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A SEMINAR REPORT ON

MULTI-USER DETECTION IN CDMA

Submitted in partial fulfillment of the requirements

for the award of the degree of

Bachelor of Technology

In

Electronics & Communication Engineering

Guide: Submitted by:

Mrs. PINKI NAYAK TARUN KUMAR

Roll No.: 0111042805

Amity School of Engineering & Technology

Guru Gobind Singh Indraprastha University (GGSIPU), Delhi

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ACKNOWLEDGEMENT

I am thankful to my guide Mrs. Pinki Nayak for her support in collection and compilation of data

and providing guidance to use and analyze the data for seminar matter.

I also thank my parents and my family for their moral support to carry out the seminar report work.

TARUN KUMAR

DATE:

PLACE:

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ABSTRACT

One of the major issues in present wireless communications is how users share the resources and

particularly, how they access to a common frequency band. Code Division Multiple Access (CDMA) is

one of the techniques exploited in third generation communications systems and is to be employed in the

next generation. In CDMA each user uses direct sequence spread spectrum (DS-SS) to modulate its bits

with an assigned code, spreading them over the entire frequency band. While typical receivers deal only

with interferences and noise intrinsic to the channel (i.e. Inter-Symbolic Interference, intermodulation

products, spurious frequencies, and thermal noise), in CDMA we also have interference produced by other

users accessing the channel at the same time. Interference limitation due to the simultaneous access of

multiple users systems has been the stimulus to the development of a powerful family of Signal

Processing techniques, namely Multi-user Detection (MUD).

These techniques have been extensively applied to CDMA systems. Thus, most of last generation digital

communication systems such as Global Positioning System (GPS), wireless 802.11b, Universal Mobile

Telecommunication System (UMTS), etc, may take advantage of any improvement on this topic. In

CDMA, we face the retrieval of a given user, the User of Interest (UOI), with the knowledge of its

associated code or even the whole set of users codes. Hence, we face the suppression of interference due

to others users. If all users transmit with the same power, but the UOI is far from the receiver, most users

reach the receiver with larger amplitude, making it more difficult to detect the bits of the UOI. This is

well-known as the near-far problem. Simple detectors can be designed by minimizing the Mean Square

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Error (MMSE) to linearly retrieve the user of interest. However, these detectors need large sequences of

training data. Besides, the optimal solution is known to be nonlinear.

There have been several attempts to solve the problem using nonlinear techniques. There are solutions

based on Neural Networks such as multilayer perceptron or radial basis functions but training times are

long and unpredictable. Recently, support vector machines (SVM) have been also applied to CDMA

MUD. The upcoming third generation mobile radio system in Europe is based on UMTS (Universal

Mobile Telecommunications Standard). In order to supply access to a common transmission channel for

several users, UMTS incorporates Code Division Multiple Access (CDMA). Besides a lot of practical

advantages, CDMA suffers from multi- user interference limiting spectral efficiency dramatically.

However, bandwidth is a very valuable resource and should be used as efficiently as possible. One

appropriate mean to increase spectral efficiency of CDMA systems is multi- user detection.This report gives an overview of different multi- user detection techniques. Their performance is

compared with the conventional single-user detection including channel coding. Specifically, linear as

well as nonlinear multi- user detectors are considered. Efficient realizations of linear detectors are given

leading to improved nonlinear techniques. It is shown that nonlinear MUD including channel decoding

can achieve a spectral efficiency twice as high as that of the well-known GSM standard (Global System

for Mobile Communications) employing TDMA and FDMA.

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TABLE OF CONTENTS

_________________________ ___________________________________________________________ __________________________________

CERTIFICATE ii

ACKNOWLEDGEMENT iii

ABSTRACT iv

LIST OF FIGURES 6

1. INTODUCTION

1.1 Synchronous CDMA 9

1.2 Asynchronous CDMA 10

2. PRACTICAL CDMA RECIEVER

2.1. Description 11

2.2 Perfect power control 12

2.3 Near far effect in CDMA 13

3. CDMA COMMUNICATION SYSTEM MODEL AND MUD

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3.1 Multiple access interference (MAI) 16

3.2 MAI versus Intersymbol interference (ISI) 16

4. MAXIMUM LIKELIHOOD SEQUENCE DETECTION

4.3 Basic concept 18

4.4 Formulation 18

5. CONVENTIONAL DETECTION FOR MULTIPLE ACCESSES

5.1 Output of the kth user 19

5.2 Matrix Notation 19

5.3 Data term and MAI term 20

6. SYNCHRONOUS AND ASYNCHRONOUS CHANNEL

6.1 Channel correlation matrix 21

6.2 Decorrelating detector 22

6.3 Polynomial expansion detectors 22

7. MINIMUM MEAN SQUARE ERROR (MMSE) DETECTION 24

8. SUCCESSIVE INTERFERENCE CANCELLATION (SIC) 25

9. PARALLEL INTERFERENCE CANCELLATION (PIC)

9.1 PIC properties 26

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10. BENEFITS AND LIMITATION OF MULTIUSER

DETECTION (MUD) 28

CONCLUSION AND FUTURE WORK 29

REFERENCES 30

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LIST OF FIGURES

Chapter - 1

Fig. 1.1 Asynchronous CDMA 9

Chapter - 2

Fig. 2.1 Practical CDMA receivers 11

Fig. 2.2 AWGN vs. Users graph 12

Chapter - 3

Fig. 3.1 CDMA communication system model 16

Chapter 5

Fig 5.1 Conventional detection for multiple accesses 19

Chapter 6

Fig 6.1 Asynchronous and Synchronous channel 22

Chapter 8

Fig 8.1 SIC block diagram 25

Chapter 9

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Fig 9.1 PIC block diagram 26

CHAPTER-1 INTRODUCTION

In addition to intersymbol and interchip interference, one of the key obstacles to signal detection and

separation in CDMA systems is the detrimental effect of multi-user interference (MUI) on the

performance of the receivers and the overall communication system. Compared to the conventional

single-user detectors where interfering users are modeled as noise, significant improvement can be

obtained with multi-user detectors where MUI is explicitly part of the signal model .if the spreading

sequences are periodic and repeat every information symbol, the system is referred to as short-code

CDMA, and if the spreading sequences are aperiodic or essentially pseudorandom, it is known as long-

code CDMA. Since multi-user detection relies on the cyclostationarity of the received signal, which is

significantly complicated by the time-varying nature of the long-code system, research on multi-user

detection has largely been limited to short-code CDMA for some time. On the other hand, due to its

robustness and performance stability in frequency fading environment, long code is widely used in

virtually all operational and commercially proposed CDMA systems, as shown in Figure 1. Actually,

each users signal is first spread using a code sequence spanning over just one symbol or multiple

symbols. The spread signal is then further scrambled using a long-periodicity pseudorandom sequence.

This is equivalent to the use of an aperiodic(long) coding sequence as in long-code CDMA system, and

the chip-rate sampled signal and MUIs are generally modeled as time-varying vector processes. The

time-varying nature of the received signal model in the long-code case severely complicates the

equalizer development approaches, since consistent estimation of the needed signal statistics cannot be

achieved by time-averaging over the received data record.

More recently, both training-based and blind multi-user detection methods targeted at the long-codeCDMA systems have been proposed. In this paper, we will focus on blind channel estimation and user

separation for long-code CDMA systems.

Based on the channel model, most existing blind algorithms can roughly be divided into three classes.

(i) Symbol-by-symbol approaches. As in long-code systems, each users spreading code changes

for every information symbol, symbol-by-symbol approaches process each received symbol

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individually based on the assumption that channel is invariant in each symbol. Channel

est