2.Simulation Studies of Smart Antenna Under the Influence Of

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Simulation Studies of Smart Antenna under the Influence of

White Signals and DOAs

Mobia Jacob#1

, Shashikumar D#2

Department of electronics and communication Engineering, Department of electronics and communication engineering,

Christ University Christ University

Bangalore, India Bangalore, [email protected]

[email protected]

Abstract The adoption of smart / adaptive antennatechniques in future wireless systems is expected to have a

significant impact on the efficient use of the spectrum, the

minimization of the cost of establishing new wireless

networks, the optimization of service quality and

realization of transparent operation across multi

technology wireless networks [1]. This antenna array used

to increase the channel capacity, extend the coverage, steer

multiple beams to track mobiles. Moreover it can also be

used to reduce multipath fading and co-channel

interference. DOA estimates the direction of arrival of the

signal, using the techniques as MUSIC, ESPRIT etc.

Keywords-DOA, MUSIC, ESPRIT, MIMO, CDMA, Beamforming

I. INTRODUCTION

The main job of DOA (Direction of Arrivals) estimation is to

estimate the direction of arrival of the incoming signals. DOA

depends on many parameters. The parameters are number of

mobile users, number of array elements, inter-elementspacing, number of signals and spatial distribution. As thenumber of mobile subscribers increases rapidly, combined

with a demand for more sophisticated mobile services

requiring higher data rates, the operators are forced to

investigate different methods to put more capacity into their

networks. Smart antennas are foreseen as one of the morepromising technologies for reducing interference and

increasing capacity in CDMA networks [1]. Further, CDMA

is a multiple access interference limited system, any action

that reduces the interference level increases more users in the

system, higher bit-rates, improved quality for the existing

users at the same bit-rates and extended cell range for the

same number of users at the same bit rates, or any arbitrarycombination of these.

II. SMART ANTENNA SYSTEM CLASSIFICATION

Based on the signal processing technique followed at the

baseband output of the antenna array smart antennas can be

grouped into four basic types based on:

1) Beam forming: Through beam forming [2], a smart antenna

algorithm can receive predominantly from desired direction

(direction of the desired source) compared to some undesired

directions (direction of interfering sources).

2) Diversity combining: A major limiting factor in wireless

communication is multipath fading where the amplitude of thereceived signal fluctuates over time. The occurrence of a

deep fade where the signal amplitude becomes very small can

impair the communications link for a conventional or a single

antenna system.

3) Space-time equalization: The preceding two techniques

usually assume that the signal of interest is a narrowbandsignal compared to the coherence bandwidth of the channel

and is thus subjected to flat fading across the bandwidth of the

signal. Multipath fading in wireless communication can also

introduce frequency distortion to the received signal.

4) Multiple Input Multiple Outputs (MIMO) [3]: As the name

suggests this scheme requires array processing at the

transmitter and receiver. There are two different types of

MIMO schemes: one uses spatial multiplexing to enhance data

rate for a given bandwidth (thus, the spectral efficiency) and

the other uses space time coding using diversity combiningtechniques to combat fading.

The system which is used for the operation can be sub divided

into three parts. These are mainly DOA estimation [4], DOA

classification and Beam forming. Each of these performs there

assigned operations.

III.DOA ESTIMATION

In a mobile environment, it is usually assumed that thescatterers surrounding the mobile station are approximately at

the same height as or higher than the mobile. At the base

station side, it is assumed that the base station antenna is

deployed above the surrounding scatterers, typically mounted

on masts placed on rooftops.

The number of DOAs that can be estimated is smaller than the

number of antenna elements. This is a major disadvantage in

environments suffering from large angle spread. If large angle

spread is present, then the point source model is not valid and

inevitably many different DOAs correspond to a single signal

source. In that case spatial structure methods require more
mailto:[email protected]:[email protected]:[email protected]


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antenna elements than the total number of impinging signals

and their multipaths. Spatial structure methods directlyestimate the DOAs of the impinging wavefronts [4]. Once the

DOAs are found, the weight vector necessary to separate the

wavefronts can be determined via beamforming methods. In

this paper, MUSIC algorithm is used to estimate the DOA of

signals.

A.

MUSIC Algorithm

In wireless transmission, the receiving antennas can collect

more signals that can be emitted by several sources; MUSIC

detects frequencies in a signal by performing Eigen

decomposition on the covariance matrix of a data vector Y of

M samples obtained from the samples of the receivedsignal[5]. The key to MUSIC is its data model

sy A v

Where V is a vector of M noise samples, S is a vector of N

signal amplitudes (N=2), and A is the M N Vandermondematrix of samples of the signal frequencies. If we assume a

zero-mean signal and white noise, then the covariance of Y

has the form

2{ }H Hy sR E yy AR A I

Here, {ss }HsR E is the N N signal autocorrelation

matrix, I is the M M identity matrix, and2

is the noise

variance. From the Eigen decomposition ofy

R , we use the

Eigen vectors associated with the N maximum Eigen values todefine the signal subspace (the column space of A), and use

the other Eigen vectors to define the noise subspace, nu . From

the orthogonality of the signal and noise subspaces, finding

the peaks in the estimator function

1( )

[ (w)] [ (w)]H Hn nJ w

a u u a

For various w values yields the strongest frequencies, where

a(w) refers to the columns of A.

MUSIC assumes that the number of samples Mand the

number of frequencies Nare known. The efficiency of MUSIC

is the ratio of the theoretical smallest variance, given by the

Cramer-Rao Lower Bound (CRLB), to the variance of the

MUSIC estimator:

( )

MUSIC( )

var

var

i

i

CRLB w

w

cff

The efficiency does not depend on the total number of

samples, m but does depend on M. As Mincreases, efficiencyand computation time increase. We pick M=8, because larger

values do not significantly improve the efficiency.

B.Analysis of DOA Estimation

For all adaptive array smart antenna simulations, the 5000input signals of the training sequence have signed values of 1

or 1 to simulate a transmitter sending binary values.

Although there are 5000 sampling instants, the results onlyshow up to 200 intervals due to the extremely high rate of

convergence of the system. The step-size parameter for the

LMS algorithm is set to 0.008, to keep simulations as realistic

as possible, for those simulations with more than one

multipath, each multipath experiences a different gain, which

contains both amplitude and phase components. It was found

that the amplitude of the gain had the most effect on the

system, with the phase having little to no effect.

The carrier frequency fc of transmitted trainingsequences is set to 400 MHz, which means the value of the

wavelength is set to 0.75m. To satisfy an element spacing d

of /2 then means that d is set to 0.375m. For simulations with

only one transmitted signal, the propagation delay fromtransmission to reaching the first antenna element is set to

100s, and for those with a second transmitted signal, the

second propagation delay is set at 150s.

IV. SIMULATION RESULT

Using the analysis specification, the simulation is performed

to analyse the behaviour of smart antennas in the presence of

white signals with different DOAs. We have taken twodifferent cases as one white signal and two white signals. In

this two cases we are giving different DOAs as 2, 3, 4. Based

on this, we are taken the steering of a beam.

A. One White Signal Case:

To ensure that the system worked correctly, the first

simulation investigated was the reception of one signal with

the one path that arrives at the base station at an angle of 60o

.A gain with amplitude of 0.5 dB was introduced to the input

signal as it was propagated to the antenna. Figure 1, 3, 5

illustrates that the received signal error converges atapproximately 54 dB sample intervals and reaches 0.01 after

43 intervals. The mean received signal error after convergencelies approximately at 0.0006.

Figure 2, 4, 6 shows that beam pattern of the system

correctly steers the main beam in the direction of 30o

, 60o

withmaximum beam strength of 2. This is due to the signal

experiencing a gain of amplitude 0.5, which reduces the

power of the signal by half. To counter this, the beam adjusts


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its gain to the inverse of the signal power in order to receive a

signal similar to the original signal.

Fig. 1 Smart antenna simulation received signal error for 1 white signal with

2 DOA

0.511.52

30

210

60

240

90 270

120

300

150

330

180

02DOA

DOA1

DOA2

Fig. 2 Smart antenna simulation beam pattern for 1 white signal with 2 DOA

Fig. 3 Smart antenna simulation received signal error for 1 white signal with 3

DOA

0.511.52

30

210

60

240

90 270

120

300

150

330

180

03DOA

DOA1

DOA2

DOA3


Fig. 5 Smart antenna simulation received signal error for 1 white signal with 4DOA

0.511.52

30

210

60

240

90 270

120

300

150

330

180

04DOA

DOA1

DOA2

DOA3

DOA4



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B. Two White Signal Case:

The final smart antenna simulation is the most complex andprovided the most unexpected results. In this simulation we

transmit 2 training sequences, each with three multipath

components. However, second and third multipath

components of each signal are both set to arrive at the antenna

array one sample period behind the first multipath.

Essentially, this means that the 2nd

and 3rd

multipath is arriving

at the base station at the same time but from different

directions.

From figure 7, 9, 11 although there were three multipaths for

each signal in the system, only two sets of received signal

errors are being displayed. That is, only four unique weight

vectors exist. This is because the weight vectors for the

second and third multipaths are exactly the same due to these

signals arriving at the same time. This means that for

multipath components of the same signal that arrive at the

same time, only one weight vector is needed.

After this finding, it was expected that the main beam would

either be directed in the direction of the closest multipath or

the one with the greatest gain. However, the beam pattern

shown in figure 8,10,12 displays the three different beam

patterns but the patterns for the 3rd

multipath of both signals

have two main lobes in the correct directions of the 2nd

and 3rd

multipaths. The gains of these beams are half what they would

normally be and swapped between the multipath components.

The smart antenna simulations confirmed that smart system

have an ability to distinguish between signals of interest and

interferers by directing beams in the directions of the desiredsignals and nulls in the directions of the interferers.

Fig.7 Smart antenna simulation received signal error for 2 white signals with

2 DOAs each

Fig. 8 Smart antenna simulation beam pattern for 2 white signals with 2 DOA

each

Fig.9 Smart antenna simulation received signal error for 2 white signals with

3 DOAs each

1234

30

210

60

240

90 270

120

300

150

330

180

03DOA

DOA1 of sig 1

DOA2 of sig 1

DOA3 of sig 1

DOA1 of sig 2

DOA2 of sig 2

DOA3 of sig 2



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Fig.11 Smart antenna simulation received signal error for 2 white signal with

4 DOA

1234

5

30

210

60

240

90 270

120

300

150

330

180

04DOA

DOA1 of sig 1

DOA2 of sig 1

DOA3 of sig 1

DOA4 of sig 1

DOA1 of sig 2

DOA2 of sig 2

DOA3 of sig 2

DOA4 of sig 2


IV.CONCLUSIONS

In this paper, the problem of estimating directions of arrival

(DOAs) of multiple sources observed on the background ofwhite noise by using MUtiple SIgnal Classification (MUSIC)

algorithm. This demonstrates the systems user oriented

steering abilities. Using this approach reduces the interference

substantially and hence increases the capacity of the system.

Smart antenna simulation is done by considering multiple

paths with multiple directions of arrivals of signals. The

simulations confirmed that smart system have an ability to

distinguish between signals of interest and interferers by

directing beams in the directions of the desired signals and

nulls in the directions of the interferers. If the number of white

signals increasing with direction of arrivals increasing which

results the more chance of increasing the interference andreducing the signal strength.

REFERENCES

[1] Al-Ardi, E.M., R.M. Shubair and M.E. Al-Mualla,

Investigation of high-resolution DOA estimation

algorithms for optimal performance of smart antenna

systems, Proceedings of the 4th

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Conference on 3G Mobile Communication

Technologies, IEEE Xplore Press, pp: 460-464, June

2003.

[2] Khan, Z.I., M.M. Kamal, N. Hamzah, K. Othman and

N.I. Khan,. Analysis of performance for MultipleSignal Classification (MUSIC) in estimating direction of

arrival, Proceedings of the IEEE International RF and

Microwave Conference, IEEE Xplore Press, Kuala

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[3]

Godara, L.C., Application of antenna arrays to mobile

communication, II. Beam-forming and direction-of-arrival considerations, Proceedings IEEE.pp: 1195-

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459.April 1993.

[5]

Lavate, T.B., V.K. Kokate and A.M. Sapkal,Performance analysis of MUSIC and ESPRIT doa

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April 2010

[6] Mouhamadou, M., G. Neveux and P. Vaudon,

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[7] Quyen, T.C., MUSIC for OFDM by using an uniformantenna array with two elements,Proceedings of the 1st

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Telecommunication Systems, Services and Applications,

IEEE Xplore Press, Bali, Indonesia, pp: 149-153,October 2011.

[9] Zhou, L., D. Huang, H. Duan and Y. Chen,A modified

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