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MEMORY ENHANCED SMART ANTENNAS FOR Wireless COMMUNICATION
SANTOSH KuMAR Jha
Assistant Professor, ece dept
Fet, Mody university,lakshmangarh
Rajasthan – 332311
ersantoshjha@yahoo.co.in
ABSTRACT
The paper focuses on the interaction and integration of several critical
components of a mobile communication network using smart-antenna
systems. This wireless network is composed of communicating nodes that
are mobile, and its topology is continuously changing. One of the central
motivations for this work comes from the observed dependence of the
overall network throughput on the design of the adaptive antenna system
and its underlying signal processing algorithms. In the framework of control
methods for adaptive phased-arrays, this paper deals with complex
communication scenarios by considering a memory-enhanced cooperative
algorithm. Compared to existing approaches where far-field interferences
are taken into account, the proposed analysis considers a more realistic
situation where the jamming sources are located either in the near-field or
in the far-field of the receiving antenna.
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Key Words: Smart antenna, memory-enhanced cooperative algorithm,
MIMO, PSO
INTRODUCTION
The demand for increased capacity in wireless network has
motivated recent research towards the development of algorithms
and standards that exploit space selectivity [1]. As a result, there
are many efforts on the design of “Smart” antenna arrays and the
associated beam forming algorithms[2][3]. Smart-antenna systems
provide opportunities for higher system.
The continuous evolution of communication systems requires
the development and customization of techniques based on the
idea of diversity [5]. In the frame work of antennas design, such a
theory has been applied for developing smart systems able to
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improve the quality of the received signal and to suppress the
effects of interfering sources. The concept of spatial diversity [6]
has led to the coupling of array theory with adaptive control and
therefore to the design of antenna architectures able to maximize
the system performances [i.e., the signal-to-interference-plusnoise ratio (SINR) by tuning dynamically the weights of the array
elements.
The mathematical theory of adaptive systems has been
originally proposed by Applebaum in [7] dealing with linear arrays
of isotropic sources in the presence of far-field (FF) narrow-band
signals (i.e. a desired signal and a set of jammers). The array
weights, adapted for placing nulls in the far-field pattern in the
directions of interference, are obtained by multiplying the
quiescent weights by the inverse of the sampled covariance matrix
formed from the complex signals received at each element in the
array.
Alternatively, the adaptive control has been also recast as an
optimization problem by defining a suitable cost functional to be
maximized. Originally, deterministic techniques based on gradient
methods (e.g., the least mean square (LMS) algorithm by widrow et
al. [8][9] have been proposed, but the resulting approaches were
still characterized by several non-negligible drawbacks. Because of
the need of estimating the covariance matrix of the desired signal
from the measurements of the received signals at each element of
the array, the array must have an expensive receiver or a
correlator at each element.
Unfortunately, most arrays (or the simplest/cheapest) have a
single receiver at the output of the summer and the receivers
(when available) would require sophisticated calibrations. On the
other side, these methods consider variable analog amplitude and
phase weights, but phased arrays usually have only digital beam
steering phase shifters at the elements and the feed network
(fixed) determines the amplitude values. Therefore, the continuous
phase values calculated by the adaptive algorithms are only
approximated and the quantization error limits the null
placement.
In order to reduce the complexity and the costs of adaptive
systems, the possibility of implementing a phase-only control (i.e.,
adjusting the phase shift resetting) for reducing the total output
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power measured by the receiver at the output of the summer has
been investigated. A significant improvement on this technique
has been proposed by Haupt [10] who used a genetic algorithm
(GA) to adjust some of the least significant bits of the beam
steering phase shifters for minimizing the total output power thus
removing the interfering signals from the output of the array.
Notwithstanding the success and successive experimental
implementation, such a (GA)-based approach did not take into
account constantly changing conditions and the need of a
readaptation to new environments once the population converged.
Such a problem has been overcome in successive works by weile
and Michielssen [11] or Donelli et al [12] by using dipoloidy and
dominance or cooperative algorithms (i.e., the particle swarm
optimizer (PSO).
In this paper in the initial state the paper presents an insight
into smart antenna system. Using the human auditory system as
an analogy, It introduces the different types of smart antenna
systems. The paper is aimed at assessing the effectiveness and
reliability of an enhanced PSO-BASED technique in the presence
of more complex working conditions. In particular, the signals
impinging on the array are characterized by randomly variable
directions and generated by electromagnetic sources located at
differences distances from the antenna system. More in detail, the
source of the desired signal is assumed to be very far from the
system, whereas the distance of the interfering sources from the
array varies from the near to the far zone. Such a situation turns
out to be quite realistic since it could model/describe an “infomobility” scenario where a moving network node (e.g., a car or a
pedestrian) communicates with a base station, a neighboring node
(i.e., close to the receiving system) would be considered as a nearfield jamming source.
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SMART ANTENNA ANALOGY
The functionally of many engineering system is readily understood
when it is related to our human-body system. Therefore, to give an
insight of how a smart-antenna system works. Let’s imagine two
persons carrying on a conversation inside a pitch-dark room
(Figure1). The listener among the two persons is capable of
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determining the location of the speaker as he or she moves about
the room because the voice of the speaker arrives at each acoustic
sensor – the – ear – at a different time. The human signal
processor – the brain – computes the direction of the speaker from
the time differences or delays received by the two ears. Afterwards,
the brain adds the strength of the signals from each ear, so as to
focus on the sound of the computed direction. Furthermore, if
additional speakers join in the conversation, the brain can tune
out unwanted interferers and concentrate on one conversation at
a time.
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Fig 1: Respond of the listener (human being) and Respond of the smart
antenna
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Conversely, the listener can respond back to the same direction as the
desired speaker by orienting his or her transmitter – his or her mouth –
towards the speaker.
Electrical smart-antenna systems work the same way, using many
antennas instead of the ears, and a digital signal processor instead of the
brain (Figure 1). Therefore after the digital signal processor receives the time
delays from each antenna element, it computes the direction-of-arrival
(DOA) of the signal of interest (SOI). It then adjust the excitations (the
amplitudes and phase of the signals) to produce a radiation pattern that
focuses on the signal of interest SOI, while tuning out any signal not of
interest (SNOI).
THE QUEST FOR MORE CAPACITY
Maintaining capacity has always been a challenge, as the
number of services and subscribers increases. To achieve the capacity
demand required by a growing number of subscribers, cellular radio
systems had to evolve throughout the years. To justify the need for smartantenna system in the current cellular system structure, a brief history on
the evolution of cellular radio systems is present.
Since the early days, system designers knew that capacity would be a
problem, especially because the number of channels or frequencies allotted
by the Federal Communication Commission (FCC) was limited. Therefore, to
achieve the capacity required for thousands of subscribers, a suitable
cellular structure had to be designed; the resulting structure is depicted in
Figure – 2.
Each shaded hexagonal area in Figure-2 represents a small
geographical area, called a cell with a maximum range (radius) R. At the
center of each cell resides a base station equipped with an omni directional
antenna. With a given band of frequencies, Base stations in adjacent cells
are assigned frequency bands that contain frequencies completely different
from neighboring cells. By limiting the coverage area to within the
boundaries of a cell, which are separated from one another by distances
large enough to keep interference levels within a system is called frequency
reuse cells having the same side use the same frequency spectrum. Cell
splitting, as shown in Figure – 3, subdivides a congested cell into smaller
cells, called microcells, each with its own base station and a corresponding
reduction in antenna height and transmitter power. Cell splitting improves
capacity by decreasing the cell radius R, and keeping the D/R ratio
unchanged; D is the distance between the centers of the clusters. The
disadvantages of cell splitting are the costs incurred from the installation of
new base stations, the increase in the number of handoffs (the process of
transferring communication to a new base station when the mobile unit
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travels from one cell to another), and a higher processing load per
subscriber.
SMART ANTENNA SYSTEMS
Despite it benefits, cell-sectoring did not provide the solution needed for the
capacity problem, therefore the system designers began to look into a
system that could dynamically satirize a cell. Hence, they began to examine
smart antennas.
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Fig 2: A typical cellular structure with a seven cells reuse pattern
Fig 3: A schematic diagram of cell splitting
Many refer to smart-antenna systems as smart antennas, but in reality,
antennas are not smart; it is the digital signal processing, along with the
antennas which make the system smart. Although it might seem that
smart-antenna systems are a new technology, the fundamental theory of
smart antennas is not new. In fact, they have been applied in defenserelated systems since world war – II. In recent years, with the emergence of
powerful, low-cost digital signals processors (DSPs). Geneal-purpose
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processors (and application-specific integrated circuits, or ASICs) as well as
innovative signal-processing algorithms, smart-antenna systems have
become practical for commercial us.
Smart-antenna systems are basically an extension of cell sectoring in
which the sector coverage is composed of multiple beams. This is achieved
by the use of antenna arrays, and the number of beams in the sector (e.g., a
sector of 120°) is a function of the array geometry. Because smart antennas
can focus their radiation pattern toward the desired users while rejecting all
unwanted interference and therefore, a lower bit error rate (BER) they can
provide a substantial capacity improvement. These systems can generally
be classified as either swathed-beam or adaptive-array systems.
SWITCHED-BEAM SYSTEMS
A switched-beam system is a system that can choose from one of many
predefined patterns, in order to enhance the received signal (Figure-4). It is
obviously an extension of cell-sectoring, as each sector is subdivided into
smaller sectors. As the mobile unit movies throughout the cell, the
switched-beam systems detects the signal strength, choose the appropriate
predefined beam pattern, and continually switches the beams as necessary.
The overall goal of the switched-beams system is to increase gain, according
to the location of the user. However, since the beams are fixed, the intended
user may not be in the center of the main beam. If there is an interfere near
the center of the active beam, it may by enhance more than the desired
user.
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ADAPTIVE-ARRAY SYSTEM
Adaptive-array systems can locate and track signals and can dynamically
adjust the antenna pattern to enhance reception while minimizing
interference, using signal processing algorithms. A functional block diagram
of such a system is shown in figure – 5. This figure shows that after the
system down-converts the received signals to base band and digitizes them,
it locates the signal of interest using the direction of arrival (DOA) algorithm
computes the direction of arrival of all signals by computing the time delays
among the antenna elements. Afterwards, the adaptive algorithm, physical
communications channel in the same cell, with only an angular separation.
This technology dramatically improves the interference-suppression
capability while it greatly increase frequency cost. Basically, capacity is
increased not only through inter-cell frequency reuse, but also through
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intra-cell frequency reuse.
Fig 4: A switched beam system
MATHEMATICAL FORMULATION
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Let us consider an array of N elements. The narrowband signal received by
the nth element of the array at the time-step² t e , e =1,…, L, can be expressed
as follows:
(r)
s (r)
(t e ) e jα n
n (t e ) = a
(r)
j2πft e
n = 1,.....N; l = 1,...., L
------------------- (1)
(t e ) = h (t e ) e
, h (t e ) and f being the slowly-varying
where a
envelope of the received signal and the carrier frequency, respectively.
(r )
Moreover, ℘n is the phase term of the received signal coming from the
angular coordinates (θ r , Φ r ) that identify the direction-of-arrival ( DΩA) of the
received signal.Under far-field conditions [13], the phase term of (1) turns
out to be
(r )
χ nr =
(r)
2π
λ
r
(u r xn + vr y n + qr z n )
------------- (2)
Where
r = sin θ r , cos ø r , v r = sin θ r , sin ø r , and q r = cos θ r
and (xn, yn, zn) are the Cartesian coordinates of the nth element of the array.
u
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By considering Co-channel interferences sn( r ) is the result of the
summation of the desired signal sn( d ) , a set of I jammers
Fig 5: A typical architecture of Adaptive array
{si(,gn) ; i = 1, ..... } ,
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and an uncorrelated background noise for noise signal
σ2
(o)
sn
] characterized by an average power to
s
(r )
n 33
(t  ) = s ( t  ) +
Where
d
n
d
n
s (t  )
=
(d )
a (t ) e
jα nd
I
(g)
o
s
(t
)
+
s
∑ i,n  n (t  )
- - - - - - (3)
i =1
and
(g)
i ,n
s
(t  ) = a
(g)
i ,n
(t  )e
jα i(,gn )
(d )
α
Analogously to (2), n = (2π / λ ) (u d xn + vd yn + qd zn ),
α i(,gn ) =
2π
λ
n = 1,….., N;
[ pi − ( pi ui − xn ) 2 + ( pi vi − y ) 2 + ( pi ui − z n ) 2
I = 1,……. I
]
--------------- (4)
To model [16] the phase term of the ith interference source located at (pi,ɵi,
øi) either in the far-field depending on the value of pi.
As far as the signal
s ( e ) available at the output of the summer is
concerned, it appears that
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S
(e)
(te ) =
60
N
∑W
n =1
- - - - - - (5)
(r)
n8 n
(t e )
Where wn = wn e jβn is the nth complex weight. Consequently, the total
output power measured by the single receiver is equal to
N
N
p (t e ) = pt ( w) ∆
∑ w
≈ n =1
p
e − jβ n
∑w
p =1
pe
− j 3p
Ω rp.n (t e ) - - - - - - - (6)
That is a function of w = {wn ; n = 1,....N}, Ω p , n (t e ) being the (p,n)-entry of
the convariance matrix of the received signal.
In order to minimize the total output power thus removing the interfering
signals from the output of the array, the array coefficients are iteratively
updated for taking into account constantly changing (i.e., at each time-step)
conditions and the need of a readaptation to new environments. Moreover, a
time-varying phase-only control is implemented to reduce the complexity
and the costs of the adaptive system. In particular, the following
optimization problem:
r
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β opt (te ) = arg {min β [ p (t e )]} - - - - - - (7)
Is solved by means of the enhanced PSO-based strategy (section III) to
determine the optimal setting of the phase. {β = ( β n:n =1,.... N }, since amplitude
coefficients {wn : n = 1.....N } are fixed quantities (e.g., uniform amplitudes or
distributed according to Dolph-Chebychev pattern)
SMART ANTENNAS BENEFITS
The principal reason for the growing interest in smart antenna system is the
capacity increase. In densely populated areas, mobile systems are normally
interference-limited, meaning that the interference from other users is the
main source of noise in the system. This means that the signal-tointerference ratio (SIR) is much larger than the signal-to-noise ratio(SNR). In
general, smart antennas will increase the SIR by simultaneously increasing
the useful received signal level and lowering the interference level.
Another benefit that smart-antenna systems provide is an increase of range.
Because smart antennas are more directional than omnidirectional and
sectorized antennas, a range-increase potential is available. In other words,
smart antennas are able to focus their energy toward the intended users,
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instead of wasting it in other unnecessary directions, like the
omnidirectional antennas. This means that base stations can be placed
further apart, leading to a more cost-efficient development. Therefore,
smart-antenna systems will be most suited in rural and sparsely populated
areas, where radio coverage rather than capacity is more important [4].
Another added advantage of smart-antenna systems is security in a society
that is becoming more dependent on conducting business and transmitting
personal information, security is an important issue. Smart antennas make
it more difficult to tap a connection. Because the intruder must be
positioned in the same direction as the user as seen from the base station
to successfully tap a connection.
Finally, due to the spatial-detection nature of smart-antenna systems,
they can be used to locate people in case of emergencies, or for any other
location-specific service.
Smart antennas not gained traction with wireless Network operators.
Many research and development activities have been poured into developing
smart antennas for wireless communications systems. Yet despite the
promise of increased network capacity and enhanced spectrum utilization,
smart antenna system have largely failed to break into the mainstream
cellular networks, as operators have backed at adopting these technologies.
In such conditions, MIMO techniques appear to be more suitable,
since they rely on a rich propagation channel to increase system capacity.
MIMO systems are based on using multiple antennas with uncorrelated
signals, along with channel information to pack more bits into the
communication channel. Instead of a large, single panel that beamswitching and some adaptive antenna systems require, a number of
antennas separated in space can be used. This is similar to the current
configuration of cellular base stations, where most sectors feature at least
two antennas for diversity reception. In short, MIMO techniques address
many of the logistical issues that have limited the deployment of earlier
“smart antenna” systems.
The expected growth in broadband-data wireless systems is the likely
catalyst that will spur the deployment of smart antenna systems.
Bandwidth remains the basic requirement for achieving high data rates,
despite all the improvements made in access technologies, error-control
coding, modulation techniques, and physical and medium access control
layer performance. Provide that smart antenna systems can address the
issues of cost, integration, simplicity of implementation, and demonstrable
and quantifiable improvements, their deployment is only a matter of time.
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CONCLUSIONS
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Insight into smart-antenna systems, using the human auditory system as
an analogy, as well as a general overview of smart antennas. Moreover, the
quest for more capacity was illustrated with the evolution of the cellular
radio system.
This paper has investigated both the theoretical and numerical aspects of
the use of digital phase-shifters only weighting for adaptive null steering in
complex interference scenarios. It has demonstrated the application of a
PSO-based control equipped with enhanced memory features for the
adaptation of the antenna array to minimize the total output power at the
receiver. The mathematical formulation of the approach and the algorithmic
sequence of the enhanced adaptive control have been carefully described.
The numerical validation has been carried out by considering different array
geometries and various interference configurations.
References
1. R.H.Roy, “ An overview of smart Antenna Technology: The Next Wave in Wireless
Communications” 1998 IEEE Aerospace Conference (Volume 3), May 1998, pp. 339345.
2. J.Razavilar Rashid-Farrokhi and K.J.R. Liu, “Traffic Improvements in Wireless
Communication Networks using Antenna Array, “ IEEE Journal on Selected Areas in
Communications, 18, March 2000, pp. 458-471.
3. P.Rashid-Farokhi, L.Tassiulas and K.J.R.Liu, “ Joint Optimal Power Control and
Beamforming in Wireless Networks using Antenna Array, “ IEEE Transactions on
Communications, CGM-46, October 1998, pp 1313-1324.
4. A.O.Boukalov and S.G. Haggman, “ System Aspects of smart Antenna Technology in
Cellular Wireless Communication.
5. C.B. DIETRICH, w.l.Stutzman, K.Byung-Ki, and K.Dietze, “ Smart antennas in
wireless communications: Base-station diversity and handset beamforming, “IEEE
Antennas propag. Mf., vol. 52. pp-142-151, oct. 2000.
6. M.Chryssomallis, “Smart antennas,” IEEE Antenna propag. Mag., vol. 42 pp. 129136, jun. 2000
7. S.P.Applebaum, “ Adaptive arrays,” IEEE Trans. Antennas Propag., vol. 24. pp. 585598, Sep. 1976.
8. B.widrow, P.E.Mantey, L.J.Griffiths, and B.B.Goode,” Adaptive antenna systems.”
Proc. IEEE, vol. 55, pp. 2143-2159, Dec. 1967.
9. L.C.Godara, “ improved LMS algorithm for adaptive beamforming,”IEEE Trans.
Antennas Propag., vol. 38 pp. 1631-1635. Oct 1990.
10. R.L.Haupt, “Phase only adaptive sidelobe nulling using digitally controlled phaseshifters,” IEEE Trans. Antennas Propag., vol. 24, pp. 638-649, Sep. 1976
11. D.S.Weile and E.Michielssen, “ The control of adaptive antenna arrays with genetic
algorithms using dominance and diploidy,” IEEE Trans. Antenna Propag., vol. 49 pp.
1424-1433, Oct 2001.
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12. M.Donelli, R.Azaro, F.D.Natale and A.Massa, “ An innovative computational
approach based on a particle swarm strategy for adaptive phased-arrays control,” IEEE
Trans. Antennas Propag., vol 54, pp. 888-898. Mar 2006.
13. C.A.Balanis, Antenna Theory, Newyork : wiley, 1996
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