

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ABSTRACT

This report presents design of multi-user CS(Complementary Sequence) modem design based on mobile communication system. Now a days it is experiencing huge growth rates in mobile communica­tion systems,increasing mobility awareness in society, and the worldwide deregulation of former monopolized markets.Therefore,Multi-user communication plays important role in huge mobile market and multi-user communication can be effected by means of uncorrelated sets of CSs.A specified and unique sub group of the available uncorrelated sequence sets would be assigned to each independent user,thus allowing simultaneous access to the channel by all users,without detrimental interaction.This project discussed data transmission using uncorrelated complementary sequence Sets using the Code Division Multiple Access (CDMA) technology.Multiuser CDMA communication System models are discuss with the use of Mat-Lab simulink and have investigate the performance of multi user effects under different channel characteristics and system performance are measure on the basis of Bit Error Rate (BER) performance with respect to SNR.

TABLE OF CONTENTS

DECLARATION STATEMENT i

ABSTRACT i

ACKNOWLEDGEMENTS ii

TABLE OF CONTENTS iii

LIST OF FIGURES vi

GLOSSARY vii

CHAPTER 1 8

1.1 Introduction 8

1.2 Aim and Objectives 9

1.2.1 Aim 9

1.2.2 Objectives 9

1.3 Organization of the Project Report 9

CHAPTER 2 11

Background Theory 11

2.1 Mobile Communication System 11

2.1.1 CDMA technology and Standards 11

CDMA System Standards 11

2.1.2 Spread Spectrum 12

2.2 Mobile Channel Characteristics 12

2.2.1 Additive White Gaussian Noise( AWGN ) channel 12

2.2.2 Multipath Propagation 13

2.2.3 Multiple Access Interference 13

2.3 Modulation and Demodulation 14

2.3.1 QPSK 14

2.4 Complementary Sequences 15

CHAPTER 3 16

Introduction to Complementary Sequences 16

3.1 History 16

3.2 Complementary Sequences (CS) 16

3.2.1 Definition 16

3.2.2 Properties of complementary sequence sets 16

3.2.3 Aperiodic autocorrelation property of the CS set 17

3.2.4 Aperoidic Cross Correlation property of CS set 18

3.3 Complementary sequence set synthesis 18

3.3.1 Binary complementary sequence pairs 18

3.3.2 Extended binary complementary sequence set of even length 20

3.4 Multi-level complementary sequence set synthesis

21

3.4.1 Synthesis of Multilevel CS sets with odd dimensions 22

3.4.2 Sequence compression 22

CHAPTER 4 23

Project Implementation 23

4.1 Software Tool 23

4.2 System design 24

4.2.1 Model of Transmitter 25

4.1 Data Encoding 25

4.1.1 CS Sets generation 25

5.2.2 Simulation of Transmitter 28

The following parameter were set to the QPSK modulation block 29

Multiplexer 30

5.2.3 Model of Channel 30

5.2.4 Model of Receiver 31

5.2.5 Simulation of Receiver 32

QPSK Demodulation 32

Despreading 32

5.2.5 Bit Error Rate Calculation 32

CHAPTER 6 32

Results And Analysis 32

6.1 Input Data 33

6.2 Complementary Sequence Code 33

6.3 Scope output comparison 33

6.4 Modulation and Demodulation 34

6.5 System performance analysis 36

6.5.1 Single user communication system 36

6.5.2 Two user system with 2Ã-2 CS set 37

6.5.3 Multi-User comparison 38

CHAPTER 7 39

Conclusion and Future development 39

7.1 Conclusion 39

7.3 Future development 40

References 41

BIBLIOGRAPHY 43

APPENDICES 44

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LIST OF FIGURES

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GLOSSARY

CS........................Complementary Sequence

AWGN...................Additive White Gaussian Noise

BER......................Bit Error Rate

CDMA...................Code Division Multiple Access

ACF......................Autocorrelation Function

CCF......................Crosscorrelation Function

CCK......................Complementary Code Keying

AWGN...................Additive White Gaussian Noise

SNR..................... Signal to Noise Ratio

QPSK.................. Quadrature Phast Shift Keying

CHAPTER 1

1.1 Introduction

Mobile communication is one of the biggest segments of information technology and every technology fore­caster predicts a seemingly endless growth. Why is it that mobile communication now seems to be an essential part of society, and with an equally strong market demand for both personal users and professional users[8].In this statue digital techniques are widely implemented in these new systems to provide the new data-based services to support more mobile users with much faster speed communication at lower error rate.

The main objective of the project is to provide multi-user communication bases on Code Division Multiple Access(CDMA).Multi-user communication can be effected by means of uncorrelated sets of Complementary sequences.A specified and unique sub group of the available uncorrelated sequence sets would be assigned to each independent user,thus allowing simultaneous access to the channel by all users,without detrimental interaction.It is emphasized that Complementary Sequence is used in the digital transmission of the binary signal. The CS set is chosen in encoding of the binary data for transmission because of its special unique characteristics such as have an impulsive auto-correlation function (ACF), CS code has greater immunity to noise, it has good performance in error control and security in coding. CS code has been implemented on the spread spectrum as a coding method to spread the data.In this status complementary sequence is introduced to develop an optimum architecture with the function of spreading the energy of a given bit of information uniformly over the channel bandwidth.

Communication System are discussed with the use of MatLab simulink models. Multi-user CDMA communication model has been designed to investigate the performance of multi user effects under different channel characteristics. The Additive white Gaussian Noise is taken in to consideration when designing the models.

For the Simulink models, simulation results have been presented and analysed under different operating environments. The analysis are done in multi user operating conditions for the CDMA and different length of CS sets. The effects of the system under propagation through AWGN are considered for the CDMA model.

The performance of the system has been carried out by plotting analytical graphs using the Matlab and assess the system performance on the basis of Bit Error Rate (BER) performance as a function of Signal to Noise Ratio(SNR).The Scope results and Scatter plot results of different sections are discussed and have been compared with the expected results.

1.2 Aim and Objectives

1.2.1 Aim

The aim of this project is to design various multi-user modem configurations based upon complementary sequences (CSs) with a view to developing an optimum architecture for mobile communications.This involves developing a complete communication system simulation in Matlab software.

1.2.2 Objectives

Study whole mobile communication system

Select the most suitable technology to deploy and identify the system configurations.

Investigates Modulation, Demodulations and Encoding used in a modem

According to the selection of suitable technology study about their technical details, modulation methods and Encoding techniques.

Investigates creation of different Complementary Sequences sets

Transmitter and Receiver design using Matlab simulink

Design transmitter and receiver model with the encoding and decoding of complementary sequences.

Define and analyzing the different stages performances (such as BER and SNR) will be carried out and analytical graphs will be drawn using the Matlab

1.3 Organization of the Project Report

CHAPTER 1

This Chapter discussed basically about the introduction to the project. It also covers the aims & objectives that were planned for the project.

CHAPTER 2

This chapter consists of the history & development of mobile communication systems. 1st ,2nd ,3rd generation systems,CDMA Standards and Mobile channel characteristics have been discussed and briefly explained spread spectrum.

CHAPTER 3

Describe history and advantages of Complementary Sequences, and its properties and types. It also deals with generation and manipulation of CS sets.

CHAPTER 4

Describes the system model of the project with explanations the main characteristics of the simulated system. This chapter also deal with the models of the system such as Transmitter, channel. Receiver and the theoretical performance of the system.

CHAPTER 5

Set out the results and analytical graphs of the system simulation and detailed discussion which aspect affect the system to get the different results compared with the theoretical performances.

CHAPTER 6

Conclusions are drawn and recommendations for future work and possible improvement are suggested. It shows the problems encountered throughout the project.

CHAPTER 2

Background Theory

2.1 Mobile Communication System

Mobile communication is a faster developing technology since 1980, continuously and strongly from year to year. While the first generation systems in the 80's were mostly based on analog technologies. The disadvantages of Analog systems were overcome by second (2G) generation of mobile communication systems which represent data digitally. The first commercial deployment of 2G system called GSM was made in 1992. After that other 2G system also known as CDMAone (IS-95) was standardized. Upgrade to 2G systems offering higher data speeds called 2.5G systems was developed. GSM has two such technologies called High Speed Circuit Switched Data (HSCSD) and General Packet Radio Service (GPRS). Similarly in CDMA an extension of IS-95 known as IS-95B or CDMATwo was developed. To meet the future bandwidth requirements 3G cellular systems were standardized. The different 3G standards evolved include EDGE, CDMA2000 and WCDMA.In this project CDMA standards have been considered [9].

2.1.1 CDMA technology and Standards

Code Division Multiple Access (CDMA) is a important concept in wireless com­munications. CDMA has now gained huge international acceptance by mobile radio system operators as an upgrade both system capacity and the service quality.This CDMA2000 standards provides a migration path from the original cdmaOne system through the CDMA2000 1X format to further high speed formats. Following chart show the three CDMA Standards.

CDMAone

CDMA2000

WCDMA

Assigned Spectrum(MHz)

869-894

869-894

2110-2170

Voice Users

15-50

15-100

196(max)

Data services

2G

3G

3G

Normal data rate

14.4k

307.2k

936k

Ideal coditions

114k

2M

4M

Typical Mode

FDD/CDMA

FDD/CDMA

FDD/CDMA

Chip rate

1.2288

1.2288

3.84

Modulation

QPSK

QPSK/OQPSK

QPSKCDMA System Standards

Table 2.1 CDMA Standards[]

2.1.2 Spread Spectrum

In CDMA communication systems, spread spectrum a means of signal modulation, in which the information signal is spread over a very wide bandwidth. This band spread is achieved by means of a code which is independent of data. A code synchronized reception at the receiver is used for despreading the subsequent data recovery. Spread spectrum has moved into the commercial market place due to some of it important charac­teristics, from providing hidden communications and protection against enemy fabrication and jamming in military systems,. The main arguments have been reduced effects of multipath, interference rejection and, specially, multiple-access capabilities[8].

Figure 2.1 Spread spectrum[8]

2.2 Mobile Channel Characteristics

2.2.1 Additive White Gaussian Noise( AWGN ) channel

The Additive White Gaussian Noise( AWGN ) channel is commonly used to model an environment with a very large number of additive noise sources. Most additive noise sources on modern electronics are a direct consequence of zero-mean thermal noise, which is caused by random electron motion within conductors and devices at the front end of the receiver [10].

r (t) = s (t) + n (t) Equation 2.1

r(t)

s(t)

n(t)

Figure 2.2 Noise addition of the signal

2.2.2 Multipath Propagation

In a mobile mobile radio environment, the surrounding objects, such as building and trees, act as reflectors of radio waves. These obstacles causes to produce reflected waves with attenuated amplitudes and phases. If a modulated signal is transmitted, multiple reflected waves of the transmitted signal will arrive at the receiving antenna from different directions with different propagation delays. These reflected waves are known as multipath waves . As a results to the different arrival angles and times, the multipath waves at the receiver have different phases. When data received to the receiver antenna at any point in space they may combine either in a constructive or a destructive way depending on the random phases.

The mobile unit moving through the multipath field will receive a signal which can vary widely in amplitude and phase. When the mobile unit is stationary, the amplitude variations in the received signal arc due to the movement of surrounding objects in the radio channel. This amplitude fluctuation of the received signal is called signal fading. It is caused by the time-variant multipath characteristics of the channel.[11]

Figure 2.3 Multipath propagation

2.2.3 Multiple Access Interference

All mobile communication systems suffer from co-channel interference. In cellular CDMA, users share the same frequency band with different spreading codes. Therefore, their reverse link transmissions interfere with one another resulting in the near-far effect, making it difficult to recover the weaker users.

2.3 Modulation and Demodulation

The modulation process can be described as impressing an information-carrying signal (the modulating or source signal) on a carrier, thereby varying certain characteristics of the carrier to represent the source information in the modulated signal. In a digital communication system, the purpose of modulation is to convert the symbols to waveforms that are compatible with the transmission channel so that good transmission efficiency is achieved. The demodulation process is the recovery of the original information from the modulated signal, but demodulation also includes the reduction of noise, interference, and distortion incurred in the channel.[13]

2.3.1 QPSK

Quadrature Phast Shift Keying (QPSK) is an M-ary constant-amplitude digital-modulation scheme in which the number of bits is two and number of signaling elements is four. Each signaling element is represented by two bits. This can carry twice as much data in the same bandwidth as can a single-bit system, provided the SNR is high enough. Accordingly. QPSK has 4 different phase shifts as shown in figure, sepa­rated by multiples of π/2 or 90° of the carrier signal.

2.4 Phase plane of QPSK

2.4 Complementary Sequences

In this project, it is emphasized that Complementary Sequence is used in the digital transmission ot the binary signal. The CS set is chosen in encoding of the binary data for transmission because of its special unique characteristics. The definition and characteristics are discussed further in chapter 3 as well as the implementation of CS. Because of its important characteristic such as have an impulsive auto-correlation function (ACF), CS code has greater immunity to noise, it have good performance in error control and security in coding. CS code has been implemented on the spread spectrum as a coding method to spread the data[2]. CS sets have great potential in the context of a multi-function communication system design in that they are able to provide a range of important functions simultaneously. Sequence lengths and numbers, ie CS set dimensions, together with power level and clock rate, could be adapted in response to channel state.[3]

CHAPTER 3

Introduction to Complementary Sequences

3.1 History

Binary complementary sequences, also known as complementary code pairs, were proposed by M.J.E. Golay at the turn of 60's. Thereafter the idea of complementarity has been studied further, and also been extended for non-binary sequences by several authors.Complementary sequences have been found useful in multislit spectrometry, radar and spread-spectrum (SS) applications because of their excellent correlation and pseudorandom properties.[6]

3.2 Complementary Sequences (CS)

3.2.1 Definition

A set of complementary sequences is defined as a pair of equally long, finite sequences of two kinds of elements which have the property that the number of pairs of like elements with any given separation in one sequence is equal to the number of pairs of unlike elements with the same separation in the other sequence[6].

The example,the two binary sequences shown below are complementary,and the digit seperations indicated;

Sequence 1: +1 +1 -1 +1

Like Unlike

Sequence 2: +1 -1 -1 -1

Unlike Like

Figure 3.1 Like and unlike elements

3.2.2 Properties of complementary sequence sets

It is described by Darnell & Kemp as;

a) They should be completely deterministic

b) They should comprise discrete real states to enable them to be mapped to discrete-level modulation schemes.

c) They should have 2-valued auto-correlation functions.

d) Sets of sequences should have low crosscorrelation functions between members of the set

e) The correlation properties specified in (C) and (D) above should apply for both aperiodic and periodic sequence generation.

3.2.3 Aperiodic autocorrelation property of the CS set

Above like and unlike property leads directly to the characteristics form of the summed aperiodic ACF for CSs,ie that the sum of the two individual sequence aperiodic ACFs is everywhere zero,except at the zero-delay(in phase)positin where it takes the value 2N,N being the number of digits in each sequence.

This can observe from the bellow example.

Let CS pairs as;

Sequence A: +1 +1 -1 +1

Sequence B: +1 -1 -1 -1

Position : 0 1 2 3

The auto correlation function can be calculated for two sequences as follows,

For Sequence A

0 0 0 0 0

+ + - +

0 0 0 0 0

Reference

+ + - +

@ Delays -4T ACF= 0

+ + -

+

@ Delays -3T ACF= +1

+ +

- +

@ Delays -2T ACF= 0

+

+ - +

@ Delays -T ACF= -1

+ + - +

@ Delays 0 ACF= +4

+ + -

+

@ Delays T ACF= -1

+ +

- +

@ Delays 2T ACF= 0

+

+ - +

@ Delays 3T ACF= +1

+ + - +

@ Delays 4T ACF= 0

Figure 3.2 Evaluation of ACF

For Sequence B

0 0 0 0 0

+ - - -

0 0 0 0 0

Reference

+ - - -

@ Delays -4T ACF= 0

+ - -

-

@ Delays -3T ACF= -1

+ -

- -

@ Delays -2T ACF= 0

+

- - -

@ Delays -T ACF= +1

+ - - -

@ Delays 0 ACF= +4

+ - -

-

@ Delays T ACF= +1

+ -

- -

@ Delays 2T ACF= 0

+

- - -

@ Delays 3T ACF= -1

+ - - -

@ Delays 4T ACF= 0

Figure 3.3 Evaluation of CCF

Assuming that the sequences are locked with interval T, caculate the aperiodic ACFs for the sequences of the expressions above:

Delay : -3T -2T -IT 0 T 2T 3T

Sequence A ACF: +1 0 -1 +4 -1 0 +1

Sequence B ACF: -1 0 +1 +4 +1 0 -1

Summed ACF: 0 0 0 +8 0 0 0

The cross correlation between individual sequences in a CS set is non-zero accept zero at delay T=0. Auto correlation property of the complementary sequence set is exploited in the most its practical applications by carefully avoiding its cross correlation property.

3.2.4 Aperoidic Cross Correlation property of CS set

Another property of the complementary sequence set is its uncorrelated sets of sequences. Summed cross correlation of the two different sequences with each other would result in to zero at all the delay positions. The sets are called uncorrelated in the complementary sense.

Consider the two pairs of binary CSs shown below;

Sequence 1: +1 -1 -1 +1 +1 +1 +1 -1

Sequence 2: +1 -1 -1 -1 +1 -1 +1 +1

Set A Set B

The overall aperiodic CCF between sets A and B is given by the sum of the aperiodic CCF's between corresponding sequences in each of the sets calculation is shown in figure, evaluated equivalent delays.

Sequence 1 CCF: +1 0 +1 0 +3 0 -1

Sequence 2 CCF: -1 0 -1 0 -3 0 +1

Summed CCF : 0 0 0 0 0 0 0

Delay : -3T -2T -T 0 T 2T 3T

Since the summed aperiodic CCF value in the above example is zero at all delay positions.

3.3 Complementary sequence set synthesis

3.3.1 Binary complementary sequence pairs

When consider the multi-user communication system simulation which uses complementary sequence set to encode the message signal, one of the most important part is how to generate the adaptive CS sets. There are many methods available to generate the complementary sequence sets. The most old and easy methods are the block structure method and recursive CS algorithm.

Starting with pair of binary CS sets of length 2n (n integer> synthesised by dividing them in block structures.

+A

+B

+B

-A

Figure 3.4 Block Structure 1

Where the arrows indicate the sense in which all the digits in a given block should be read;a 'block' is half the sequence length , ie 2n-1 digits.The sysnthesis procedure is recursive for generating larger sequences.

Following example showing recursion method starting with arbitrary initial binary elements

+A = +1

+B = +1

+1

+1

+1

-1Hense

1st recursion (n=l)

+1

+1

-1

+1

+1

-1

-1

-12nd recursion (n=2)

+1

+1

-1

+1

-1

-1

-1

+1

+1

-1

-1

-1

-1

+1

-1

-1

3rd recursion(n=3)

Figure 3.5 Recursive method

It can be seen that ,for n ≥ 2,the first halves of thesequences obtained from the n th recursion are themselves the pair of sequences generated by the(n-1)th recursion.In similar way ,the second half of any pair of sequences also form a complementary pair.And the 'first-half pair' and the 'second-half pair' are uncorrelated in a complementary sense.

In addition to above, following block structures can be used to synthesise CS pairs

+A

-B

+B

+A

Figure 3.6 Block structure 2

Inverse block structures

+A

-B

-B

+A

-A

+B

-B

-A

Figure 3.7 Inverse block structures

3.3.2 Extended binary complementary sequence set of even length

Previous recursive synthesis procedure described simply generates pair of binary sequences.By allowing A and B to be defined as CS sets,rather than sequences,the same block structures can yeild generalized sets of CSs in which there are more than two sequences per set.Redefining the CS by taking the 2-bit sequences using the following base block structure,

Sequence 1 : +1 +1 A and B

Sequence 2 : +1 -1

The block structure gives 4Ã-4 CS set,

Seq 1: +1 +1 +1 +1

Seq 2: +1 -1 +1 -1

Seq 3: +1 +1 -1 -1

Seq 4: -1 +1 +1 -1

Again this procedure can be used to produce (8Ã-8), (16Ã-16), etc. CS sets and by taking Seq 1 and Seq 2 as A and B non square CS sets can be generated, in which sequence length is greater than the number of sequences in the set.

3.4 Multi-level complementary sequence set synthesis

Previous discussed complementary sequences sets are binary and further it can be used for multi-level(>2) CS sets synthesis.

As an example ,if A and B are now arbitrarily chosen to be as follows;

A= +1

B= +2

using block structure 1 of figure 3.3.1, following non binary CS pair results

Seq 1 +1 +2

Seq 2 +2 -1

Again above CS set can be used for synthesis 4Ã-4 multi-level CS to set using recursion of block structure 1.

Seq 1:

+A

+B

+A

+B

Seq 2:

+B

-A

+B

-A

Seq 3:

+A

+B

-A

-B

Seq 4:

+B

-A

-B

+A

Seq 1:

-1

+2

-1

+2

Seq 2:

+2

-1

+2

-1

Seq 3:

+1

+2

-1

-2

Seq 4:

+2

-1

-2

+1

(a) (b)

Figure 3.8 4Ã-4 Multi level CS Sets

If sequences are needed in specific ways by taking the values for A and B as 1,2,3,4

A = +1 and A = +3

B = +2 B = +4

Block structure 1 and 2 respectively gives

Seq 1 :

+1

+2

and

+3

-4

Seq 2 :

+2

-1

+4

+3

(a ) (b)

When taken each CS sets individually they are complementary.It should be noted however,that they are not uncorrelated in a complementary sense;this is becouse the digit weighting in the two sets are different.

Seq 1:

+1

+2

+3

-4

Seq 2:

+2

-1

+4

+3

Seq 3:

+3

-4

-1

-2

Seq 4:

+4

+3

-2

+1

Figure 3.9 Multi level structure

3.4.1 Synthesis of Multilevel CS sets with odd dimensions

To synthesis CS sets of odd length sequences,the truncation and partitioning techniques can be used.For example ,the figure 3.9 structure provide the following sets.

+1

+2

+3

-4

+1

+2

+3

+2

+3

-4

+2

-1

+4

+3

+2

-1

+4

-1

+4

+3

+3

-4

-1

-2

+3

-4

-1

-4

-1

-2

+4

+3

-2

+1

+4

+3

-2

+3

-2

+1

(a)

(b)

(c)

(d)

Figure 3.10 Truncation and Partioning

3.4.2 Sequence compression

Another way to produce odd length CS sets is by sequence compression, where two complementary and uncorrelated sets and adding column of zeros at the end and the beginning of each of the sequence sets respectively. When A= +1 and B =+2,From block structure 1 and 2 above gives,

Seq 1

+1

+2

and

+1

-2

Seq 2

+2

-1

+2

+1

(a)

(b)

Column of zeros is added to each of the sequence sets as shown below

Seq 1:

+1

+2

0

and

0

+1

-2

Seq 2:

+2

-1

0

0

+2

+1

(a)

(b)

CHAPTER 4

Project Implementation

In this chapter the Communication System models are discussed with the use of Mat-Lab simulink models. The assumptions and design features are described one by one in the below sections. A basic CDMA communication model is design to investigate the performance of multi user effects under different channel characteristics. The Additive white Gaussian Noise is taken in to consideration when designing the models.

4.1 Software Tool

MATLAB is a high performance language for technical computing. It integrates computation, visualization, and programming environment. Furthermore, MATLAB is a modern programming language environment.It has sophisticated data structures, contains built in editing and debugging tools, and supports object oriented programming. These factors make MATLAB an excellent tool for simulation of this Communication system[12].

4.2 System design

The main aim ot the implementation is to simulate a multiuser communication system, which transmits digital data over mobile communication channel. The coding method chosen was Complementary Sequence Sets (CSs).

User 1

User 2

User 1

User 2

Channel

Encoder

CS Code

Channel

Decoder

Demodulator

Modulator

Channel

CS Code

Figure 4.1 Block diagram of communication system

It can be seen from the block diagram above, the whole system is consisted of four individual parts. The top blocks buildup the transmitter part including information source, CS generator,Channel encode, and the modulation. The next block which is Gaussian While noise simulate the wireless channel. The blocks at the bottom comprise the receiver part with the sub-block demodulation,channel decoding and received information. In addition, the last block which is called BER calculation is the part for data analysis.

4.2.1 Model of Transmitter

Primarily,in transmitter the groups of message signal are generated in the information source block.In order to achieve the multiuser transmission, the messages are multiplied with the uncorrelated set of complementary sequences.

The following figure shows the block diagram of the transmitter,

User 1

User 2

Modulator

CS Code 1

CS Code 2

Modulator

∑

Figure 5.2.1 Block diagram of transmitter

4.1 Data Encoding

4.1.1 CS Sets generation

For multi-user communication system, uncorrelated complementary sequence sets need to select according to the number of users and system requirements.

Sequence selection shows in below example

2 Users - 2Ã-2 CS set

3 Users - 3Ã-3 CS set

In this system number of rows in the complemantary sequence and users should be same.Bearing in mind, it has the same dimensions as the encoded data of every user for the transmission of data using CS sets.

In general, information bit '0' considere as '-1' and '1' bit cosider as '+1' when data communicate using complementary sequences.Following examples demonstrate the encoded and Original random data and complementary sequences sets of various sizes.

2Ã-2 Set

+1

+1

+1

- 1

-1

-1

-1

+1

Original Sequence Encoded Siquence

2Ã-4 Set

+1

+1

-1

+1

+1

-1

-1

-1

-1

-1

+1

-1

-1

+1

+1

+1

Original Sequence Encoded Sequence

+1

+1

+1

+1

+1

-1

+1

-1

+1

+1

-1

-1

-1

+1

+1

-14Ã-4 Set

+1

+1

+1

+1

+1

-1

+1

-1

+1

+1

-1

-1

-1

+1

+1

-1

Original Sequence Encoded Sequence

Figure 4.1.1 Encoding of different CS sets

Consider the transmitter with two users, the information bit coming from each user are "1" and "-1" for user A and user B respectively. However, the CS spreading code of the two users are in below figure.

+1

+1

+1

-1

User A

+1

+1

-1

+1

User B

Figure 4.1.2 User A and B CS sets

+1

+1

+1

-1

-1

-1

-1

+1 +1

Ã-

User A

+1

+1

-1

+1

-1

-1

+1

-1

-1

Ã-

User B

4.1.3 Encoding operation of user data

In multi user communication system, the each bit of individual user data stream Xs(t) being transmitted is multiplied with an uncorrected set of a complementary sequence, and the output from the transmitter part is given by,

Es(t) = Xs(t) X Cr(t) Equatioin 4.1.1

5.2.2 Simulation of Transmitter

The simulation of the communication model was done using the MATLAB ready-made blocks, representing all the aspects of a communications system. Then,the parameters of these blocks,were set to according to the needs of the design.

Figure 5.2.2 Simulation of transmitter

Bernoulli Binary Generator

Bernoulli Binary Generator block was used to generate the different random binary user transmitting data streams. The following parameters were set, according to the requirements.

Probability of zero - 0.5 (By changing this value different data streams were set for each user)

Initial seeds - 61

Sample time -1/2000

Out put type -Frame based output

CS code Subsystem

To generate 2Ã-2 complementary sequence code three 'Matrix concatinate' blocks were used. Constant +1 and -1 values have been inputed into the two Matrix concatinate blocks as shown in following figure and those two blocks have sent through another Matrix concatinate block. Different dimensions of CS sets can be create by arranging this block in appropriate way.

Figure 5.2.3 blocks of CS code generation

Spreading

XOR logical operator has been used for multiply two inputs of information data stream and generated complementary CS code.

QPSK Modulation

The QPSK Modulator Baseband block modulates using the quaternary phase shift keying method. The output is a baseband representation of the modulated signal[11].QPSK maps the bit sequence being transmitted to a symbol sequence, the elements of which consist of an alphabet of four different symbols. In the signal transmission a modulation symbol corresponds to exactly one of the four possible phase positions of the carrier wave.[12]

The following parameter were set to the QPSK modulation block

Phase offset(rad) :pi/4

Constellation ordering :Binary

Input type :Bit

If Input type parameter has been set to Bit, then the input contains pairs of binary values. If the Phase offset parameter is set to pi/4, then the block uses one of the signal constellations in the following figure, depending on whether the Constellation ordering parameter is set to Binary or Gray.[11]

Figure 5.2.4 QPSK Signal Costellation[11]

Multiplexer

The modulated data of each user should transmit through a single channel.Therefore Multiplexer has been used for sending multiple signals of information on a carrier at the same time in the form of a single, complex signal and then recovering the separate signals at the receiving end.'Mux' block used for the multi-user simulink and number of inputs were set to'2' for the two user syste.

5.2.3 Model of Channel

r(t)

s(t)

∑

n(t)

Figure 5.2.4 Model of noisy channel

AWGN channel was used for the simulate the channel and Mode set to 'Signal To Noise Ratio(SNR)'. This block supports multichannel input and output signals as well as frame-based processing.

Figure 5.2.5 AWGN Channel

This block supports multichannel input and output signals as well as frame-based processing and following settings have been applied for the block.

Initial seeds :67

Mode :Signal to Noise Ratio(SNR)

SNR value was changed for the block for different stages to analyse the performance of the system.

5.2.4 Model of Receiver

After the demodulation, the signal is decoded using the correlation properties of the CS using despreading the signal. In the following sections the basic parts have been described in detail.

Despreader

CS Code 1

CS Code 2

∑

Demodulator

Demodulator

User 1 Data

User 1 Data

Figure 5.2.4 Block diagram of receiver

5.2.5 Simulation of Receiver

Figure 5.2.5 Simulation of receiver

QPSK Demodulation

In this simulation QPSK Demodulator baseband block demodulates a signal that was modulated using the quaternary phase shift keying method at the transmitter. The input is a baseband representation of the modulated signal[11]

Despreading

The demodulated signal signal decoded at the receiver using the Despreader by multiplying same CS code which encoded at the transmitter.

5.2.5 Bit Error Rate Calculation

The last stage of the simulation were the analyze the performance of the system.To do that BER has been calculated using the BER.This can be evaluated using the following formular,

BER = Error in bits Equation 5.1

Total Transmitted bits

For this system BER were calculated by increasing the SNR of the AWGN channel 2 dBm steps starting from 0 by and SNR vs BER graph plotted using MATLAB.

CHAPTER 6

Results And Analysis

In this chapter the Simulink models simulation results under different operating environments are presented and analysed. The analyses are done in multi user operating conditions for the CDMA and different length of CS sets. The effects of the system under propagation through AWGN are considered for the CDMA model. The Scope results and Scatter plot results of different sections are discussed and compared with the expected results.

6.1 Input Data

Input data stream was generated by using the Bernoulli Binary Generator and output bit stream can be shown in figure 6.1.1.Due to used frame based out put and four samples per frame, it displayed four different colours.Probability of zero value has changed to generate different bit streams.

Figure 6.1.1 Input Data Stream of Scop

6.2 Complementary Sequence Code

Complementary sequence code was generated using CS subsystem and observed output of varies with +1 and -1.The scop output can be shown as follows,

Figure 6.2.1 Complementary sequence code

6.3 Scope output comparison

Spreading code and the modulated signal is combined at the spreader to create the spread signal. Combiner uses XOR function to combine the two signals.Inversion of the data bit was done by XOR logic operation.Observed graphs at Bernoulli Binary Generator,CS code output,Spreader output and despreader output scop shown in below.

Figure 6.3.1 Scop output of signals

From above four graph it can be observed transmitted signal,CS code,spreaded signal and received signal respectively. Spreaded signal has been obtained multiplying the transmitted signal and CS code at the transmitter.Same transmitted signal has been received at the receiver by taking XOR operation with the encoded signal and CS code used at the transmitter.The signals shows the different colours becouse Bernoulli Binary Generator output is frame based and used four samples per frame.

6.4 Modulation and Demodulation

QPSK (Quadrature Phase Shift Keying) was used to modulate the signal as mention in the previous chapter the symbols are created with a pi/4 phase shift. The observed diagram shows in figure 6.4.1. This is similar to the theoretical diagram of QPSK.

Figure 6.4.1 Constellation diagram after modulation

The modulated signal after go through AWGN channel following constellation diagram were observed. Here the point are seem random and is impossible to work out the symbols transmitted. This proves that after spreading the signal inherits certain characteristics that make it immune to outside treats.

Figure 6.4.2 Constellation diagram after go through the channel

6.5 System performance analysis

System performance have been analyzed for single user,two users,and four users.The recorded results have analyzed and performance graphs plotted for different simulations.

6.5.1 Single user communication system

Before simulate the multi-user system single user system has been implemented and 2Ã-2 complementary sequence has been used.The simulation of this system included in Appendix ii. In this simulation, When increase the SNR of the AWGN channel 2dB steps starting from 0 the BER values are gatherd and accoding to that following graph were obtained.

Figure 6.5.1 SNR vs BER of Single user CDMA system

It is clearly observed from the graph, BER is reducing when increase the SNR of the AWGN channel.The SNR exceed the 12dB it could be observed the BER become zero.However,the performance curve is very similar to the theoritical curve and hence the system is working in a manner which have expected.And it was able to seen that BER value is zero when SNR set to infinity or without a AWGN channel.

6.5.2 Two user system with 2Ã-2 CS set

In this simulation was implemented for two users assigning uncorrelated 2Ã-2 complementary sequence set as described in previous chapter.BER values are gathered for each users respectively and both curves have been plotted in a same graph.

6.5.2 SNR vs BER for two user system

It is clearly observed BER is reducing when increase the SNR of the AWGN channel in this graph as well.Both curves for each user have been plotted and this also similar to the theoritical graph.For two user system observed BER is greater than the single user system and BER become zero for both users SNR exceed 12dB.

Following figure observed by adding BER rate of both user and this shows total performance of the two user system.

Figure 6.5.3 SNR vs BER graph for two user system

6.5.3 Multi-User comparison

Figure 6.5.4 shows the SNR vs BER curves for the three different users red,blue and green for 1 user,2 users and 3 users respectively.

Figure 6.5.4 BER vs SNR curve for multi-user comparison

It could be observed when increase the SNR of the channel BER going down for all performance curves.On the other hand when number of users increasing BER value for particular SNR value is higher.And those curves behave similar to the theoritical performance graphs and therefore.system performance is good.

CHAPTER 7

Conclusion and Future development

7.1 Conclusion

The aim of this project was to design various multi-user modem configurations based upon complementary sequences (CSs) with a view to developing an optimum architecture for mobile communications.This has been involved to developing a complete communication system simulation in Matlab software.Multi-user CDMA communication model has been designed to investigate the performance of multi user effects under different channel characteristics and simulation results have been presented and analysed under different operating environments in chapter 6.

Complementary sequence code produced an efficient coding in spread spectrum. Although the communication system models have been implemented for a several users with the use of different CS sets, it could shows that CS code offers a unique form of combating noise by increasing the gain of transmitted data.Because of the unique and special code of CS set, it had provided nearly perfect coding and decoding method and security. Complementary sequence set has provided a higher gain against other code that has been used in digital communications, due to its perfect correlation properties and synchronization characteristics.

According to analysis of the performance curves,It is clearly observed,BER is reducing when increase the SNR of the AWGN channel. However,the performance curve is very similar to the theoritical curve and hence it can be concluded that the system has good performance which have expected.

Effect of the system, when increase the number of users have been analyzed for two and four users and the BER for particular SNR value observed higher than the single user system.Hence can be conclude that when increases the number of users the performance of the system has been degraded.

It is important to mentioned problems encounted during the project.There were several errors when doing the simulation in Matlab.The biggest problem was occured when generating complementary sequence code at the encoder.Though,expected scop output of the CS code generator is digital signal pulse varying in +1 and -1 amplitudes the resultant scop out was the straight line in the +1 and -1 value.

7.3 Future development

In this project, it is emphasized that Complementary Sequence is used in the digital transmission of the binary signal. The CS set is chosen in encoding of the binary data for transmission because of its special unique characteristics.For future development for the project different dimensions of complemenatary sequence can be applied.Other than multi-level complementary sequence can be used with more than two integer digit states.

Further, system can be implemented together with AWGN, multipath fading and Rayleigh fading channel in order to realize the real world channel condition. Different modulation scheme such as BPSK, DPSK also can be applied in order to see the difference in the performance of the system.

To analyses the performance of the system number of user can be increase higher(ex.1000) using different length of sequences.

Another suggestion for future development can be considered encoded modulation using Multiphase CS code called Complementary Code Keying (CCK). This type of modulation was recommended by the IEEE for Wireless Personal Area Network (W PAN) and Wireless Local Area Network (W LAN) for it can reach a very high data rates of 10Mbps.

When consider this project same objects can be achieve using C++ programming language.

References

[2] Chew, Y, C. (September, 2002) Adaptive binary CS set modem over non-Gaussian channels, university of Hertfordshire

[3] Darnell, M. & Kemp, A. R, (1992) Multi Complementary Sequence Sets: Synthe: Applications, University of Hull. |

[04] CDMA capacity and quality optimization

 By Adam N. Rosenberg, Sid Kemp

[5] IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. IT-18, NO. 5, SEPTEMBER 1972

Complementary Sets of Sequences

C.-C. TSENG, SENIOR MEMBER, IEEE, AND C. LIU

[6] LINEAR EQUIVALENCE OF BINARY GOLAY COMPLEMENTARY SEQUENCES

Kari H. A. Karkkainen and Pentti A. Leppanen

University of Oulu, Department of Electrical Engineering, Telecommunication Laboratory

Linnanmaa SI, P.O. Box 444, 90571 Oulu, Finlandl

[7] Multilevel complementary sequence sets:synthesis and applications

M.Darnell and A.H.Kemp 1992

University of Hull

[8] Spread spectrum in mobile communication

 By Olav Berg, Institution of Electrical Engineers

[9] Mobile Communication Technologies

Jehadeesan R. and Rajan J.

[10]"Communication System" by Simon Haykin, 4th Edition.

[11]MATLAB help

[12] UMTS: the fundamentals

 By Bernhard Walke, Peter Seidenberg, Marc Peter Althoff

[11] Space-time coding

 By Branka Vucetic, Jinhong Yuan

[12] INTRODUCTION TO MATLAB FOR ENGINEERING STUDENTS

David Houcque - Northwestern University (version 1.2, August 2005)

[13] Mobile communications

 By Hideichi Sasaoka

[14] Wireless Communications

 By Singal

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