Standardization and System Integration of Smart Antennas

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Standardization and System Integration of Smart Antennas

A Project Report

Submitted in partial fulfillment of the requirements for the award of thedegree of

BACHELOR OF ENGINEERING

IN

ELECTRONICS AND COMMUNICATIONSubmitted by:

SAKET SUMAN (4BE/4081/07)

NEHAL GUPTA (4BE/4004/07)

RITIKA KAPOOR (4BE/4049/07)

SHRUTI MEHROTRA (4BE/4011/07)

BIRLA INSTITUTE OF TECHNOLOGY, MESRA

(Deemed University u/s 3 of UGC act, 1956)

B.I.T. EXTENSION CENTRE, JAIPUR

BISR Campus, 27-Malviya Industrial Area, JAIPUR-302017



DECLARATION CERTIFICATE

T

his is to certify that the work presented in the project reportentitled Standardization and System Integration of Smart

Antennas in the partial fulfilment of the requirement for the

award of degree of Bachelor of Engineering in Electronics &

Communication of Birla Institute of Technology, Mesra, Ranchi,

Jaipur Centre is an authentic work carried out under my

supervision and guidance.

The work is found satisfactory and upto the mark.

Date -06-12-2010

Project Guide

(Mr. Deepak Chaturvedi)

Department of Electronics and Communication

Birla Institute of Technology

Jaipur Campus



Certificate of Approval

The foregoing project entitled Standardization and System

Integration of Smart Antennas, is hereby approved as a

creditable study of research topic and has been presented in

a satisfactory manner to warrant its acceptance as a pre

requisite to the degree for which it has been submitted.

It is understood that by this approval the undersigned do not

necessarily endorse any conclusion drawn or the opinion

expressed therein, but they approve the project for the

purpose it has been submitted.

Examiner



ACKNOWLEDGEMENT

It is a great pleasure to have an opportunity to extend heart full thanks to

everyone who has helped throughout the successful completion of the project

.We convey our gratitude to all those who have helped u a stage where we

have immense confidence to set to launch our career in this competitive world.

We express our sincere gratitude towards Mr. Abhinav Dinesh,Director ,B.I.T

Mesra , Ranchi ,Jaipur Campus and Mr. S.P. Sarkar , H.O.D ,Electronics and

Communication for providing us the opportunity to undertake this project.We

are grateful to Mr. Deepak Chaturvedi ,Project Guide who was always there

when needed.

We acknowledge the role of our institute , our respected lecturers who have

played a successful role in shaping our career .We express our profound

gratitude to all the teachers for gently guiding and paving our way towards a

bright career throughout our course.

SAKET SUMAN (4BE/4081/07)

NEHAL GUPTA (4BE/4004/07)

RITIKA KAPOOR (4BE/4049/07)

SHRUTI MEHROTRA (4BE/4011/07)



ABSTRACT

The project presents an analysis of different antennas based

on their radiation patterns and then desigining the selected

antenna for implementing smartness along with system

integration.The detailed analysis of various antennas is aimed

at scrutinizing the best antenna on the radiation scale

parameter.Then implementing the smartness by

implementing LMS algorithms.The feasibility of smart

antennas in different practical domains also to be studied.



CONTENTS

1. Introduction

2. Evolution from Omnidirectional to Smart Antenna2.1 Omnidirectional Antennas

2.2 Smart Antenna Systems

2.3 R elative benefits/tradeoffs of switched beam and adaptive array

systems

3. Problems under investigation

3.1 Improvements and Benefits

3.2 Cost Factor

4. Work Done

4.1 Yagi Uda Antenna

4.2 Microstrip Antenna

4.3 Fractal Antenna

5. R esearch Issues

6. LMS Algorithm

7. Output of Program

8. Designed Fractal Antenna

9. Conclusion

10. R eferences



1. INTRODUCTION

Global demand for voice, data and video related ser vices continues to

grow faster than the required infrastructure can be deployed. The

universal and spread use of mobile phone ser vice is a testament to the public¶s acceptance of wireless technology. Over the last few years thedemand for ser vice provision via the wireless communication bearer has

risen beyond all expectations. Somewhat simplistically, the maximum

range of such systems is determined by the amount of power that can betransmitted (and therefore received) and the capacity is determined by the

amount of spectrum (bandwidth) available. The two basicp roblems thatarise in such systems are:1. How to acquire more capacity so that a larger number of customers

can be ser ved at lower costs maintaining the quality at the same time,

in areas where demand is large (spectral efficiency). 2. How to obtain greater coverage areas so as to reduce infrastructure

and maintenance costs in areas where demand is relatively small

(coverage).

There are many situations where coverage, not capacity, is a more

important issue. The International Mobile Telecommunications -2000 (IMT2000) and the European Universal Mobile TelecommunicationsSystem (UMTS) are two systems among the others that have been

proposed to take wireless communications into this century. The spatial

dimension can be exploited as a hybrid multiple access techniquecomplementing FDMA and TDMA.



2. EVOLUTION FROM OMNIDIRECTIONAL TO SMART ANTENNA

An antenna in a telecommunications system is the port through which radiofrequency (R F) energy is coupled from the transmitter to the outside world for transmission purposes, and in reverse, to the receiver from the outside world for

reception purposes [4]. To date, antennas have been the most neglected of a llthe components in personal communications systems. Yet, the manner in which

radio frequency energy is distributed into and collected from space has a profound influence upon the efficient use of spectrum, the cost of establishing

new personal communications networks and the ser vice quality provided bythose networks.

2.1 Omnidirectional Antennas

The simple dipole antenna radiates and receives equally well in all directions

(direction here being referred to azimuth) but to find its users, this single-

element design broadcasts omnidirectionally in a pattern resembling ripples

radiation outward in a pool of water .

2.2 Smart Antenna Systems

A smart antenna is a phased or adaptive array that adjusts to the environment.

That is, for the adaptive array, the beam pattern changes as the desired user and

the interference move, and for the phased array, the beam is steered or different

beams are selected as the desired user moves. Phased array or multibeam

antenna consists of either a number of fixed beams with one beam turned on

towards the desired signal or a single beam (formed by phase adjustment only)

that is steered towards the desired signal. Adaptive antenna array is an array

of multiple antenna elements with the received signals weighted and combined



to maximize the desired signal to interference and noise (SINR ) ratio. This

means that the main beam is put in the direction of the desired sign al while nulls

are in the direction of the interference.

A smart antenna system combines multiple antenna elements with a signal

processing capability to optimize its radiation and/or reception pattern

automatically in response to the signal environment. Smart antenna systems are

customarily categorized as either switched beam or adaptive array systems.

Switched beam antenna system form multiple fixed beams with heightened

sensitivity in particular directions. The adaptive system takes advantage of its

ability to effectively locate and track various types of signals to dynamically

minimize interference and maximize intended signal reception.

2.3R elative benefits/tradeoffs of switched beam and adaptive array systems



In terms of radiation patterns, switched beam is an extension of the cellular

sectorization method in which a typical sectorized cell site has three 120 -degree

macro-sectors. The switched beam approach further subdivides macro-sectors

into several micro-sectors thus improving range and capacity. The design of

such systems involves high-gain, narrow azimuth beam width antenna elements.

By adjusting to an R F environment as it changes (or the spatial origin of

signals), adaptive antenna technology can dynamica lly alter the signal patterns

to optimize the performance of the wireless system.

The adaptive approach utilizes sophisticated signal processing algorithms to

continuously distinguish between desired signals, multipath and interfering

signals as well as calculate their directions of arrival. This approach

continuously updates its beam pattern based on changes in both the desired and

interfering signal locations. The ability to smoothly track users with main lobes

and interferers with nulls insures that the link budget is constantly maximized



(there are neither micro-sectors nor predefined patterns).

The benefits and tradeoffs of switched beam and adaptive array systems can be

summarized as follows:

y Integration-Switched beam systems are traditionally des igned to retrofit

widely deployed cellular system. They have been commonly

implemented as an add-on technology, that intelligently addresses the

needs of mature networks.

y R ange/Coverage-Switched beam systems can increase base station range

from 20 to 200% over conventional sectored cells, depending on

environmental circumstance and hardware/software used.

y Interference Suppression-Switched beam antennas suppress interference

arriving from directions away from the active beam¶s center . Because beam patterns are fixed, however, actual interference rejection is often the

gain of the selected communication beam pattern in the interferer¶s

direction. Adaptive antenna approach offers more comprehensive

interference rejection. Also, because it transmits an infinite , rather than

finite number of combinations, its narrower focus creates less interference

to neighboring users than a switched-beam approach.

y Cost/Complexity-In adaptive antenna technology more intensive signal

processing via DSP¶s is needed and at the same time the installation costs

are higher when compared to switched beam antennas.

3. PROBLEMS UNDER INVESTIGATION



The introduction of smart antennas has a large impact on the

performance of cellular networks. It also affects many aspects of both

the planning and deployment of mobile systems.

3.1 Impro

vements and Benefits:

Capacity increase- The principle reason for the growing interest in

smart antennas is the capacity increase. Smart antennas will on

average, by simultaneously increasing the useful received signal

level and lowering the interference level, increase the SIR.

Range Increase - In rural and sparsely populated areas radio

coverage rather than capacity will give the premises for base

station deployment. Because smart antennas will be more directivethan traditional sector or omnidirectional antennas, a range increase

potential is available. This means that base stations can be placed

further apart, potentially leading to a more cost -efficient

deployment. The antenna gain compared to a single element

antenna can be increased by an amount equal to the number of

array elements.

New Services-When using smart antennas the network will have

access to spatial information about users. This information can beused to estimate the positions of the users much more accurately

than in existing networks. Positioning can be used in ser vices such

as emergency calls and location-specific billing.

Security-It is more difficult to tap a connection when smart

antennas are used. To successfully tap a connection the intruder

must be positioned in the same direction as the user as seen from

the base station.

Reduced Intersymbol Interference(ISI)- Multipath propagation

in mobile radio environments leads to ISI. Using transmit and

receive beams that are directed towards the mobile user of interest

reduces the amount of multipaths and therefore the inter -symbol-

interference.



3.2 Cost Factors:

Transceiver Complexity-A smart antenna transceiver is much

more complex.

The antenna will need separate transceiver chainsfor each of the array antenna elements and accurate real -time

calibration of each of them. In addition, the antenna beamforming

is a computationally intensive process.

Resource Management-Smart antennas put new demands on

network functions such as resource and mobility management.

When a new connection is to be set up or the existing connection is

to be handed over to a new base station, no angular informati on is

available to the new base station and some means to find themobile station is necessary.

Physical Size - For the smart antenna to obtain a reasonable gain,

an array antenna with several elements is necessary. The necessary

element spacing is 0.4-0.5 wavelengths.

4. WORK DONE

The work done in VII Semester comprises of designing of various types of

antennas either on hardware or using software.

4.1 Yagi Uda Antenna:

This antenna consists of three elements: the director, the reflector and the

driven element. The driven element is an active element while the other two

are passive elements. The feeding is given at the driven element. The antenna

was designed using the polar plot software. All elements usually lie in the

same plane, supported on a single beam. There are no simple formulas for designingYagi-Uda antennas due to the non-linear relationships between

physical parameters such as element length, diameter and position, and

electrical characteristics such as input impedance and gain, but performance can

be estimated by computer simulation. Consequently, antennas are designed

empirically by trial and error , often based on existing designs, and checked by

direct measurement, or by computer simulation.



4.2 Microstrip Antenna:

A patch antenna is a narrowband, wide- beam antenna fabricated by etching

the antenna element pattern in metal trace bonded to an insulating die lectric

substrate with a continuous metal layer bonded to the opposite side of the

substrate which forms a groundplane. Common microstrip antenna radiator shapes are square, rectangular, circular and elliptical, but any continuous shape

is possible. Patch arrays can provide much higher gains than a single patch at

little additional cost.

Ie3d-

Ie3d is a powerful full-wave EM design package for all aspects of high

frequency applications. It is based upon 3D integral equation method of moment

for high accuracy and high efficiency full -wave EM simulations. It is not just

for planar structures, it can also handle f ull 3D structures.

It is not limited by uniform grids and shapes of the structures. It is much more

capable, accurate, efficient and flexible than other EM simulators.

MGR ID has been the standard layout editor for Ie3d. It allows a user t o create a

structure as a set of polygons.

The antenna was designed using the Ie3d software following the steps:

y The dimensions were: length=31mm, width=20mmy The dielectric constant=3.4

y Frequency of operation=2.4 GHz.

y After providing the feed point, meshing is performed and the simulation

done.

y Plots are generated of S-parameters and Smith Chart.

y There is repeated simulation after providing a new feed point each time to

check the necessary conditions.

4.3 Fractal Antenna

A fractal antenna is an antenna that uses a fractal, self-similar design to

maximize the length, or increase the perimeter (on inside sections or the outer

structure), of material that can receive or transmit electromagnetic

radiation within a given total surface area or volume. Not all fractal antennas



work well for a given application or set of applications. Computer search

methods and antenna simulations are commonly used to identify which fractal

antenna designs best meet the need of the application. Two fractal antennas, a

transmitter and a receiver were made using insulated copper plates and

aluminium wires.

5. RESEARCH ISSUES

1. The first research issue is cost, including the cost of power . Multiple

antennas in the handset not only increase the dollar cost of the handset,

but also increase the power and thus decrease battery life. The number of

required receiver chains must be reduced because the R F electronics and

the A/D converter required with each antenna are expensive. One method being considered is a low-cost phased array. Cost is limiting the number

of antenna elements that can be used.

2. The second key research issue is size. Large base station arrays are

difficult to deploy for aesthetic reasons, and multiple external antennas on

terminals are generally not practical. Issues of gain and efficiency and the

effect of hand placement on the terminal need research.

3. The third issue is diversity, which, is needed for multipath mitigation. For

diversity, multiple antennas are needed on the base stations and/or

terminals. Spatial diversity is difficult to achieve in point -to-pointsystems where a near line-of-sight exists between the transmitter and

receiver, and, further, at higher frequencies, sufficient spatial separation

does not appear feasible.

4. A fourth issue is signal tracking, i.e., determining the angle -of-arrival of

the desired signal with phased arrays to determine which beam to use and

adjusting the weights with adaptive arrays to maximize the desired signal -

to-noise-plus-interference ratio in the output signal.

5. A fifth issue is spatial-temporal processing, i.e., equalization of

intersymbol interference due to delay spread at high data rates, with

cochannel interference suppression. The use of OFDM is being

considered .

6. A sixth issue involves putting the necessary hooks in the standards such

that smart antenna technology can be used effe ctively. In second

generation cellular systems, ANSI-136 and IS-95, implementing smart



antennas had problems because the standards did not consider their use.

In particular, ANSI-136 required a continuous downlink signal to all

three users in a frequency channel, which precludes the use of different

beams for each of these three users. In IS-95, there is a common

downlink pilot, which also precludes the use of different beams for eachuser, as all users need to see the pilot.

7. The seventh issue is vertical integration or an interdisciplinary approach.

R esearch on smart antennas will require multiple factors/expertise to be

considered-smart antennas cannot be studied in isolation.

6. LMS ALGORITHM

%% LMS Algorithm %%%%

%%%%%%%%%%%%%%%%%%%

%-----Givens-----%

clear all;

clc;

clf;

d = .5; % element spacing in terms of wavelength d = lambda/2

% N = input(' How many element do you want in uniform linear array? '); %

number of elements in array

N= 16;

thetaS = input(' What is the desired users AOA (in degrees)? ');

thetaI1 = input(' What is the interferers AOA(in degrees)? ');

thetaI2 = input(' What is the interferers AOA(in degrees)? ');

%----- Desired Signal & Interferer -----%

T=1E-3;

t=(1:100)*T/100;

it=1:100;

S=cos(2*pi*t/T);

thetaS = thetaS*pi/180; % desired user AOA

I = randn(1,100);

thetaI1 = thetaI1*pi/180; % interferer AOA

thetaI2 = thetaI2*pi/180; % interferer AOA



%----- Create Array Factors for each user's signal for linear array -----%

vS = []; vI = [];

i=1:N;

vS=exp(

1 j*(i-

1)*2*pi*d*sin(thetaS))

.';

vI1=exp(1 j*(i-1)*2*pi*d*sin(thetaI1)).';

vI2=exp(1 j*(i-1)*2*pi*d*sin(thetaI2)) .';

vI = vI1+vI2;

%----- Solve for Weights using LMS -----%

w = zeros(N,1); snr = 20; % signal to noise ratio

X=(vS+vI);

R x=X*X';mu=1/(real(trace(R x)))

%mu = input('What is step size?')

wi=zeros(N,max(it));

oldmu = mu;

for n = 1:100;

mu(n) = oldmu/(1-(oldmu^(n+1)));

oldmu = mu(n);

end

for n = 1:length(S)

x = S(n)*vS + I(n)*vI;

%y = w*x.';

y=w'*x;

e = conj(S(n)) - y; esave(n) = abs(e)^2;



% w = w +mu*e*conj(x);

w=w+mu(n)*conj(e)*x;

wi(:,n)=w;

yy(n)=y;

mu(n);

x1 = S(n)*vS + I(n)*vI;

%y = w*x.';

y1=w'*x;

e1 = conj(S(n)) - y1; esave(n) = abs(e1)^2;

% w = w +mu*e*conj(x);

w1=w+mu(n)*conj(e1)*x1;wi(:,n)=w1;

yy1(n)=y1;

end

w = (w./w(1));% normalize results to first weight

w1 = (w1./w1(1))

%----- Plot R esults -----%

theta = -pi/2:.01:pi/2;

AF = zeros(1,length(theta));

% Determine the array factor for linear array

for i = 1:N

AF = AF + w(i)'.*exp(1 j*(i-1)*2*pi*d*sin(theta));

end

AF1 = zeros(1,length(theta));

for i = 1:N

AF1 = AF1 + w1(i)'.*exp(1 j*(i-1)*2*pi*d*sin(theta));

end



figure;

plot(theta*180/pi,abs(AF)/max(abs(AF)),'k')

xlabel('AOA (deg)')

ylabel('|AF_n|')

axis([-90 90 0 1.1])

set(gca,'xtick',[-90 -60 -30 0 30 60 90])

grid on

hold on

plot(theta*180/pi,abs(AF1)/max(abs(AF1)),'g')

hold off

figure;

plot(it,S,'k',it,yy,'k--',it,yy1,'r*')

xlabel('No. of Iterations')

ylabel('Signals')

legend('Desired signal','Array output')

disp('%------------------------------------------------------------------------%')

disp(' ')

disp([' The weights for the N = ',num2str(N),' ULA are:'])

disp(' ')

for m = 1:length(w)disp([' w',num2str(m),' = ',num2str(w(m))])

end

disp(' ')

figure;

plot(it,abs(wi(1,:)),'bx',it,abs(wi(2,:)),'go',it, abs(wi(3,:)),'ms',it,abs(wi(4,:)),'r + ','markersize

',2)

xlabel('Iteration no.')

ylabel('|weights|')

legend('w1','w2','w3','w4')title('adaptation of weights')

figure;plot(it,esave,'k')

xlabel('Iteration no.')

ylabel('Mean square error')



Output Of the Matlab Program

Input Parameters

What is the desired users AOA (in degrees)? 60

What is the interferers AOA(in degrees)? 90



What is the interferers AOA(in degrees)? 45

mu = 0.0171

w1 = 1.0000

-0.9404 + 0.3945i

0.7768 - 0.7632i

-0.4822 + 1.0941i

0.0326 - 1.3173i

0.5295 + 1.3261i

-1.0740 - 1.0496i

1.4310 + 0.5193i

-1.4799 + 0.1228i

1.2160 - 0.6881i

-0.7484 + 1.0348i

0.2315 - 1.1265i

0.2197 + 1.0195i

-0.5690 - 0.7962i

0.8318 + 0.5001i

-1.0122 - 0.1250i

The weights for the N = 16 ULA are:

w1 = 1

w2 = -0.94035+0.39454i

w3 = 0.77677-0.76317i



w4 = -0.48223+1.0941i

w5 = 0.032613-1.3173i

w6 = 0.5295+1.3261i

w7 = -1.074-1.0496i

w8 = 1.431+0.51928i

w9 = -1.4799+0.12278i

w10 = 1.216-0.68812i

w11 = -0.74841+1.0348i

w12 = 0.23149-1.1265i

w13 = 0.21971+1.0195i

w14 = -0.56901-0.79618i

w15 = 0.83181+0.50008i

w16 = -1.0122-0.12498i



Designed Fractal Antenna



8. CONCLUSION

The following conclusions summarize our study:

y To induce smartness into an antenna, we studied the radiation patterns of

various antennas like yagi uda, microstrip, horn and fractal antennas andwe now have to choose an antenna in which smartness introduction is

feasible.

y The smartness would be induced using a stepper motor and parallel port

programming in visual basic or C language.

y The actual microstrip patch antenna designed using Ie3d software with

accurate dimensions of the patch and the dielectric constant gives a

uniform directional pattern.

y For a smart antenna, the return loss has to be less than -20db, the real part

of impedence is nearly 50 ohms and the imaginary part almost zero.

y A practical fractal antenna designed using insulated copper plates and

aluminium wires and the corresponding radiation pattern was generated.

y The designed microstrip antenna was found to work best at 2.4GHz.



9.REFERENCES

1. Smart Antenna Systems for Mobile Communications by

ecole polytechnic.

2. Library Users Manual version 12.0 of Ie3d.3. 3G4G Cell Applications pdf document.

4. Polar Plot software documents.

Documents

Standardization and System Integration of Smart Antennas