21444658 Multi User Detection in Cdma Project Report

8/6/2019 21444658 Multi User Detection in Cdma Project Report

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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

estimation and equalization is carried out for each individual received symbol by taking

instantaneous estimates of signal statistics based on the sample values of each symbol. Based

on the BCJR algorithm, an iterative turbo multi-user detector was proposed.

(ii) Frame-by-frame approaches. Algorithms in this category stack the total received signal

corresponding to a whole frame or slot into a long vector, and formulate a deterministic

channel model. Computational complexity is reduced by breaking the big matrix into small

blocks and implementing the inversion locally. As can be seen, the localization is similar

to the process of the symbol-by-symbol approach. And the work is extended to fast fading

channels.

(iii) Chip-level equalization. With the observation that channels remain approximately stationary

over each time slot, the underlying channel, therefore, can be modeled as a time-invariant

system, and at the receiver, chip-level equalization is performed. In all these three categories,

one way or another, the time varying channel is converted or decomposed into time

invariant channels. In this paper, the long-code CDMA system is characterized as a time-

invariant MIMO system as in. Actually, the received signals and MUIs can be modeled as

cyclostationary processes with modulation-induced cyclostationarity, and we consider blind

channel estimation and signal separation for long-code CDMA systems using multistep linear

predictors. Compared with subspace methods, linear prediction methods can deliver more

accurate channel estimates and are more robust to overmodeling in channel order estimate. In

this paper, multistep linear prediction method is used to separate the intersymbol interference

introduced by multipath channel, and channel estimation is then performed using nonconstant

modulus precoding technique both with and without the matrix-pencil approach .The channel

estimation algorithm without the matrix-pencil approach relies on the Fourier transform, and

requires additional constraint on the code sequences other than being nonconstant modulus. It

is found that by introducing a random linear transform, the matrix-pencil approach can

remove (with probability one) the extra constraint on the code sequences. After channel

estimation, equalization is carried out using a cyclic Wiener filter. Finally, since chip-level

equalization is performed, the proposed approach can readily be extended to multirate cases,

either with multicode or variable spreading factor. Simulation results show that compared

with the approach using the Fourier transform, the matrix-pencil-based approach can

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significantly improve the accuracy of channel estimation, therefore the overall system

performance.

1.1Synchronous CDMA

Consider a synchronous CDMA digital communication system as depicted in Figure 1. Its main goal is

to share the channel between different users, discriminating between them by different assigned codes.

Each transmitted bit is upsampled and multiplied by the users spreading codes and then the chips for

each bit are transmitted into the channel (each element of the spreading code is either +1 or .1 and they

are known as chips).

The channel is assumed to be linear and noisy; therefore the chips from different users are added

together, plus Gaussian noise. Hence, the MUD has to recover from these chips the bits corresponding to

each user. At each time step t, the signal in the receiver can be represented in matrix notation as: xt =

HAbt + nt (1) where bt is a column vector that contains the bits (+1 or .1) for the K users at time k. The

K K diagonal matrix A contains the amplitude of each user, which represents the attenuation that each

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users transmission suffers through the channel (this attenuation depends on the distance between the

user and the receiver). H is an L K matrix which contains in each column the L-dimensional spreading

code for each of the K users. The spreading codes are designed to present a low cross-correlation

between them and between any shifted versions of the codes, to guarantee that the bits from each user

can be readily recovered. The codes are known as spreading sequences, because they augment the

occupied bandwidth of the transmitted signal by L. Finally, xt represents the L received chips to which

Gaussian noise has been added, which is denoted by nt. At reception, we aim to estimate the original

transmitted symbols of any user i, bt(i), hereafter the user of interest. These MUDs have good

convergence properties and do not need a training sequence to decode the received bits, but they need

large training sequences before their probability of error is low. Therefore the initially received bits will

present a very high probability of error that will make impossible to send any information on them.

Some improvements can be achieved by using higher order statistics, but still the training sequences are

not short enough for most applications.

1.2Asynchronous CDMA

Thej:th user experiences the SNR:

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{ }

2 2

2 2 2

2

, , ,

0

2

jj jj

j

ij j ij ij j j

i j i j i j

m mSNR

m n m m n n

= + + +

E E E E

1 4 4 2 4 4 3


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Block diagram of asynchronous system

CHAPTER-2 PRACTICAL CDMA RECIEVER

2.1. Description

a practical cdma reciever consists of a low pass filter which filters out the unwanted noise signals and

forwards the desired band of frequencies to a multiplier which multiplies the recieved signal with the

locally generated code,next is an integrator which integrates the product of the recieved signal with the

unit step signal and subsequently a sampled signal is produced after the decision and phasing of

sampling.

0( )

mt

u tLPF

Local code

From channel Decision

Phasing of sampling

xiv{ }

2

2 2

2

, ,

0

2

jj

j

ij ij j j

i j i j

mSNR

m m n n

+ +

E E E1 4 4 2 4 4 3

/c j j L P WP R=


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Hence, SNR upper bound for thej:th user is:

2.2 Perfect power control

1. Equal received powers forUusers means that

2. Therefore the j:th users SNR equals

and the number of users is

3. where* (for BPSK)

s Number of users is limited by:

x Channel AWGN levelN0.

x Processing gainLc.

xv

0 eff N NB P =

01

c j

j U

i eff i

i j

LP

SNR P N B

=

+

1 ( 1)Uii i ji j

P P U P =

= =

0

0

( )( 1)

c j

eff j

L PSNR

N B U P

+

1

1 11 co

U LSNR SNR

+

1

0

2j j bc

eff N o

P PW ESNR L

B N P R N = = =


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x received powerPr.

2.3 Near far effect in CDMA

Assume all users apply the same power but their distance to the receiving node is different. Hence the

power from the i:th node is

Where d is the distance, and a is the propagation attenuation coefficient (a = 2 for free space, in urban

area a = 35)

s Express the power ratio of the i:th and j:th user at the common reception point

s Therefore, the SNR of the j:th user is

xvi

j

o i i j j i j

i

d P Pd Pd P P

d

= = =

00

11

c j c j

j jUU

jeff i

eff ji

i i

i j i j

L P L P SNR S NR

d N B P N B P d

==

+ +


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2.3.a The near-far effect in asynchronous CDMA

s Grouping the previous yields condition

Multiple-access interference (MAI) power should not be larger than what the receiver sensitivity can

accommodate. Note the manifestation ofnear-far -effect because just one larger sum term on the left

side of the equation voids it

s Example: Assume that all but one transmitter have the same distance to the receiving node. The

one transmitter has the distance d1=dj /2.5 and a=3.68, SNR0=14, SNR1=25,Rb = 30 kb/s,Beff= 20 MHz, then

s By using the perfect power balance the number of users is

Hence the presence of a single user so near has dropped the number of users into almost 1/3 part of

the maximum number

If this user comes closer than:

all the other users will be rejected, e.g. they can not communicate in the system in the required SNR

level. This illustrates the near-far effect

, (2 / ) /(1/ ) 2 / 2c BPSK c b b c b eff L T T T T T B= =

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1 0 1

1 11

Uj

ci i

i j

dL U

d SNR SNR

=

=

3.68

1

(2.5) 2U

j

i i

i j

dU

d

=

= +

3.68

0 1

3.68

0 1

1 1(2.5) 2

1 12 2.5 14

c

c

U LSNR SNR

U LSNR SNR

+

+ =

0 1

1 11 42

( ) ( )cU L

SNR SNR

= + =

1 /2.78jd d


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To minimize the near-far effect efficient power control is should be adaptively realized in

asynchronous CDMA-systems.

CHAPTER-3 CDMA COMMUNICATION SYSTEM MODEL

AND MUD

Consider a synchronous CDMA digital communication system as depicted in Figure 1. Its main goal is

to share the channel between different users, discriminating between them by different assigned codes.

Each transmitted bit is up sampled and multiplied by the users spreading codes and then the chips for

each bit are transmitted into the channel (each element of the spreading code is either +1 or .1 and they

are known as chips). The channel is assumed to be linear and noisy; therefore the chips from different

users are added together, plus Gaussian noise. Hence, the MUD has to recover from these chips the bits

corresponding to each user. At each time step t, the signal in the receiver can be represented in matrix

notation as: xt = HAbt + nt (1)

where bt is a column vector that contains the bits (+1 or .1) for the K users at time k. The K K

diagonal matrix A contains the amplitude of each user, which represents the attenuation that each users

transmission suffers through the channel (this attenuation depends on the distance between the user and

the receiver). H is an L K matrix which contains in each column the L-dimensional spreading code for

each of the K users. The spreading codes are designed to present a low cross-correlation between them

and between any shifted versions of the codes, to guarantee that the bits from each user can be readily

recovered. The codes are known as spreading sequences, because they augment the occupied bandwidth

of the transmitted signal by L. Finally, xt represents the L received chips to which Gaussian noise has

been added, which is denoted by nt.

At reception, we aim to estimate the original transmitted symbols of any user i, bt(i), hereafter the user

of interest. Linear MUDs estimate these bits as bt(i) = sgn{w. i xt} (2) The matched filter (MF) wi = hi,

a simple correlation between xt and the ith spreading code, is the optimal receiver if there were no

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additional users in the system, i.e. the received signal is only corrupted by Gaussian noise. The near-far

problem arises when remaining users, apart from the UOI, are received with significantly higher

amplitude. While the optimal solution is known to be nonlinear, some linear receivers such as the

minimum mean square error (MMSE) present good performances and are used in practice.

These MUDs have good convergence properties and do not need a training sequence to decode the

received bits, but they need large training sequences before their probability of error is low. Therefore

the initially received bits will present a very high probability of error that will make impossible to send

any information on them. Some improvements can be achieved by using higher order statistics , but still

the training sequences are not short enough for most applications.

3.3 Multiple Access Interference (MAI)

s CDMA system can be realized by spreading codes having low cross -correlation as Gold codes

(asynchronous usage) or Walsh codes (synchronous usage).Multipath channel with large delay

spread can destroy code cross-correlation properties. Asynchronous systems with large code gain

assume other users to behave as Gaussian noise.

s Additional compensation of MAI yields further capacity (increases receiver sensitivity). This can

be achieved by:

x Code waveform design (BW-rate/trade-off).

x Power control (minimizes near-far effect).

x FEC- and ARQ-systems.

x Diversity-systems: - Spatial - Frequency Time.

x Multi-user detection.

3.4 MAI versus Intersymbol interference (ISI)

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x Note that there exists a strong parallelism between the problem of MAI and that of ISI:

x Hence, a number of multi-user detectors have their equalizer counter parts as:

x Maximum likelihood.

x Zero-forcing.

x Minimum mean square.

x Decision feedback.

x General classification of multi-user detectors:

1. Linear.

2. Subtractive.

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Asynchronous channel of K-users behaves the same

way as a single user channel having ISI with *memory

depth of K-1


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CHAPTER-4 MAXIMUM LIKELIHOOD SEQUENCE

DETECTION

4.3Basic concept

s The Maximum Likelihood(ML) principle:

x Has the optimum performance provided transmitted symbols equal alike.

x Has large computational complexity - In exhaustive search 2NKvectors to be

considered! (Kusers,Nbits).

x Requires estimation of received amplitudes and phases that takes still more

computational power.

x Can be implemented by using Viterbi-decoder that is practically optimum ML-detection

scheme to reduce computational complexity by surviving path selections.

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CHAPTER -5 CONVENTIONAL DETECTION FOR MULTIPLE

ACCESSES

5.1Output of the kth user

s Detection quality depends on code cross- and autocorrelation:

,

1( ) ( )

b

i k i k

Tb

g t g t dtT

= .

s Hence we require a large autocorrelation and small cross correlation (small ISI).

,

,

1,

0 1,

i k

i k

i k

i k

==

s The output for the K:th user consist of the signal, MAI and filtered Gaussian noise terms (as

discussed earlier).

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1 ,

1( ) ( )

1( ) ( )

b

b

k kTb

K

ik k k i k i i k Ti k

b

k k k k k

y r t g t dt T

y Ad Ad n t g t dt T

y Ad MAI z

=

=

= + +

= + +

Received SNR of this was considered earlier in this report.

5.2 Matrix Notation

Assume a three user synchronous system with a matched filter receiver:

1 2,1 2 2 3,1 3 3 1

2 1,2 1 1 3,2 3 3 2

3 1

1

,3 1 1 2,3 2 2 3

1

2 2

3 3

Ad

A

y Ad Ad z

y Ad Ad z

y A dd z

d

Ad A

=+ + +

= ++ + = + ++

1 2,1 3,1 1 1 1

2 1,2 3,2 2 2 2

3 1,3 2,3 3 3 3

1 0 01 0 0

1 0 0

y A d z y A d z

y A d z

= +

That is expressed by the matrix-vector notation as

= +y RAd z

= +y R A d z

5.3 Data term and MAI term

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Matched filter outputs

Correlations between each pair of codes

Received amplitudes

Data

Noise


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Matrix Rcan be partitioned into two parts by setting:

= +y RAd z

Note that hence Q contains off-diagonal elements orR(or the crosscorrelations)

And therefore MF outputs = +y RAd z can be expressed as

Therefore the term Ad contains the decoupled data and QAd represents the MAI.

Objective of all MUD schemes is to cancel out the MAI-term as effectively as possible (constraints

to hardware/software complexity and computational efficiency).

CHAPTER-6 SYNCHRONOUS AND ASYNCHRONOUS

CHANNEL

In synchronous detection decisions can be made bit-by-bit. In asynchronous detection bits overlap and

multi-user detection is based on taking all the bits into account.

1

( ) ( ) ( ) ( ) ( )K

k k k k

k

r t A t g t d t n t =

= +

1 1bT + 13 bT +1 2 1bT + 23 bT + 1 1bT + 13 bT +1 2 1bT + 23 bT + TThe matrix Rcontains now partial correlations that exist between every pair of theNKcode words .

6.1Channel correlation matrix

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( )= + + = +y I Q A d z d Q A dA z


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In this example the correlation matrix extends to 6x6 dimensions:

= +y RAd z

2,1

1,2 3,2

2,3 4,3

3,4 5,4

4,5 6,5

5,6

1 0 0 0 0

1 0 0 0

0 1 0 0

0 0 1 0

0 0 0 1

0 0 0 0 1

=

R

Note that the resulting matrix is sparse because most of the bits do not overlap. Sparse matrix algorithms

can be utilized to reduce computational difficulties (memory size & computational time).

6.2 Decorrelating detector

s The decorrelating detector applies the inverse of the correlation matrix to suppress MAI,

1

dec

=L R

and the data estimate is therefore:

1

1

1

( )

dec

dec

=

= ++

=+=+

RAd

d R y

R z

Ad R z Ad z

AA Q dd

We note that the decorrelating detector eliminates the MAI completely! However, channel noise is

filtered by the inverse of correlation matrix - This results in noise enhancement!Decorrelating detector ismathematically similar to zero forcing equalizer as applied to compensate ISI.

6.3 polynomial expansion detectors

Many MUD techniques require inversion ofR. This can be obtained efficiently by PE:

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1

0

SNi

PE i

i

w

=

= L R R

PE PE y=d L

0 1

0 1

0

...S

S

S

NNi

PE i N

i

w y w y w y w y=

= = + +d R R R R

For finite length message a finite length PE series can synthesize R-1 exactly. However, in practice a

truncated series must be used for continuous signaling:

y yR 2yR

y yR 2yR0w 1w 2w

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CHAPTER-8 SUCCESSIVE INTERFERENCE

CANCELLATION (SIC)

1 ( )

b A t T 1 1( b g t T

1(

b s t T

( )b

r t Tb

T

( )r t1

d

Each stage detects, regenerates and cancels out a user

s First the strongest user is cancelled because

x It is easiest to synchronize and demodulate.

x This gives the highest benefit for canceling out the other users.

s Note that the strongest user has therefore no use for this MAI canceling scheme!

s PROS: Small HW requirements and large performance improvement when compared toconventional detector.

s CONS: Processing delay, signal reordered if their powers changes, in low SNR performance

suddenly drops.

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CHAPTER-9 PARALLEL INTERFERENCE CANCELLATION

(PIC)

2 ( )bA t T

( )K b A t T

1 ( )bA t T

1

( )i

i

s t

2

( )ii

s t

( )i

i K

s t

( )br t T

1( ) b s t T

2 ( ) bs t T

( )K b s t T

( 1) ( )

( ( ))

m m

m

+ =

= + +

d y QAd

Ad QA d d z

= + +y Ad QAd z

( )= + += + +

y I Q Ad z

d QAdA z

9.1 PIC properties

s SIC performs better in non-power controlled channels.

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s PIC performs better in power balanced channels.

s Using decorrelating detector as the first stage:

x Improving first estimates improves total performance.

x Simplifies system analysis.

s Doing a partial MAI cancellation at each stage with the amount of cancellation increasing for

each successive stage.

x Tentative decisions of the earlier stages are less reliable - hence they should have a lower

weight.

x Very large performance improvements have achieved by this method.

x Probably the most promising suboptimal MUD.

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CHAPTER-10 BENEFITS AND LIMITATION OF MULTIUSER

DETECTION (MUD)

s Significant capacity improvement - usually signals of the own cell are included .

s More efficient uplink spectrum utilization - hence for downlink a wider spectrum may be

allocated.

s Reduced MAI and near-far effect - reduced precision requirements for power control.

s More efficient power utilization because near-far effect is reduced.

s If the neighboring cells are not included interference cancellation efficiency is greatly reduced.

s Interference cancellation is very difficult to implement in downlinkreception where, however,

larger capacity requirements exist (DL traffic tends to be larger).

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PROS:

CONS:


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CONCLUSION AND FUTURE WORK

It has been shown that CDMA systems suffer from severe multi- user interference. Although strong

error control coding is able to ensure reliable transmissions for medium system loads, it is beneficial to

apply multi- user detection especially for high system loads. Based on the uplink of an OFDM-CDMA

environment, the performances of various multi-user detectors have been demonstrated. Concerning

linear approaches, the MMSE detector can be efficiently approximated by iterative strategies like the

Gauss-Seidel algorithm to avoid an explicit inversion of the correlation matrix and save computational

cost. Taking into account the discrete nature of the signal alphabet, nonlinear elements like clipping or

channel decoding have to be incorporated into the iterations. This concept improves performance

significantly. Even in the case of an overloaded system, e.g. 2 s U N=, the single-user performance can

be reached leading to high spectral efficiencies.

There are significant advantages to MUD which are, however, bounded and a simple

implementation is needed.

Current investigations involve implementation and robustness issues.

MUD research is still in a phase that would not justify making it a mandatory feature for 3G

WCDMA standards.

Currently other techniques such as smart antenna seem to be more promising.

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REFERENCES

1. Multi user detection using CDMA by Sergio Verdu.

2.Licentiate Course on Signal Processing in Communications (CDMA Overview) by Mika Raitola of

Nokia Research Center Radio Communications Laboratory.

3.Implementation Issues of Multi-user Detection in CDMA Communication Systems by Gang Xu.

4. Computer networks by Tanenbaum.

5. Research paper titledmulti user detection using Gaussian process by Fernando and Caro.

6.Multi-User Detection in Multicarrier-CDMA Systems by Dr.-Ing. Volker Khn, Ronald Bhnke and

Prof. Dr. Ing Karl and Dirk Kammeyer.

7. Multi-User Detection forCDMA Systems by Alexander Duel Hallen and Zoran Zvonar.

8.Optimal Multiuser Detection for CDMA Systems byDr M Motani.

Documents

21444658 Multi User Detection in Cdma Project Report