volume 3, issue 9, september 2013 issn: 2277 128x

© 2013, IJARCSSE All Rights Reserved Page | 317

Volume 3, Issue 9, September 2013 ISSN: 2277 128X

International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com

Performance Improvement of DS-CDMA System with Successive

Interference Cancellation Receiver Madhvi Jangalwa

Sapna Dubey

E& TC Department, DAVV Indore Software developer, IGATE Mumbai

India India

Abstract— In a Code Division Multiple Access (CDMA) system, many users use simultaneously the entire frequency

band to transmit their data and users’ data is separated on the basis of their unique spreading code. It is logical that

the user spreading code be mutually orthogonal so as to avoid interference among different users. The DS-CDMA

system is well known for eliminating the effects of Multiple Access Interference (MAI) which limits the capacity and

degrades the BER performance of the system. This paper deals with the bit Error Rate (BER) performance of a DS-

CDMA system over AWGN and on different modulation techniques, which is affected by the different number of users.

In DS-CDMA systems, overcoming near/far effects and fading is imperative for satisfactory performance. One way to

combat the near/far effect is to use stringent power control, as is done in most of the commercial systems. Another

approach is Multi-User Detection (MUD). In addition to mitigating the near/far effect, MUD has the more

fundamental potential of raising capacity by cancelling MAI. This paper also shows performance evaluation of DS-

CDMA with Successive Interference Cancellation (SIC) receiver, which is a sub-optimal method of Multiuser detector

(MUD).

Keywords— DS-CDMA, MAI, MUD, SIC, BER.

I. INTRODUCTION

Direct-sequence code-division multiple access (DS-CDMA) is a promising multiple access capability for third and next

generations mobile communication systems. Code-Division Multiple Access (CDMA) is a technique whereby many

users simultaneously access a communication channel. The users of the system are identified at the base station by their

unique spreading code. The signal that is transmitted by any user consists of the user’s data that modulates its spreading

code, which in turn modulates a carrier. An example of such a modulation scheme is Quadrature Phase Shift Keying

(QPSK). In this paper, we introduce the Rayleigh channel and Additive white Gaussian noise (AWGN) Channel, and

investigated the Bit Error Rate (BER) performance of a DS-CDMA system over these channels. In the DS-CDMA

system, the narrowband message signal is multiplied by a large bandwidth signal, which is called the spreading of a

signal. The DS-CDMA system is presented for eliminating the effects of Multiple Access Interference (MAI) which

limits the capacity and degrades the BER performance of the system. MAI refers to the interference between different

direct sequences users. As the number of user’s increases, the MAI grows to be considerable and the DS-CDMA system

will be interference limited.

With the emergence of multiple access techniques, there has been an increase in the interest in performing simultaneous

estimation and detection over all users [5]. MAI can be prevented by selecting mutually orthogonal signature waveforms

for all the active users [6] [3]. However, it is not possible to ensure perfect orthogonality among received signature

waveforms in a mobile environment, and thus MAI arises. In order to mitigate the problem of MAI, Verdu [7] proposed

and analysed the optimum multiuser detector for asynchronous Gaussian multiple access channels.

Mathematical function to represent MAI [12]

𝕽𝒌 = 𝒔𝒌 𝑺𝑵𝑹 𝑸 𝟏𝟎𝑺𝑵𝑹

𝟏+𝟏𝟎𝝈𝟐𝑺𝑵𝑹 + 𝑨𝒌

𝑲𝒌=𝟐 𝑼𝒌 + 𝜼𝑲 (1)

The first two terms of equation (1) can be used to approximate the error rate and the MAI for a user k. It should be noted

that the second term gives an average variance of the MAI over all possible operating conditions that can be used to

compute the required SNR for a desirable BER performance. We use equation (1) in conducting the simulation result and

performing the experimental verification by giving the non folded amplitude of the k signal and computing the

corresponding MAI as a Gaussian random variable with zero mean and conditional variance that represents by 2 s .

Similarly, the first terms of equation (1) can be used to approximate the suppression of MAI for a desirable BER

performance. Since there is no closed-form mathematical relationship exists between the first two terms of equation (1),

we believe this is the optimal approximation of the MAI. This paper also shows the performance comparison of DS-

CDMA system with and without MUD receiver here we are using Successive Interference Cancellation MUD; this

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Madhvi et al., International Journal of Advanced Research in Computer Science and Software Engineering 3(9),

September - 2013, pp. 317-324


comparison is on the basis of simulated results which are performed in MATLAB. The remainder of the paper is

organized as follows. In the next section we present channel modelling. Section 3 deals with description of Multi user

detector, and why sub-optimal non-linear SIC MUD is been used as a detector at receiver side. Section 4 deals with

proposed transmitter and receiver model for simulation. Different MATLAB simulation results and conclusion are

described in section 5 and section 6 respectively.

II. CHANNEL MODEL

An Additive White Gaussian Noise channel model as the name indicate Gaussian noise get directly added with the signal

and information signal get converted into the noise in this model scattering and fading of the information is not

considered [12].

III. MULTIUSER DETECTION SYSTEM

The superposition of the signals transmitted by the users in a multiple access spread spectrum, also known as Code

Division Multiple Access (CDMA), system cause considerable interference to the desired signal if the users’ signature

waveforms are not orthogonal to each other all the time, a situation which is unlikely to occur in mobile-originated calls

[13]. A lot of research has focused on reducing or cancelling the multiple access interference in order to improve the

CDMA receivers’ performance [1]. The initial approach was to design an improved single-user detector operating

efficiently in multi-user channel by applying advanced adaptive signal processing algorithms. It is worth noting that these

detectors are preferred by individual mobile users because knowledge of the parameters (signature waveforms, timing,

amplitude and phase) of the interfering users is not desired. The second approach considers the detection of signals

associated with a group of users where spreading codes, timing information and possibly signals amplitude and phase are

known and used jointly to better detect each user [1]. These devices are called multi-user detectors.

Conventional CDMA [1] systems independently detect each user in parallel using a matched filter which

consists of the unique spreading code used by that user. These spreading codes are designed such that different ones are

highly uncorrelated in order to suppress other users’ signals and treat it as simple additive white noise. This approach

proves to be very suboptimal since these interfering signals need not be treated as random noise. Instead, the information

in these interfering signals can be used to enhance the desired user’s Signal-to-Noise Ratio (SNR), thereby raising the

capacity of the system. Multiuser detectors attempt to do exactly that, i.e. detect interfering signals and cancel them out

from the desired user’s signal.

Multiuser detection is one of the receiver design technology for detecting desired signal(s) from interference and

noise. Traditionally single-user receiver is known suffering from the so-called near-far problem, where a near-by or

strong signal source may block the signal reception of far-away or weak user(s). The near-far is more serious in CDMA-

liked wireless multi-user communication systems. And multiuser detection techniques can help the receiver solve this

problem. There are many types of MUD’s; basically they are divided into two types Optimal and Suboptimal detectors

[5]. The Conventional Matched Filter (MF) detector was simple to implement, and also does not require knowledge of

the channel or the user amplitudes, but it does not take MAI into account and also suffers from near-far problem. That is

how optimum Mud came into account by removing MF detector disadvantages, but it also faces some challenges like

needs knowledge about the channel characteristics at the receiver which is been overcome by sub-optimal detector

because Optimum Multiuser Detector is highly complex and the complexity grows exponentially with number of users.

This complexity is impractical even for moderate number of users. Although, the optimum detector has been shown to

dramatically increase the capacity of the system, its complexity deems it infeasible to implement in the real world. The

capacity can ultimately increase using sub-optimal multiuser detectors that balance between the two extreme cases of

using the optimal detector or the matched filter detector [7].

In order to reduce complexity in the system, suboptimal technique is being used, Sub-optimum Multiuser

Detectors have better near-far resistance than Matched Filter Detector and have lesser complexity (linear complexity)

than Optimum Detector (exponential complexity). Classified into two categories linear and non-linear sub-optimal MUD,

main problem associated with optimum MUD is relaxed by linear MUD like the decision algorithm complexity grows

exponentially with k, k matched filters are required where k is the number of users and a large amount of accurate

channel and user information is required. Main problem associated to this is, that it enhances noise and undefined for the

situation where there are more users simultaneously using the channel then spreading chip information bit, which make

non-linear sub-optimal MUD to came into account in which complexity does not grow linearly as number of users

increases. This is further divided into two categories Decision Feedback (DF) and Interference Cancellation (IC).

The Decision-Feedback (DF) multiuser detector has been developed for synchronous and asynchronous

applications. They are analogous to the Decision-feedback equalizers developed for Inter symbol Interference

suppression, where the decision made for stronger users are used to cancel interference for later users. The problem with

this technique is similar to that of the linear multiuser detector: accurate channel estimates are required, and matrix

inversion must be performed. If the channel estimates are accurate the DF detector outperforms the linear detectors, with

the later users approaching the single-user bound. Due to its complexity and questionable robustness, it is doubtful that

Decision-feedback (DF) MUD can be applied to a realistic wireless channel. That is why interference cancellation

scheme are been sought out as a best detector used at receiver side to improve system capacity of DS-CDMA system.

Interference Cancellation MUD attempts to remove Multi Access Interference (MAI) after decoding. It also relaxes a

number of requirements of the filtering approaches to MUD, such as accurate priori channel estimates, and is typically

much closer in design to existing and proves CDMA receivers. This is how Successive Interference Cancellation (SIC)

http://en.wikipedia.org/wiki/Receiver_(radio)

http://en.wikipedia.org/wiki/Technology

http://en.wikipedia.org/wiki/Interference_(communication)

http://en.wikipedia.org/wiki/Near-far_problem

http://en.wikipedia.org/wiki/CDMA

http://en.wikipedia.org/wiki/Wireless

http://en.wikipedia.org/wiki/Multi-user


September - 2013, pp. 317-324


non-linear sub-optimal MUD detector technique is been sought for better performance for DS-CDMA system. The

successive IC scheme is simpler to implement in hardware than the parallel IC scheme, but more robust in cancelling the

interference since it successively subtracts the interference from the received signal. The strongest interferer can be

detected in the presence of the weaker interferer, and its removal from the received signal enhances the detection of the

weaker signals, so we start with detecting the strongest interferer.

Fig. 1 Stages of SIC

This implicitly implies that we first have to rank the received signals according to their strength such that:

E1 > E2 > E3.......................>EK (2)

Fig. 2 Schematic showing Successive Interference Cancellation

Where Ek is the energy of the kth user. For simplicity and without loss of generality, we assume that the signal from user-

1 is the largest followed by signal from the user-2 signal, and so on. The implication in (6) is that the signal from the kth

user is the weakest. Once the ranking of the received signals is achieved, the detection of the user-1 signal using the

conventional Matched Filter (MF) receiver can be accomplished. Let user-1 detected symbol be 𝑏 1(t) which will be used

to regenerate user-1 signal 𝑥 1(t) as:

(3)

Where C1(t) is signature waveform for user-1. Subtracting (3) from the received signal r(t) we get the input to the next

stage of the IC as:

(4)

Repeating the above procedure on the next strongest user signal (user-2) enables the detection of 𝑏 2(t) and the generation

of the feedback signal 𝑥 2(t). The input to the next stage IC is:

(5)


September - 2013, pp. 317-324


The procedure can be repeated until the user with the weakest signal (kth user) is detected 𝑏 K(t) and the input to the final

stage is:

(6)

The computation complexity of the successive IC algorithm is linear in the number of users. This scheme, though simple,

has a number of disadvantages, the most prominent of which is that, since the IC proceeds serially; a delay of the order of

K computation stages is required to complete the multi-user detection. Furthermore, an erroneous detection at any stage

will be fed back to cause error propagation in the following stages which increases, rather than decreases, the interference

level.

IV. DESIGN MODEL

A. Transmitting Model

Fig. 3Transmitting Model

At first, the data generator generates the data randomly, that generated data is User Data, which is further given to

modulator which perform PSK modulator where order of M is generalized we can take either BPSK modulation or QPSK

modulation technique. Then these modulated data is spreaded individually by using PN sequence. The spreaded data is

given to interleaver and after summing of individual data is transmitted through AWGN and Rayleigh Channel.

B. Receiver Model

The randomly generated data in system can be transmitted through channel can be received with the proposed receiver

model which is shown in Figure (4) given below

.

Fig. 4 Receiver Model

After receiving the transmitted data, the data is been fetched Successive Interference Cancellation detector, so as remove

interference from it. In which dispreading is also performed. Then the output of SIC receiver is used for further

processing. After this data is interleaved and demodulated by demodulator by using same modulation technique as

applied at transmitter side to get our original data.

As we have used channel or medium for transmission so some unwanted noise and disturbance will also get affected to

our data, which decreases the system performance, to improve system capacity we have to combat this interference, we

have used Successive Interference Cancellation (SIC) Multiuser Detection technique at receiver.


September - 2013, pp. 317-324


V. MATLAB SIMULATION RESULT

The implementation of DS-CDMA for multiple users is simulated in MATLAB, here we have analysed the performance

of DS-CDMA system by plotting the graph between BER vs. SNR, and here we have taken SIC detector to improve the

system capacity. By graphs we can analyse that as SNR increases BER decreases which in turn improve the capacity of

system by eliminating MAI. The result is been justified by plotting various graphs between BER vs. SNR for system with

SIC detector and Matched Filter Detector and also there is performance improvement with SIC detector rather than

detector without SIC. Simulation has been done over AWGN channel and for BPSK & QPSK modulation technique. The

number of bits taken for simulation is 10,000.

Fig. 5.1 Comparison graph of MF performance on the basis of different MAI

Fig.5.2 Comparison graph of SIC performance on the basis of different MAI.


September - 2013, pp. 317-324


Fig.5.3 Comparison graph of MF and SIC performance on the basis of processing gain

Fig.5.4 Comparison graph of MF performance with different modulation techniques.

Figure 5.1 and 5.2 shows comparison graph of Matched Filter Detector and Successive Interference Cancellation

Detector on the basis of different MAI, for this graph is been plotted between BER vs. SNR respectively. Here by this

graph we can conclude that for low MAI MF and SIC performance better as MAI increases performance degrades, and

also SIC perform better than Conventional Detector. In Figure 5.3 shows performance of MF and SIC detector and there

comparison on the basis of different processing gain. Then we have simulation result showing performance of DS-

CDMA for different modulation technique here we have taken BPSK and QPSK for both MF detector and SIC detector

in Figure 5.4 and Figure 5.5 respectively, by which we can calculate that DS-CDMA has better performance for BPSK

modulation technique than QPSK.


September - 2013, pp. 317-324


Fig. 5.5 Comparison graph of SIC performance with different modulation technique.

After that in continuation we have comparison graph between SIC and conventional detector for performance

enhancement of DS-CDMA system in AWGN environment for different number of users, by which we can see that our

SIC gives better result than conventional detector, which justify our work in Figure 5.6 and Figure 5.7. By seeing all

these results it justify that sub-optimal non-linear detector (SIC) gives better result in comparison with Conventional and

optimal detector.

Fig. 5.6 Graph shows GUI of SIC for comparison of MF detector SIC detector on basis of no. of user for user 15.


September - 2013, pp. 317-324


Fig. 5.7 Graph shows GUI of SIC for comparison of MF detector and SIC and SIC detector on basis of no. of user for

user 5.

VI. CONCLUSIONS

The inclusion of SIC in a CDMA receiver can significantly improve its performance relative to that of conventional

CDMA receiver where no interference cancellation is attempted. In Code-Division Multiple-Access (CDMA) systems

transmitting over time-varying multipath channels, both Inter-Symbol Interference (ISI) and Multiple-Access

Interference (MAI) arise. From the comparison results, it is clear that successive interference cancellation schemes have

better performance than Conventional Matched Filter, and at the same time, is simpler and less complex than the

optimum detector, and also there is no problem of linearity which arises in sub-optimum linear multiuser detector, in

which as the number of users increases it complexity also increases to remove MAI. And also they perform better with

BPSK modulation schemes than QPSK modulation schemes.

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[7] S. Verd´u, Multiuser Detection. Cambridge Univ. Press, 1998.

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CDMA Multi-user Detector‖ February 24, 2010.

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