introduction to smart antennas overview.pdf

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INTRODUCTION
Research and development on the PHS-WLL systems have been already developed, and
now cell station are going to be developed. In order to extend the performance of WLL
systems, Smart Antenna, or adaptive array antenna for the systems is in the plan to be
employed. Smart Antenna is one of the key technologies expected to dramatically improve
the performance of future wireless communications systems because it has the potential to
expand coverage, increase capacity, and improve signal quality[1][2]. Hence, research on
Smart Antenna becomes a very active field in recent years.
I ADPATIVE / SMART ANTENNA BASICS
1 Definition
A smart antenna combines antenna arrays with digital signal processing units in order to
improve reception and emission radiation patterns dynamically in response to the signal
environment.
2 Two major categories of Smart Antenna
A. Switched beam antenna
Switched beam antenna systems form multiple fixed beams with heightened sensitivity in
the particular directions.
B. Adaptive antenna
Using a variety of new signal processing algorithms the adaptive system take advantage of
its ability to effectively locate and track various types of signals to dynamically minimize
interference and to maximize intended signal reception.
II BASIC SCHEME FOR TRANSMISSION OF SIGNALS
For the smart antenna systems, there is a scheme, the SDMA (space division multiple
access), which uses an array of antennas to provide control of space by providing virtual
channels in an angle domain.
The scheme allows a transmission to take place in one cell, without disturbing the
transmission in another cell. Using space diversity, the shape of a cell may be changed
dynamically to reflect the user movement.

2
The term adaptive antenna (or intelligent antenna or smart antenna) is used for the phased
array when the gain and the phase of the signals induced on various elements are changed
before combining to adjust the gain in a dynamic fashion, as required by the system. A plot
of the array response as a function of angle is normally referred to as the array pattern,
beam pattern or radiation diagram. The process of combining the signals from different
elements is known as beam forming.
Wireless Communication technologies have a great progress in recent years and the
markets, especially the cellular telephone, have been growing enormously. Moreover the
next generation communication services will use higher frequency band area and require
more channel capacity and wider bandwidth for a high-speed data communication. As a
large increase in channel capacity and high transmission rates for wireless communications,
the technologies for the power saving and efficient frequency usability are required.
IV FUNDAMENTALS OF ANTENNA ARRAYS
A. Intelligent Antenna Definition
An”intelligent” antenna is an array of spatially separated antennas whose outputs are fed in
to a weighting network. The part that makes the antenna array ”intelligent” is the signal
processing unit which calculates the weights that produce the desired radiation pattern of
the array[3].
B. Intelligent Antenna Working
An adaptive antenna array system is composed of N number of array element as shown in
fig. no. 1. The numbers of users are M and interfering signals are I. For each element
weight is assigned by system which is a complex function. Real part of which gives
undesired (or interference) users arriving at directions θ1, θ2, ………, θI, . At a particular
instant of time t = 1, 2,……….,K, where K is the total number of snapshots taken. The
desired users signal vector xs(t) can be defined as
M
xM (t) = ∑ a(θM)sm(t)……………………………………….………….(1)
m=1

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Where a(θM) is the N × 1 array steering vector which represents the array response at
direction θM and is given by
a(θM)=[exp[j(n1)ψm]T
;1<n<N……………………………..(2)
Fig.1:- Functional block diagram smart antenna systems
Where [(.)]T
is the transposition operator, and ψm represents the electrical phase shift from
element to element along the array. This can be defined by
ψm=2π(d/λ)sin(θM)…………………………………………..(3)
where d is the inter-element spacing and λ is the wavelength of the received signal.
The desired users signal vector xM (t) of (1) can be written as
xM(t)=AM(t)………………………………………………….(4)
where A M is the N × M matrix of the desired users signal direction vectors and is given by
DOWN CONVERTER
DOWN CONVERTER
DOWN CONVERTER
ADC
ADC
ADC
W1
W2
WN
∑
ADAPTIVE
PROCESSOR
TO
THE
DEMODULATOR

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AM = [a(θ1), a(θ2)……. a(θM )]…………………………..… (5)
and s(t) is the M × 1 desired users source waveform vector defined as
s(t) = [ s1(t), s2(t) ……sM(t)]T
………………………………. (6)
We also define the undesired (or interference) users signal vector xI(t) as
x I(t) = A I i(t) ………………………………………..…….(7)
where A I is the N × I matrix of the undesired users signal direction vectors and is given by
A I = [a(θ1), a(θ2)……. a(θ I )]…… ………………………. (8)
and i(t) is the I × 1 undesired (or interference) users source waveform vector defined as
i(t) = [ i1(t), i2(t) ……iI (t)]T
…………………………………(9)
The overall received signal vector xM (t) is given by the superposition of the desired users
signal vector xM (t), undesired (or interference) users signal vector xI (t), and an N×1 vector
n(t)
which represents white sensor noise. Hence, x(t) can be written as
x (t) = xM (t) + n(t) + xI (t) ……………………….……….(10)
where n(t) represents white Gaussian noise. The conventional (forward-only) estimate of
the covariance matrix defined as
R = E{x(t) xH
(t)}……………………………………….. (11)
where E{.} represents the ensemble average; and (.)H
is the Hermitian transposition
operator. Equation (11) can be approximated by applying temporal averaging over K
snapshots (or samples) taken from the signals incident on the sensor array. This averaging
process leads to forming a spatial correlation (or covariance) matrix R given by :
1 / K
KR = ∑ x (k) xH
(k)……………………………………. (12)
k=1
Substituting for x (t) from (10) in (12) yields

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R = AMRssAH
M + n(k) n(k) H
+AIRiiAI
H
…………….(13)
where Rss = E{s(t)sH
(t)} is an M×M desired users source waveform covariance matrix;
Rii = E{i(t)iH
(t)}is an I ×I undesired users source waveform covariance matrix.
Finally,
equation (13) can be rewritten as
1 / K
KKR = ∑ AM [s(k) s(k) H
] AH
M + σ2
n I + 1
t=1
-------------------------------------------------
K
∑ AI [i(k) i(k) H
] AH
I
t=1
…………………..…………(14)
where σ2
n is the noise variance, and I is an identity matrix of size N × N .
C. DoA Estimation Using Music Algorithm
A common subspace-based DOA [4] estimation algorithm is MUSIC (MUltiple SIgnal
Classification). It starts by expressing the covariance matrix R obtained in (14) as
R = JR*J ………………………………………..………..(15)
Where J is the exchange matrix with ones on its anti-diagonal and zeros elsewhere; and (.)*
stands for complex conjugate.
The covariance matrix R in (15) is known to be centro-Hermitian if and only if S is a
diagonal matrix, i.e., when the signal sources are uncorrected.

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It can be shown that the covariance matrix R obtained in (14) has M signal eigenvalues
with corresponding eigenvectors v1, v2 ,………………..vM, i.e.,
VS=[v1,v2…………..vM]………………………………….. (16)
The remaining N - M eigenvalues of the covariance matrix R represent noise eigenvalues
with corresponding eigenvectors
vM+1 , vM+2 ,………………………vN , i.e.,
Vn=[vM+1, vM+2 ,……vN]………………………………… .(17)
Hence, the eigen-decomposition of the covariance matrix in (14) is defined in a standard
way as:
R = VIIV = VSIISVS + σ2
n VnVn
H
………………………….(18)
Where the subscripts s and n stand for signal and noise sub-space, respectively. In (18) IIS
is defined as
IIS = diag{ π 1,π 2,………π M}……………………………. (19)
The normalized MUSIC angular spectrum is defined as,
P(θ) = AH
A / AH
VnVn
H
A………………………………..……..(20)
By examining the denominator in (20) it is evident that peaks in the MUSIC angular
spectrum occur at angles θ for which the array manifold matrix A is orthogonal to the noise
subspace matrix En. Those angles θ define the desired directions-of-arrival of the signals
impinging on the sensor array.
The number of signals that can be detected is restricted by the number of elements in the
sensor array. It was verified that an N element sensor array can detect up to N -- 1
uncorrelated signal. This number reduces to N / 2signals if they are correlated.

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