Simulation Study Of Error In Image Transmission Over 3G Systems Using Different Types Of Error Control Codes

ABSTRACT

Error control codes are widely used in almost all digital systems as they provide a method for dealing with the unknown like noise. This research investigated the role of reducing error rates in image transmission over 3G systems using convolutional coding technique in MATLAB. The error correction code employed was the convolutional error correction codes. The performance of the codes are evaluated based on key performance indicators like Bit Error Rate (BER), number of symbols or data compared and number of errors detected. For the verification of proposed approach, computer simulation results are included. The results show a comparison of the performance terms of their Bit Error Rate (BER) of convolutional code with different code rate ( ½  and ¹/₃) used. Based on the results, between 60% and 65% improvement on coding was achieved between reference points of 10^-2 and 10^-4 respectively for the two code rates. The results also show that as the Bit Error Rate (BER) decreased, the coded system can transmit data signals with at least 3dB less power, so making the performance of the coded system better than the uncoded system.

TABLE OF CONTENTS

COVER PAGE

TITLE PAGE

APPROVAL PAGE

DEDICATION

ACKNOWLEDGEMENT

ABSTRACT

CHAPTER ONE

1.0     INTRODUCTION

1.1     BACKGROUND OF THE STUDY

• OBJECTIVE OF THE STUDY
• SCOPE OF THE STUDY
• PROJECT MOTIVATION
• SIGNIFICANCE OF THE PROJECT

CHAPTER TWO

LITERATURE REVIEW

• INTRODUCTION
• DEFINITION OF AN ERROR
• TYPES OF ERRORS
• TYPES OF ERROR DETECTION

CHAPTER THREE

3.0     METHODOLOGY

3.1      FORMULATION OF AN EXPERIMENTAL SIMULATION MODEL

CHAPTER FOUR

4.1     DATA ANALYSIS

CHAPTER FIVE

• CONCLUSION
• RECOMMENDATION
• REFERENCES

ABBREVIATIONS

AWGNAdditive White Gaussian Noise

BCHBose-Chaudhuri-Hocquengham code1

BERBit Error Rate

bppBit per pixel

BPSKBinary Phase Shift  Keying

BSCBinary Symmetric Channel

ECCError-Correcting   Code

GF(2m)Galois Field with 2melements

JSCCJoint Source-Channel Coding

KLTKarhunen-Loe`ve Transform

MAPMaximum a posteriori

MLMaximum Likelihood

MSEMean Squared Error

SNRSignal-to Noise Ratio

RSReed-Solomon code

UEPUnequal Error Protection

CHAPTER ONE

1.0                                                          INTRODUCTION

The last thirty five years have seen a dramatic change in the way communication is achieved around the world. Wireless communication has evolved from being an expensive and rare technology for the few in the 70’s to becoming a wide spread and economical means of facilitating commercial as well as public service communications. One of the majors reasons for the continuous growth in the use of wireless communication is its increasing ability to provide efficient communication links to almost any location, at constantly reducing costs with increasing power efficiency (Jemibewon, 2000).

Wireless communication is one of the most active areas of technological development. This development is being driven primarily by the transformation of what has been a medium for supporting voice telephone into a medium for supporting other services such as transmission of video, images, text and data etc (Wang, 2003). Basically, a communication system deals with information or data transmission from one point to another (Du, 2009). Over the years, there has been a tremendous growth in digital communications especially in the fields of cellular, satellite and computer communications. In these communication systems, the information is represented as a sequence of binary bits. The binary bits are then mapped (modulated) to analog signal wareforms and transmitted over a communication channel. The communication channel introduces noise and interference to corrupt the transmitted signal. At the receiver end, the channel corrupted transmitted signal is mapped back to binary bits. The received binary information is an estimate of the transmitted binary information (Huang, 1997). Normally, during signal transmission through noisy channels errors can be detected and corrected using coding techniques (Huang, 1997). Noise is any undesired signal in a communication circuit. Noise can also be unwanted disturbances supper imposed on a useful signal, which tends to obscure its information content.

1.1                                           BACKGROUND OF THE STUDY

In the beginning of the third millennium, the growth of communication systems deeply affects human societies. Lots of data flow through net- works of various types set up all over the planet. With the emergence of multimedia applications, several types of data have to be transmitted, including text, speech or sound, and images, which are at the center of this work. All these data types have undergone the digital revolution, which enables a rich set of storage and processing techniques. More and more image sources are available, be they fixed or moving, computer-generated or based on sampled analog pictures. Television, movies, satellite observations, medical images or camera pictures are a few examples of applications linked to image transmission and processing.

Transmission channels are available in lots of different types, wired or wireless, packet or  connection-oriented.  TV-cables,  telephone  networks or power lines are expanding from their initial roles into large-capacity transmission channels. They  all  have  their  own  characteristics  in  terms of available bandwidth and quality of service. Mobile applications offer more and more services, and image transmissions will truly appear in the third generation of mobile phones. Transforming all these channels suffering from various impairments into efficient transmission media requires a lot of signal processing.

In order to conquer the corresponding markets, communications  engineers have to design systems always facing the same constraints: band- width and power.  Bandwidth limitations impose an efficient compression  of transmitted data. This is particularly crucial for digital images, which  can  be of  very large size.   All  the corresponding techniques are  known   as source coding. Limitations in power determine the ability for useful signals to be recovered above noise,  generally with  a  certain probability  of errors or other impairments. Interfering signals from other sources or distortions caused by the nonideal response of the channel can also cause errors. The way to cope with them is called channel coding.

Emitter

Receiver

Figure 1: Schematic representation of an image transmission chain including source and channel codes.

A typical image transmission system contains the elements depicted in figure 1, implementing the two coding steps we have just mentioned; the picture successively undergoes source and channel coding. The source coder is responsible for removing the redundancy of the picture, in order   to lower the bit rate required to transmit it. Classically, this coder begins with a de correlating transform, whose role is to remove the correlation between adjacent pixels by expressing them in a space based on the eigen- vectors of the statistical distribution of the pixels, or close to these vectors. This can be proved to reduce the required bit rate to its minimum value. Then, a conversion from real values to bits has to be achieved. This consists in first quantizing the coefficients we have to code,  i.e.  expressing them into integer multiples of a base step, and secondly entropy coding them. This last step also contributes to reducing the bit rate, by taking into account the fact that some values are more frequent than others, e.g. values close to zero are more frequent in case of zero-mean distributions. By assigning shorter code words to these values, we reduce the average  bit rate.

After all these source coding steps, we get a  flow  of  bits to  be transmit- ted through the channel. If this channel was perfect, we could simply put the bits on it, get them back at the channel output, and use them in order   to recover the source picture. However, the channel generally introduces errors. In order to prevent them, we have to use what we call channel cod- ing. The idea is somewhat opposite to source coding, as here we introduce redundancy.  However,  this redundancy is structured in  such a way that we can use it to correct channel errors. Of course, the more redundancy we introduce, the more errors we can correct. We can already see that adapting the channel code we use to the channel characteristics will be an important issue in the design of a transmission system.

After receiving bits at the output of the channel, we have to implement decoders in order to reconstruct the transmitted picture. The channel de- coder will remove the redundancy introduced by  the channel  code  and use it in order to correct channel errors. We will see later that  different kinds of channel decoders exist for a given code, depending on the avail- able computing power in order to correct as many errors as possible. Then, the hopefully almost error-free bit stream will be used by the source de- coder in order to rebuild the picture. Basically, this decoder simply inverts the operations achieved by the source coder. However, when  facing  errors, we can try to develop more efficient strategies. This includes using some knowledge about the general shape of a picture in order to recover erroneous coefficients.

Both source and channel coding problems have led to many results and coding schemes. Behind them, Shannon’s information theory provides bounds on their performances [71]. This theory also tells us that  both coding problems can be processed separately, a pretty nice result as both problems are already hard to tackle on their own. However, Shannon’s result only holds under ideal conditions of infinite data size and computing power. In practical systems, limitations on size and complexity make Shannon’s bounds unreachable. Better solutions are generally obtained by combining both coding problems, giving rise to the so-called joint source- channel codingphilosophy. Its aim is to provide solutions not  only  to  a given source or channel coding problem, but to a more  general application including both subsystems. Such a way of designing transmission systems has led to better performances with respect to traditional design, by taking benefit from the mutual influences and relationships between source and channel coders and decoders.

In this thesis, we want to address some of the possible interactions between source and channel coders in a global system.  A key issue is how to select a channel coding scheme for hierarchical image transmission, adapted to the type of data transmitted. Different parts of the data can affect more or less the received picture by being more or less important    in the image representation, or more or less sensitive to errors. Of course, this requires a sufficient knowledge of the performances achieved by the available coding schemes, in order to accurately match the level of protection to the significance of the source data.

1.2                                               OBJECTIVE OF THE STUDY

The objective of this study is to stimulate error in image transmission over the 3g network using different types of error control codes.

1.3                                                   SCOPE OF THE STUDY

Digital communication systems have played a vital role in the growing demand for data communications. In communication systems when data is transmitted or received, error is produced due to unwanted noise and interference from the communication channel. For efficient data communications it is necessary to receive the data without error. Error-control coding technique is to detect and possibly correct errors by introducing redundancy to the stream of bits to be sent to a channel.

1.4                                           SIGNIFICANCE OF THE STUDY

The image transmission systems in 3G use error- control codes in order to improve the communication quality by trading bandwidth for a lower error rate.