Beam Forming in Smart Antenna with Precise Direction of Arrival Estimation Using Improved MUSIC

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Wireless Pers Commun (2013) 71:13531364DOI 10.1007/s11277-012-0879-9

Beam Forming in Smart Antenna with Precise Direction

of Arrival Estimation Using Improved MUSIC

T. S. Ghouse Basha P. V. Sridevi M. N. Giri Prasad

Published online: 12 October 2012 Springer Science+Business Media New York 2012

Abstract Smart antenna is now commonly used in communication systems due to its

high advantages. In order to improve the performance of smart antenna operation, efficient

design of beam forming pattern is required based on the subjected antenna parameters. In the

previous works, beam forming techniques were proposed using hybridization of soft com-

puting techniques, however the precision has not been considered in terms of direction of

arrival (DOA). This paper includes DOA while deriving the beam forming pattern of smart

antenna. To estimate precise DOA, the MUSIC algorithm is improved by introducing a tunedcorrelation matrix after solving the objective model for the matrix. Thus estimated DOA

pattern is more precise as the unwanted side lobes are suppressed when compared to the

conventional DOA pattern. Based on the estimated DOA pattern, the antenna beam forming

pattern is derived as per the required direction of angles. The experimental results show the

performance of the proposed beam forming technique over the previous techniques.

Keywords Smart antennaDOABeam formingTuned correlation matrixGenetic algorithmNeural network

1 Introduction

Over the last few years, wireless cellular communication has accomplished rapid expansion

in the demand for procuring innovative wireless multimedia services for e.g., Internet access,

multimedia data transfer and video conferencing [1]. The acceptance of smart antenna meth-

T. S. G. Basha (B)

K.O.R.M. College of Engineering, J.N.T. University, Anantapur, Kadapa, Andhra Pradesh, India

e-mail: [email protected]

P. V. Sridevi

Andhra University, Visakhapatnan, India


M. N. G. Prasad

J.N.T. University, Anantapur, India


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1354 T. S. G. Basha et al.

ods in future wireless systems is expected to have a substantial impact on the competent use

of the spectrum and the reduction of the cost of establishing new wireless networks [ 2]. A

smart antenna has the ability to diminish noise, increase signal to noise ratio and enhance

system competence [3]. The diversity effect in smart antenna refers to the transmission and/or

reception of manifold RF-waves to increase the data speed as well as to diminish the errorrate[4,5].

The term smart represents the signal processing capability that turn into an imperative

part of the adaptive antenna system, which controls the antenna pattern by adjusting a set

of antenna weights [6]. It is indispensable to boost the channel bandwidth & capacity as

well as to reduce the channel interference. Smart antennas are considered as an effectual

counter measure to achieve these requirements because they provide wide bandwidth, less

electromagnetic interruption, flexibility, less weight, speedy, phase control independent of

frequency, and low propagation loss [7]. These smart antennas dynamically adapt to chang-

ing traffic requirements. Smart antennas are often used at the base station and emit narrow

beams to aid different users [8]. The smart antenna concept is applied to different types of

antenna arrays. Phased arrays, switched multi-beam antennas, and adaptive array antennas

are normally included under the smart antenna concept with the only condition of appending

the possibility to control the radiation pattern by some means [9].

The two major functions of smart antenna are,

Direction of arrival (DOA) estimation. Beam forming.The smart antenna system measures the direction of arrival of the signal. This system focuses

on identifying a spatial spectrum of the antenna or sensor array, and estimating the DOA fromthe peaks of this spectrum [10]. The smart antenna technique deals with radiation pattern

manipulation. This technique allows main lobe positioning towards a preferred direction,

while manipulating the nulls resulting in a signal to noise ratio (SNR) maximization or

interference alleviation [11].

Particularly, using beam forming techniques at the receiver, two or more transmitters can

share the same traffic channel to keep in contact with the base station at the same time. An

adaptive antenna array is employed at the base station to create numerous antenna beams

simultaneously. Each beam captures one transmitter by automatically pointing its pattern

towards that transmitter while nulling other co-channel transmitters and multipath signals

[12].The rest of the paper is organized as follows. The related works are briefly reviewed in

Sect. 2; proposed technique with sufficient mathematical models and illustrations are detailed

in Sects.3and4; implementation results and comparisons are discussed in Sects. 5and6

concludes the paper.

2 Related Work

Some of the recent works related to smart antenna direction of arrival and beam forming areas discussed in this section.

Jain et al. [13] have presented a concise description of smart antenna (SA) system. SAs

can place nulls in the direction of invaders by adaptive adjusting of weights related to each

antenna element. Thus, SAs negate most of the co-channel interference resulting in enhanced

quality of reception and lower dropped calls. By means of direction of arrival (DOA) algo-

rithms, SAs can also find the user within a cell. Their research elucidates the architecture,

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Beam Forming in Smart Antenna with Precise Direction 1355

development and how the smart/adaptive antenna differs from the basic design of antenna.

Also, they have described about the radiation pattern of the antenna and why it was highly

adopted in its relative field. The competence of smart/adaptive antenna is more usable to

Cognitive Radio and OFDMA system.

The performance of algorithms namely, Direct Matrix Inversion algorithm (DMI) andConstant Modulus Algorithm (CMA) has been compared by Raghavendra et al. [14]. The

main benefit of those algorithms is its simplicity with a negligible loss of accuracy. Here,

the design of an adaptive antenna array after receiving the signals from the preferred and

meddling directions has been depicted. Then, the weight vector has been evaluated to reduce

the error, which offers an accurate beam pattern to each contributor. Moreover, the mean

square error and array factor have been analyzed and compared for those two algorithms.

Kumari et al.[15] has proposed direction of arrival estimation using ESPRIT & MUSIC

algorithms. These two techniques have higher resolution and accuracy. The simulation results

have exposed that the performance of both MUSIC and ESPRIT has been improved with more

elements in the array, with large snapshots of signals, and greater angular separation between

the signals. These enhancements were seen in form of the sharper peaks in the MUSIC and

smaller errors in angle detection in the ESPRIT. Specifically, MUSIC was more stable, pre-

cise and provided high resolution and this includes new possibility of user separation via

SDMA and can be extensively utilized in the design of smart antenna system.

Lavate et al. [16] has investigated the DOA estimation algorithms such as MUSIC and

ESPRIT, which are extensively used in the design of smart antenna system. MUSIC and

ESPRIT algorithms offer high angular resolution and thus, they were explored much in

detail by changing different parameters of smart antenna system. However, their simulation

has revealed that MUSIC algorithm was highly precise and stable and provides high angularresolution than the ESPRIT. Thus, MUSIC algorithm has been broadly employed in mobile

communication to measure the DOA of the arriving signals.

From the review it can be seen that the MUSIC algorithm was widely used to estimate the

direction of arrival. While exploiting the algorithm for smart antenna design, more precise

estimation is essential. However, the conventional MUSIC algorithm suffers because of lack

of suppression in side lobes. In this work, we improve the MUSIC algorithm in such a way

that the developed MUSIC spectrum has the advantage of reduced side lobes and well-struc-

tured main lobes. This lead to precise DOA estimation, which will aid in efficient design of

smart antenna, of received signals.

3 The MUSIC Algorithm

The MUSIC algorithm [17] is a popular DOA estimation techniques works based on corre-

lation matrix of the general data model,

y= Ax+n (1)A= [a1a2. . . aM]T (2)x= [x(1)x(2) . . .x(M)] (3)

where, y is the observed complex data vector, x is the unobserved emitted signal vector

(given in equation),n is the noise vector and Ais the steering vector [given in Eq. (1)]. The

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correlation matrixxis given as

R= E[yyH] (4)R= E[(Ax+n)(Ax+n)H] (5)R= E[x A A

H

xH

] +E[nnH

] (6)R= Rs+2I (7)

where, E[]is the expectation function in probability theory,2 is the noise variance param-eter,Iis the Identity matrix and Rs is the signal covariance matrix, which can be given as

Rs= E[x A AHxH] (8)where,

A.AH

= E|a21 | 0 . . . 0

0 E|a2

2 | . . . 00 0 . . . E|a2M| (9)

Based on the higher order statistical measures, the MUSIC algorithm calculates pseudo spec-

trum of the signal as follows

P()= 1NMm=1 |xH()m |2

(10)

P()= 1xH()Qn QHn s()

(11)

P()= 1|QHn x()|2 (12)

where, mis eigenvector andQ nis the matrix of eigenvectors. From the pseudo spectrum, the

direction in which the maximum power reception can be determined, which in turn, antenna

design parameters can be used. However, as previously mentioned, the MUSIC algorithm

suffers due to side lobe domination due to noise intervention in the channel. This is because of

less precise and coarsely tuned correlation matrix, which is estimated from the data model as

given in Eq. (4). This work overcomes the drawback by introducing a precise and well-tuned

correlation matrix to be derived from the data model. In order to determine the matrix, firstly

we develop an objective model and then we solve the model by using GA.

3.1 MUSIC Improvement for Precise DOA

If R be the correlation matrix to be tuned and wbe the tuning matrix of improved MUSICalgorithm and R be the correlation matrix of MUSIC algorithm, then R can be determined

as follows

R=w R: |w| = Z1 Z2 (13)S.t.

1.

Z1= Z2 (14)2.

Z1=|R|; if NR= Nc

NR; otherwise (15)

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where,NRandNcare the row size and column size ofR . Based on the above model mentioned

in Eq. (13), the objective model can be developed as

F= |Tpeak

| |Tpeak

||Tpeak|1r=0

|Tpeak|1s=0

Tpeakr Tpeak2 (16)

where,

T

peakr

=max P() : 0r ND1 (17)

Tpeak={K()}

Tpeak

(18)

In Eq. (16),Fis the objective function to be solved to determineR,Tpeak is a set of targetedpeak values of the spectrum, which can be said as major lobes of beam pattern, Tpeak isa complementary set ofTpeak, which can be said as unwanted side lobes of beam pattern.

In Eq.(17),P() is the pseudo spectrum of improved MUSIC algorithm determined with

respect to R andND is the number of known direction of arrivals. K()in Eq. (18) depictsthe number of peaks in

P(). The design of objective model intends to determine wopt, which

is an optimizedw to be used in Eq.(13)to accomplish precise DOA estimation.

3.2 Determination of Tuned Correlation Matrix

In order to perform this, we exploit a simple GA operation. The GA process in determining

wopt is as follows

Step 1. Generate chromosome Xi j: 0 j |w| 1, 0i Np1 in such a way thatXi j [0, 1], where Np is the population size

Step 2. Determine fitness ofXi j using Eq. (16)

Step 3. Perform selection process as follows

Xpar ent

k =arg minXi j Fi: 0k Np/21 (19)

Step 4. Perform crossover and mutation to obtainXnewkStep 5. Fill up population pool by X

par entk and X

newk and go to Step 2, until termination

criterion meets. Generally, the termination criterion is meeting up of maximum

number of generations.

Once the termination criterion is met, the best chromosome from population is extracted

andwopt is determined using the model

wopt (z1,z2)=Xbest(z1Z2(Z2z2)): 0 z1 Z11; 0 z2 Z21 (20)

where, Xbest is the best chromosome obtained after the execution of entire GA process. By

substitutingwopt in Eq.(13)under the considerationw=wopt, the tuned correlation matrixis obtained.

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Fig. 1 Process flow diagram of

proposed beam forming

technique

ImprovedMUSIC

Multi-objective

model

Beam pattern

model

Design

Database

Training

Database

Neural Network

Online process

In GA Process

4 Genetic Algorithm (GA)-Neural Network (NN) Based Smart Antenna Beam

Forming

The beam forming process follows as similar to as that of our previous paper [18], however,

we include the estimated direction of arrival using the improved version of MUSIC algorithm

in this work. The block diagram of the proposed beam forming technique is given below.

In Fig.1, the design database holds the information about different angle of arrivals of

signals to be considered while determining antenna parameters. For every such angle, the

GA process is initiated with antenna parameters as chromosomes. The chromosomes are

evaluated by the multi-objective model, which is a function of improved MUSIC spectrum

and the previous model. The model includes DOA estimation as follows

F(i)=arg minP(), f(Y)

(21)

where, f(Y)is the objective model used in (21) and P()is the improved pseudo spectrumof MUSIC algorithm,Yp=

xp, p,Dp,Mp

, where, xp,pDp and Mp are the genes of

the pth chromosome, pis the number of genes in the chromosome. Once the training dataset

is generated, neural network is fed by the dataset to work in online. Given a direction of

arrival, the trained neural network provides the best antenna parameters to be set so as to

maximize the efficiency of signal reception.

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Table 1 Performance measures between the proposed and conventional beam forming technique

Sl. No Angle of signal Peak signal to interference ratio(dB) Signal to interference ratio (dB)

arrival in degrees

Proposed beam Conventional Proposed beam Conventional

forming technique Beam forming forming technique Beam formingtechnique technique

1 10 36.634 35.42 0.76439 0.75076

2 40 37.362 35.9602 0.76149 0.75211

3 70 36.172 35.42 0.76439 0.74762

4 100 39 36 0.547250 0.53657

5 Result and Discussions

The improved MUSIC algorithm is done and then combined the implementation with

the beam forming technique in MATLAB and the results are compared with the conven-

tional MUSIC algorithm. For a known set of signals under different angles of arrivals, the

conventional [17] and improved MUSIC pseudo spectrum is obtained and given in Fig.2.

In Fig.2,it can be seen that the conventional MUSIC spectrum has main lobes but along

with side lobes, which are unwanted and disturbing, whereas the improved MUSIC spectrum

has relatively higher magnitude on the desired main lobe and suppressed side lobes. As the

direction of arrival of desired signal can be estimated well without the unwanted side lobes,

the improved MUSIC spectrum is used for beam forming.

5.1 Beam Forming Pattern Analysis

The beam forming pattern is analyzed under various set of signals (does not coincide with

the signals used in MUSIC spectrum comparisons) to be transmitted. The results are shown

in Fig.3.

5.2 Comparison

The following Table 1 shows the comparison of Performance measures between the proposedmethod and conventional beam forming technique[18] when the angle of arrival of signal is

10, 40, 70 and 100 in terms of peak signal to intereference ratio and signal to interferenceratio.

5.2.1 Discussion

To evaluate the performance of the technique, we select four signals having the angle of

arrival as 10, 40, 70 and 100 respectively. From Fig. 3, it can be seen that all the main

beams target towards the angle of arrival, however side lobes are suppressed in proposedbeam forming whereas side lobes intervention is there in conventional beam forming. More-

over, to accomplish such a better effect, the antenna parameters to be selected have also been

found out using the proposed technique. Table1shows the performance comparison over

the proposed and conventional method. In Fig.3,one can see the side lobes in the proposed

method also, however when compared the conventional method[18], which is using tradi-

tional MUSIC spectrum, the magnitude of the side lobes are relatively less. This can be seen

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Fig. 2 Comparison of

conventional and improved

MUSIC pseudo spectrum for

known signals arriving at

different direction of arrivals

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Fig. 3 Beam forming pattern for (i) signal set 1, (ii) signal set 2, (iii) signal set 3 and (iv) signal set 4

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from the tabulated details (in Table 1) that the concerned peak signal to interference ratio

and the signal to interference ratio are relatively lesser than the conventional method. This

proves that the MUSIC spectrum has been necessarily enhanced to further deploy in beam

forming technique.

6 Conclusion

This work is an effort made to improve the conventional beam forming method [18]. The

work firstly introduced an objective model to improve the MUSIC spectrum by suppressing

the unwanted side lobes. Secondly, the improved MUSIC spectrum is included in the previ-

ous beam forming model and solved the problem as a multi-objective model. Thus improved

beam forming technique was implemented and experimented under various signal arriving

scenario. The proposed beam forming technique showed relatively better performance when

compared to the previously proposed beam forming technique. This was initially proved by

analyzing the improved MUSIC spectrum and the traditional MUSIC spectrum in which all

the desired main lobes accomplished maximum magnitude with suppressed unwanted side

lobes, whereas the conventional MUSIC spectrum has low magnitude main lobes with side

lobes. This in turn shows its positive impact on the beam forming method and hence the

method leads to relatively high signal to interference ratio, peak signal to interference ratio.

Moreover, the experimental results have proved that the time consuming by the proposed

method for beam forming is again relatively lesser than the conventional method.

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Author Biographies

T. S. Ghouse Bashais presently working as an Associate Professor and

Head of Department of Electronics and Communication Engineering

in K.O.R.M College of Engineering, Kadapa, and pursuing the Ph.D.

from J.N.T.University, Anantapur under supervision of Dr. P.V. Sridevi

and Prof. M.N.Giri Prasad. He carried out his M.Tech project workin Defence Research and Development Laboratory, Hyderabad, during

2005-2006 and working in teaching field since 2002 in different cad-

res. He received his B.Tech and M.Tech from the Department of Elec-

tronics and Communication Engineering from J.N.T.U University and

Nagarjuna University respectively. His areas of interest include micro-

wave antennas, digital signal processing and mobile communications.

P. V. Sridevi is presently working as an Associate Professor at the

Department of Electronics and Communication Engineering, And-

hra University College of Engineering, Visakhapatnam. She is having

teaching experience of 20 years. She received her Ph.D. from And-

hra University in 1997. She received her B.Tech in Electronics and

Communication Engineering from V.R. Siddartha Engineering College,

Vijayawada in 1986 and M.E. in Applied Electronics from PSG Col-

lege of Technology, Coimbatore in 1988. Her areas of interest include

antennas, microwaves, VLSI and image processing.

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M. N. Giri Prasad is presently working as a Professor at the Depart-

ment of Electronics and Communication Engineering, JNT Univer-

sity College of Engineering, Anantapur, Andhra Pradesh, India. He is

a life member of ISTE, IEI and NAFEN. He received B.Tech from

J.N.T.University College of Engineering, Anantapur, Andhra Pradesh,

India in 1982, M.Tech from Sri Venkateshwara University, Tirupati,Andhra Pradesh, India in 1994 and Ph.D. from J.N.T.University, Hy-

derabad, Andhra Pradesh, India in 2003. His research areas are biomed-

ical instrumentation, wireless communications, image processing and

signal processing, etc. He is having around 25 National and Interna-

tional publications to his credit.

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Beam Forming in Smart Antenna with Precise Direction of Arrival Estimation Using Improved MUSIC

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