UNIVERSITY
OF TRENTO
DIPARTIMENTO DI INGEGNERIA E SCIENZA DELL’INFORMAZIONE
38123 Povo – Trento (Italy), Via Sommarive 14
http://www.disi.unitn.it
A FULLY-ADAPTIVE SMART ANTENNA PROTOTYPE: IDEAL
MODEL AND EXPERIMENTAL VALIDATION IN COMPLEX
INTERFERENCE SCENARIOS.
M. Benedetti, G. Oliveri, P. Rocca, and A. Massa
January 2009
Model and Experimental Validation in Complex
Interferen e S enarios
ManuelBenedetti,OliveriGia omo,Ro aPaolo,and AndreaMassa, A ademy Member,
EMA
ELEDIA Resear hGroup
Department ofInformation Engineering andComputer S ien e
Universityof Trento, ViaSommarive 14, I-38050Trento - Italy
Tel. +39 0461 882057,Fax +390461 882093
E-mail: andrea.massaing.unitn.it,
{manuel.benedetti,gia omo.oliveri, paolo.ro a}disi.unitn.it
Model and Experimental Validation in Complex
Interferen e S enarios
M. Benedetti, G.Oliveri, P. Ro a, and A. Massa
Abstra t
In this paper, the ar hite ture of a smart antenna prototype is des ribed and its
fun tionality assessed. The system prototype is omposed byan 8-elements linear
array ofdipoles witha nite re e tingplaneand theadaptive behaviorisobtained
modifying a set of array weights with ele troni ally-driven ve tor modulators. In
order to real-time rea t to omplex interferen e s enarios, the system is ontrolled
by a software ontrol module based on a Parti le Swarm Optimizer. To
demon-strate the feasibilityand the ee tiveness of the proposed implementation, a set of
representativeresults on erned withdierentinterferen e s enariosisreportedand
dis ussed.
Index Terms:
SmartAntennas, Adaptive Array, Phased Arrays, Adaptive Control,
Thanks tothe development of wireless te hnologies, mobile ommuni ation devi es have
known a wide diusion in the last de ade. To properly deal with omplex s enarios
hara terizedbymultipleusersaswellasdierentstandards, ommuni ationsystemsthat
allowa suitablequality of servi e(QoS) and anenhan ed se urity are needed [1℄. Inthis
framework,smartantennas [2℄-[4℄have been re ognizedaspromisingtoolsforane ient
management of the physi al layer. Su h systems usually onsist of an antenna array
and a signal pro essing module toreal-time maximize the quality of the ommuni ation
evaluated(atthe physi allayer) intermsofsignal-to-interferen e-plus-noiseratio(SINR)
atthere eiver. Towardsthisend,smartantennassteerthemainlobeofthebeampattern
to tra k the desired signal and an el the interferen es determining suitableattenuation
values along the jammers dire tions. As a onsequen e, the hannel apa ity and the
servi e overage turn out to be in reased with respe t to standard solutions [5℄. On the
other hand, thanks totheir re on gurability, smartantennas havebeen alsoemployed in
multiple-inputmultiple-output(MIMO) wirelesssystems [6℄[7℄.
Althoughtheee tivenessofahardwareimplementationhasbeentheoreti allyproved[8℄,
the te hnologi aldi ulties and osts for the implementationof fully-adaptivesolutions
prevented a widespread appli ation of smart antennas in wireless ommuni ations. As
a matter of fa t, simpler ar hite tures have been usually onsidered (e.g., re on gurable
parasiti antennas or swit hed beam antennas [9℄). As far as parasiti ar hite tures are
on erned, several prototypes have been implemented by exploiting the theoreti al basis
of the fun tioning of the Uda-Yagi antennas [10℄[11℄ (i.e., a single a tive antenna whose
pattern is shaped by means of a set of real-time ontrolled parasiti elements). Despite
ompa t sizes and low- ost ar hite tures, su h devi es are not able to steer the position
of the patternnullsina ontinuous way,thus limitingtheadaptation tothe environment
of the wholetransmission system.
Consequently, some eorts have been devoted to realize a fully-adaptive behavior and
more omplex prototypes have been built. Somedevi es have been implemented making
use of omplex a quisition systems, where the signal is olle ted at the re eiver and at
algorithm by Widrow [14℄) for the minimization of a ost fun tion de ned as the error
betweenthere eivedsignalandareferen eone. Ontheotherhand,simplerfully-adaptive
systems based on the measurement of the re eived signal at the re eiver have been also
implemented [15℄. Su h devi es are usually ontrolled by meansof heuristi optimization
strategies aimed at maximizing a suitable tness fun tion proportional to the
signal-to-interferen e-plus-noise ratio. In both ases, the ee tiveness of the implementation has
beenassessedby omparingmeasuredandsimulatedradiationpatternsin orresponden e
with a single interferer in omingfroma xed dire tion.
This paper dis usses the implementation of a fully-adaptive smart antenna devoted to
the real-time suppression of multiple jammers and fo us on the experimental validation
by onsidering omplex interferen e s enarios. The antenna has already been presented
byAzaro et al. [16℄ and its fun tionalitieshave been preliminaryassessed by onsidering
a simple experiment [17℄. The prototype is hara terized by a simple fun tional s heme
where the signals olle ted by the array elements are suitably weighted by the hardware
ontrolunita ordingtoaniterativestrategybasedona ustomizedversionoftheParti le
Swarm Optimizer (PSO) [18℄ to maximize a suitable tness fun tion. As far as the
validation is on erned, a sele ted set of experiments hara terized by multiple slots of
time and dierent dire tions of arrival for the interferen es is onsidered. The e ien y
oftheproposedimplementationisanalyzedintermsofboththe behaviorofthe
signal-to-interferen e-plus-noise(SINR)ratioandthe apabilityofpla ingnullsinsuitablepositions
of the radiationpattern.
The paper is stru tured as follows. In Se t. 2, the behavior of the smart antenna is
brie y outlined. Se tion 3 is on erned with the des ription of the ar hite ture of the
smart antenna. The validation of the prototype is then dis ussed in Se t. 4, where a
sele ted set ofmeasurementsofthe signal-to-interferen e-plus-noiseratioaswellasofthe
radiation patterns is ompared with the results of the numeri al simulations. Finally,
Let us onsider a linear array of
M
equally-spa ed elements. At ea h time-step(1)
t
l
,l
= 1, ..., L
,the antennare eives asignal froma desiredsour e and a set ofQ
interfering sour es. The signal olle ted atthem
-thelement of the array is given bys
m
(t
l
) = d
m
(t
l
) +
Q
X
q=1
u
q,m
(t
l
) + n
m
(t
l
)
m
= 1, ..., M
(1)where
d
m
andu
q,m
denote the ontributionof the desired signal and of theq
-thinterfer-en e,
q
= 1, ..., Q
,respe tively. Moreover,n
m
models the un orrelated ba kground noise.Withreferen etoFig. 1,the signal
y
(t
l
)
availableatthe outputofthe re eiverturnsoutto be
y
(t
l
) =
M
X
m=1
w
m
(t
l
) s
m
(t
l
)
(2) wherew
m
(t
l
) = α
m
(t
l
) e
jβ
m
(t
l
)
isthe
m
-th omplex weight atthel
-thtime-step.As far as the smart behavior of the system is on erned, the weight ve tor
w
(t
l
) =
{w
m
(t
l
) ; m = 1, ..., M}
is updated at ea h time-step to rea t to the ontinuously (i.e., at ea h time-step) hanging interferen e/environment onditions, thus maximizing thesignal-to-interferen e-plus-noiseratio (SINR) atthe re eiver. Sin ethe SINR isnot
usu-ally/easilymeasurable at the re eiver, the total power
Ψ
tot
(t
l
)
of the output signaly
(t
l
)
is onsidered[19℄[20℄asanindexof thesystemperforman eandthe tnessfun tion
Θ (t
l
)
Θ (t
l
) =
P
M
m=1
α
m
(t
l
) e
j
[
2π
λ
x
m
osφ
d
+β
m
(t
l
)
]
2
Ψ
tot
(t
l
)
(3)ismaximizedtodeterminethe optimalweightve tor
w
opt
(t
l
) =
arg[
maxw
SIN R
{w (t
l
)}]
[20℄. In (3),
x
m
is the position of them
-th element,φ
d
and
φ
u
q
,q
= 1, ..., Q
, are thedire tions of arrival(DoAs) of the desired and
q
-thjammer, respe tively. Forthe sake oflarity,the measured value of
Ψ
tot
isobviously relatedto the array oe ients a ording
(1)
Atime-stepisaslotoftime,betweentwo onse utivesnapshots, hara terizedbythepresen eof adesiredsignalanda xednumberofinterferingsignalswithinvariantdire tions ofarrival(DoAs).
Ψ
tot
(t
l
) =
M
X
m=1
α
m
(t
l
) e
jβ
m
(t
l
)
M
X
i=1
α
i
(t
l
) e
−j β
i
(t
l
)
Ω
tot
i,m
(4) whereΩ
tot
i,m
is the(i, m)
-th entry of the o-varian e matrix of the re eived signaly
(t
l
)
[21℄.
At ea h time-step, the arising optimizationproblem
w
opt
(t
l
) =
arg[
maxw
Θ {w (t
l
)}]
(5)isthensolvedbymeansofaPSO-drivenapproa hfollowingtheimplementationguidelines
in [18℄. More spe i ally, a swarm of
P
parti les is used to model the trial solutions atea h time-step. The swarm samples the solution spa e in a set of su essive iterations,
k
l
= 1, ..., K
max
(k
l
being the iteration index of thel
-th time-step) by exploiting the history of the swarm as well as the knowledge of the optimal solutions at the previoussteps(i.e., theso- alledswarmmemory
Π (t
l
) =
w
opt
(t
i
) , i = l − 1, l − 2, ....
)untilthe
new time-step (
l
← l + 1
) or whenΘ {w (t
l
)} ≥ η
,η
being a user-de ned onvergen ethreshold [22℄.
3 Ar hite ture of the Smart Antenna
The smart antenna prototype operates as a re eiving system in the ISM band (
f
c
=
2.45 GHz
). It is omposed by the fun tional blo ks shown in Fig. 1: (a) a Radiating Module [Fig. 2(a)℄;(b)aHardware ControlModule [Fig. 2(b)℄;( )aRadio-frequen ySignalsCombiner;and(d)aSoftwareControlModule [16℄[17℄. Forthesakeof
omplete-ness, the ar hite ture of the fun tionalblo ks isdes ribed inthe following subse tions.
(a) Radiating Module
The array onsists of
M
= 8
(D
=
λ
2
being the inter-element distan e whereλ
is the free-spa e wavelength) printed dipoles built followingthe indi ations given in [23℄. Moreother side of the FR-4 substrate. Withreferen e to Fig. 2(a),a nite re e ting plane of
length
L
= 4.5 λ
and heightH
= λ
has been pla edbehindand parallel tothe dipoles ata distan e
W
= 0.25 λ
from their arms.(b) Hardware ControlModule
The HW Control Module is omposed by a set of
M
AD8341 Analog Devi e RF ve tormodulators [24℄ that weight the signals
s
m
(k
l
)
,m
= 1, ..., M
, from the array elementswith the oe ients
w
m
(k
l
)
,m
= 1, ..., M
, omputed by the PSO-driven ontrol. Su h amodule has been installedona FR-4printed ir uit board (PCB)and ea h modulatoris
drivenbytwolow-frequen y dierentialsignals
ν
m
(k
l
)
,ζ
m
(k
l
) ∈ [−0.5, 0.5 V ]
(Fig. 1)togenerate aphase shift value
β
m
equaltoβ
m
(k
l
) =
ar tanν
m
(k
l
)
ζ
m
(k
l
)
, β
m
∈ [0; 360] ,
(6)and an attenuation given by
α
m
(k
l
) =
q
ζ
m
(k
l
)
2
+ ν
m
(k
l
)
2
, α
m
≤ 0.5
(7)in the range
α
m
∈ [4.5 dB; 34.5 dB]
.( ) Radio-frequen y Signal Combiner
The
M
= 8
RF signals oming from the HW Control Module are added, by means ofthe RF power ombiner shown in [Fig. 2(b)℄,to determine the output signal
y
(k
l
)
. Theombineris omposedbyseven Wilkinsondevi es[25℄printedonanArlonN25substrate.
(d) PSO-driven Software Control Module
The outputsignal is then pro essed by a spe trum analyzer AgilentESA-E4404 to
mea-sure the total output power
Ψ
tot
(k
l
)
. Su h an information is then transferred to theni ation interfa es and a SW unit, whi h implements the PSO-driven ontrol logi [18℄,
havebeeninstalled. Atea htimestep
t
l
,theSWmodulemaximizesthe SINR bylookingfor the optimal set
{[ζ
opt
m
(t
l
) , ν
m
opt
(t
l
)] ; m = 1, ..., M}
of the dierential signals (i.e., theorresponding optimal weight on guration,
w
opt
(t
l
) = {w
opt
m
(t
l
) ; m = 1, ..., M}
, beingw
opt
(t
l
) =
arg maxp=1,...,P
Θ
w
p
(k
l
)
) that optimizes(3). 4 Experimental Validation
This se tion is on erned with the experimental validation of the smart antenna
proto-type. Firstly, the results of a preliminary testing and alibration of the prototype will
be dis ussed. Then, the behavior of the prototype in dealing with omplex interferen e
s enarios willbe des ribed and analyzed inorder to assess the feasibility of the adaptive
ontrolad the reliability/ee tivenessof the proposed implementation.
4.1 Preliminary Calibration and Testing
Apreliminary alibrationandtestingoftheprototypehasbeen arriedout by omparing,
for ea h fun tional blo k, the numeri al simulations of the design phase with a set of
experimental measurements. For the sake of brevity, the behavior of the HW Control
Module and of the whole system in astati on gurationis dis ussed here.
The rst validationis on erned with the a ura yof the RF ve tor modulatorin
gener-ating therequiredattenuationandphase shiftvalues. Towards this purpose, letusde ne
the errorindexes
∆ =
r
α
2
+ ˜
α
2
− 2α˜
α
osβ
− ˜
β
(8) andδ
= |α − ˜
α|
(9)where the supers ript
˜
indi ates an experimentally measured quantity. Table 1sum-marizes the obtained results in orresponden e with a set of representative values of the
mismat h-ing between simulated and measured valuesis mainlydue to the approximation of the
α
oe ient. As a onsequen e, the weight amplitudes have been xed to
α
m
(k
l
) = 0.5
;m
= 1, ..., M
;l
= 1, ..., L
and the adaptive ontrol has been obtained by adjusting the phase oe ientsβ
m
(k
l
)
.These ondexperimentdealswithastati s enarioanditisaimedatassessingtheability
of the whole system to reprodu e the simulated quies ent pattern. Towards this end,
the radiationpattern
G(φ)
has been measured ina ontrolled environment(i.e., ane hoihamber) and it has been ompared with the orresponding data from the numeri al
simulation[22℄. Figure3 shows the patterngeneratedbysetting
w
m
= 0.5
,m
= 1, ..., M
.As it an be observed, the simulated pattern is arefully mat hed despite a shift of the
angular positionsof the nulls. Su h adieren eis mainly ausedby the mutual oupling
ee ts and the presen e of the nite re e ting plane. As a matter of fa t,the numeri al
simulator did not modelthe a tual stru ture of the smart antenna, but an ideal array of
point-likeideal radiators.
4.2 Validation in Complex Time-Varying S enarios
This subse tionis aimedatanalyzingthe performan e ofthe prototypeinatime-varying
situation. The results of three experiments on erned with variations on DoAs,
inter-feren e power, and number of jammers will be presented and dis ussed. As far as the
measurementsetupis on erned,the interferen e sour eshavebeen pla edinfrontofthe
smart antenna along a ir ular perimeter (
ρ
= 28 λ
) and at dierent angular positions(Fig. 4).
Experiment A
Thes enarioofthe rst experimentis on ernedwithasingleinterferen e sour e(
Q
= 1
)whose DoA and power have been varied a ording to the evolution detailed in Tab. II.
Moreover,
φ
d
has been set to
0
andΨ
d
= 100 mW
.
The obtained results are summarized in Fig. 5 where the behavior of the SINR at the
re eiver versus the iteration index
χ
(χ
, P
L
l=1
k
l
) [Fig. 5(a)℄ and two representative samples of the radiationpattern [Figs. 5(b)-5( )℄ are reported. As for the SINR at ea hiteration
χ
, it has been omputedas followsSIN R
(χ) =
Ψ
˜
d
(χ) − ˆ
n
(χ)
˜
Ψ
u
(χ)
(10) whereΨ
˜
d
(χ)
andΨ
˜
u
(χ)
are the power of the desired signal and of the undesired one
measured when turningo the interferen e sour es[i.e.,
u
q
(χ) = 0
,q
= 1, ..., Q
℄ and thedesiredsignal [i.e.,
d
(χ) = 0
℄,respe tively. Moreover,ˆ
n
(χ)
isthe noiselevelestimatedatthe
χ
-th iteration turning o bothd
(χ)
andu
q
(χ)
,q
= 1, ..., Q
. Withreferen e to Fig.5(a), the SINR values measured at ea h time-step
t
l
,l
= 1, ..., L
turn out to be alwaysgreaterthan
32 dB
withadynami sofabout10 dB
[SIN R
(t
1
) = 33.86 dB
,SIN R
(t
2
) =
38.48 dB
,SIN R
(t
3
) = 42.36 dB
,SIN R
(t
4
) = 32.96 dB
, andSIN R
(t
5
) = 34.86 dB
℄. As for the response of the PSO-driven software ontrol module, the omputation timerequired by ea h iteration is in the order of
1.6 ms
on a CPU IntelP4, as also reportedin [18℄. For omparison purposes, the plot of the SINR simulated with the model in[18℄
is reported,as well. As it an be observed, thereis ana eptable agreement between the
twoplotsalthough somedieren es o urs sin ethe modeldoesnotreprodu ethe a tual
stru ture of the antenna
(2)
.
In ordertogivesome indi ationsof theee ts of the SINR maximizationonthe antenna
beamforming, some samples of the radiation pattern in orresponden e with dierent
time-steps are shown. More spe i ally, Figure5(b) shows the pattern shapeat the step
l
= 1
[SIN R
(t
1
) = 33.86 dB
℄when the interferen e impingeson the array fromφ
u
= 69
with apowerequal to
150 mW
. The measuredbeampatternis hara terizedby anull ofabout
−22 dB
along the jammer dire tionφ
= φ
u
. A similar result is obtained at
l
= 3
forajammer omingfromthedire tion
φ
u
= 132
withalowerpowerlevel(
Ψ
u
= 80 mW
)
[Fig. 5( )℄.
Experiment B
In the se ond experiment, the interferen e s enario is hara terized by
Q
= 2
undesiredsour es with the desired signal that impinges on the antenna from
φ
d
= 0
. Unlike the
(2)
Thenumeri alsimulationsareperformedby onsideringanidealarrayofpoint-likesour espla ed in frontofanin nitere e tingplaneat adistan eof
0.25
wavelengths.previousexperiment,thepowerofjammershas beenset tothe samevalueof thatof
d
(t
l
)
(i.e.,
Ψ
d
= Ψ
u
= 100 mW
).
Although the DoAs of the interferen es have been varied as in Tab. III, the smart
an-tenna isable to obtainSINR values atthe re eiverin the range
20.23 dB ≤ SINR (t
l
) ≤
23.45 dB
[Fig. 6(a)℄. Asexpe ted,theaverageSINRvalueredu es omparedtothatofthe previousexperimentbe auseofthepresen eoftwoundesiredsignals(av
l=1,...,L
{SINR (t
l
)}⌋
Exp B
=
21.81 dB
vs.av
l=1,...,L
{SINR (t
l
)}⌋
Exp A
= 36.46 dB
). However, the radiation patternsare still hara terized by nulls ee tively lo ated at
φ
u
q
(t
2
) = {111, 78}
[Fig. 6(b)℄ andφ
u
q
(t
3
) = {114, 132}
[Fig. 6( )℄further on rming the on lusionson the interferen e re-je tion of the smart antenna prototype. More spe i ally, the nulls obtained with thePSO-driven ontrol have the following depths:
G
{φ
u
1
(t
2
)} = −27 dB
,G
{φ
u
2
(t
2
)} =
−17 dB
in the se ond time-step (l
= 2
) [Fig. 6(b)℄ andG
{φ
u
1
(t
3
)} = −23 dB
andG
{φ
u
2
(t
3
)} = −27 dB
atl
= 3
[Fig. 6( )℄.Con erning the omparison between simulated and measured patterns, there is a good
agreement about the positions of the nulls as well as on the pattern shape in the range
30 < φ < 150
, while non-negligible dieren es o ur in the other angular regions due to the limiteddimensions of the measurement environment.Experiment C
The lastexperimentis on ernedwiththe samesetupofthe ExperimentB ex eptforthe
number of interferers (
Q
= 3
) and their DoAs as des ribed in Tab. IV. The plot of theSINR versus theiterationindex
χ
isshown inFig. 7(a). Asit anbeobserved, the rangeof the SINR is
12.17 dB ≤ SINR (t
l
) ≤ 21.08 dB
and the ee tivenessof the interferen esuppression pro edure isstillassessed as on rmed by the patternsamplesshown in Fig.
7(b) (
l
= 3
) and Fig. 7( ) (l
= 4
). As regardsl
= 3
, the measured lo ationsof the nullsturn outtobe losetothesimulatedoneseven thoughthey arenot ompletelyalignedto
the DoAs of the undesired signals. However, the attenuation in orresponden e with the
jammers is always lowerthan
10 dB
, sin eG
{φ
u
1
(t
3
)} = −11 dB
,G
{φ
u
2
(t
3
)} = −12 dB
, andG
{φ
u
3
(t
3
)} = −12 dB
. On the ontrary, Figure 7( ) shows that, at the time-stepl
= 4
, the nulls are ee tively lo ated along the dire tionsφ
u
1
(t
4
) = 120
,φ
u
and
φ
u
3
(t
4
) = 102
, with a depth ofG
{φ
u
1
(t
4
)} = −24 dB
,G
{φ
u
2
(t
4
)} = −20 dB
, andG
{φ
u
3
(t
4
)} = −27 dB
, respe tively. 5 Con lusionsIn this paper, an experimental realization of a fully adaptive array has been dis ussed.
The system is hara terized by a simple ar hite ture onsisting of a radiating module, a
HW ontrol module, a power ombiner, and a SW ontrol module. The signal re eived
at the array elements is suitably pro essed a ording to a PSO-based ontrol strategy
aimed at maximizing the SINR at the output the system. The experimental validation
has demonstrated the feasibility of the adaptive ontrol as well as the reliability of the
antenna prototype.
More spe i ally, the proposed implementationis hara terized by
•
alow- omplexity andlow- ostar hite ture abletoshiftthe lo ationsofthe pattern nulls ina ontinuous way;•
apability of adaptively rea t to hanging interferen e s enarios hara terized by single and multiplejammers;•
apability of optimizingthe system performan e in terms of SINR values and sup-pression of the interferen es.As for future developments, they willbe on ernedwith
•
the analysis and a su essive implementation of more realisti numeri al models aimed at arefully modeling the non-ideal behavior of the array (e.g., non-idealarray elements, the presen e of the re e tingplane, mutual ouplingee ts, et ..);
•
thedesignand testingofmore ompa t geometriesusefulforanintegrationinsmall size wireless devi es (e.g., mobile stationsand wireless sensor nodes).[1℄ A. Alexiouand M. Haardt, Smartantenna te hnologiesfor future wireless systems:
trends and hallenges, IEEE Comm. Mag.,vol. 42,pp. 90-97, Sep. 2004.
[2℄ S. Bello ore, C. A. Balanis, J. Foutz, and A. S. Spanias, Smart antennas systems
for mobile ommuni ation networks. Part. 1: overview and antenna design, IEEE
Trans. Antennas Propagat., vol. 44,pp. 145-154, Mag.2002.
[3℄ S. Bello ore, J. Foutz, C. A. Balanis, and A. S. Spanias, Smart antennas systems
formobile ommuni ationnetworks.Part.2: beamformingandnetworkthroughput,
IEEE Trans.Antennas Propagat., vol.44, pp. 106-114,Mag. 2002.
[4℄ M. Chryssomallis, "Smart antennas," IEEE Antennas Propagat. Mag., vol. 42, pp.
129-136, Jun. 2000.
[5℄ L. C. Godara, Appli ations of antenna arrays to mobile ommuni ations, Part I:
performan e improvement, feasibility,and system onsiderations, Pro . IEEE, vol.
85, pp. 1031-1060, Jul.1997.
[6℄ R. D. Mur h and K. B. Letaief, Antenna systems for broandband wireless a ess,
IEEE Commun. Mag., vol.40,pp. 76-83,Apr. 2002.
[7℄ D. Piazza, N. J. Kirsh, A. Forenza, R. W. Heath, K. R. Dandekar, "Design and
Evaluation of a Re on gurable Antenna Array for MIMO Systems," IEEE Trans.
Antennas Propagat., vol.56, pp. 869-881,Mar. 2008.
[8℄ S. P.Applebaum, "Adaptive arrays," IEEE Trans.Antennas Propagat., vol. 24,pp.
585-598, Sep. 1976.
[9℄ C. T. P. Song, A. Mak, B. Wong, D. George, and R. D. Mur h, Compa t low ost
dual polarized adaptive planar phased array for WLAN, IEEE Trans. Antennas
Propagat., vol. 53,pp. 2406-2416, Aug. 2005.
[10℄ M. D. Migliore, D. Pin hera, and F. S hettino,A simple and robust adaptive
on gurableWi-FiBandAntenna BasedonaParasiti Mi rostripStru ture, IEEE
Antenna Wireless Propagat. Lett.,vol. 6,pp. 623-626, 2007.
[12℄ N. Celik, W. Kim, M. F. Demirkol, M. F. Iskander, and R. Emri k,
Implementa-tion andexperimentalveri ationofhybridsmart-antennabeamformingalgorithm,
IEEE AntennaWireless Propagat. Lett., vol.5, pp. 280-283,2006.
[13℄ M. Diop,J.F.Diouris, and J.Saillard,Alow- ostexperimentaladaptivearray built
in the UHF band (900 MHz) for a minimum response time in interferen e
an ella-tion, in Pro . IEEE Vehi ular Te hnology Conferen e, 1992,pp. 25-28.
[14℄ B. Widrow, P. E. Mantey, L.J. Gri ths, and B. B. Goode, "Adaptive antenna
sys-tems," Pro . IEEE, vol. 55,pp. 2143-2159, De . 1967.
[15℄ R. L. Haupt and H. Southall, Experimental adaptive ylindri al array, in Pro .
IEEE Aerospa e Conferen e,1999, pp. 291-296.
[16℄ R. Azaro, L. Ioriatti, M. Martinelli, M. Benedetti, and A. Massa, An experimental
realization of afully-adaptivesmart antenna, Mi row. Opt.Te h.Lett., vol.50,no.
6, pp. 1715-1716, Jun. 2008.
[17℄ M. Benedetti, R. Azaro, and A. Massa, Experimentalvalidation of afully-adaptive
smart antenna prototype, Ele t. Lett., vol. 44,no. 11, pp. 661-662,May 2008.
[18℄ M. Benedetti,R. Azaro, and A.Massa, Memory enhan ed PSO-based optimization
approa hforsmartantennas ontrolin omplexinterferen e s enarios, IEEETrans.
Antennas Propag., vol. 56,pp. 1939-1947, Jul.2008.
[19℄ D. S.Weileand E.Mi hielssen,"The ontrolofadaptiveantennaarrayswithgeneti
algorithms using dominan e and diploidy," IEEE Trans. Antennas Propagat., vol.
49, pp. 1424-1433, O t. 2001.
[20℄ R. T. Compton Jr., Adaptive Antennas. EnglewoodClis, NJ: Prenti e Hall,1988.
approa h based on a parti le swarm strategy for adaptive phased-arrays ontrol,
IEEE Trans.Antennas Propagat., vol.54, pp. 888-898,Mar. 2006.
[23℄ H.-R.Chuang andL.-C. Kuo,3-DFDTDdesign analysisofa2.4-GHz
polarization-diversity printed dipole antenna with integrated balun and polarization swit hing
ir uit for WLAN and wireless ommuni ation appli ations, IEEE Trans. Mi row.
Theory Te h., vol. 51,pp. 374-381, Feb. 2003.
[24℄ Analog Devi es, 2004. 1.5 GHz to 2.4 GHz RF ve tor
modulator. One Te hnology Way, MA. [Online℄. Available:
www.analog. om/UploadedFiles/Data_Sheets/AD8341.pdf.
•
Figure1. Ar hite tureoftheFully-AdaptiveSmartAntennaprototype. Fun tional blo k diagramof the system.•
Figure 2. Photographsof theFully-AdaptiveSmartAntennaprototype: (a)Front view(Radiating Module)and (b)ba k view(HW Control Module andPowerCom-biner).
•
Figure 3. Testing Phase - Quies ent radiationpattern.•
Figure 4. Testing Phase - Sket h of the geometry of the testingsetup.•
Figure 5. Experiment A - (a) Behavior of the SINR versus the iterationindexχ
. Beam patternsynthesized at(b)l
= 1
and ( )l
= 3
.•
Figure 6. Experiment B - (a) Behavior of the SINR versus the iterationindexχ
. Beam patternsynthesized atthe time-steps (b)l
= 2
and ( )l
= 3
.•
Figure 7. Experiment C - (a) Behavior of the SINR versus the iterationindexχ
. Beam patternsynthesized at(b)l
= 3
and ( )l
= 4
.Table Captions
