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Smart antennas for umts
1. Smart Antennas - A Technical Introduction
SYMENA Software & Consulting GmbH
Wiedner Hauptstraße 24/15, A-1040 Vienna, Austria
Phone: [+43-1] 585 51 01-0, Fax: [+43-1] 585 51 01-99
info@symena.com, www.symena.com
Abstract— Smart Antennas are recognized characteristics. This weight adaptation is the
as a key technology for capacity increase in ”smart” part of the Smart Antennas, which
3G radio networks. Smart Antennas offer a should hence (more precisely) be called
mixed service capacity gain of more than ”adaptive antennas”.
100% and hence reduce to less than half
the number of base stations required. They
are one of the most promising technologies
for the enabling of high capacity wireless
networks. Since Smart Antennas are more
expensive than conventional base stations,
they should be used where they are truly
needed.
In this paper we provide a brief overview
of Smart Antennas, their benefits and how
they actually work.
I. SMART ANTENNA BASICS
Conventional base station antennas in
existing operational systems are either
omnidirectional or sectorized. There is a waste
of resources since the vast majority of
transmitted signal power radiates in directions
other than toward the desired user. In addition, Fig. 1. Smart antenna patterns in a multi-
signal power radiated throughout the cell area service UMTS system with high data rate
will be experienced as interference by any other interferers and desired low data rate users.
user than the desired one. Concurrently the
base station receives ”interference” emanating Smart Antennas can be used to achieve
from the individual users within the system. different benefits. The most important is higher
Smart Antennas offer a relief by transmitting / network capacity, i.e. the ability to serve more
receiving the power only to / from the desired users per base station, thus increasing
directions. revenues of network operators, and giving
A Smart Antenna consists of M antenna customers less probability of blocked or
elements, whose signals are processed dropped calls. Also, the transmission quality
adaptively in order to exploit the spatial can be improved by increasing desired signal
dimension of the mobile radio channel. In the power and reducing interference. A schematic
simplest case, the signals received at the model of how Smart Antennas work is shown in
different antenna elements are multiplied with Figure 1. The example cell serves several low
complex weights, and then summed up; the data rate users and a few high data rate users.
weights are chosen adaptively. Not the antenna The latter are indicated by mobile terminals
itself, but rather the complete antenna system with large screen and keyboard. Let us consider
including the signal processing is adaptive or the uplink first: Without Smart Antennas the
smart. All M elements of the antenna array high data rate users heavily interfere with the
have to be combined (weighted) in order to more distant desired user. The former have to
adapt to the current channel and user send with higher TX power in order to fulfill the
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2. requirements at the receiver. Using Smart we will provide an overview of Smart Antenna
Antennas means the antenna beams are classifications such as switched beam
directed towards and focused on the desired antennas, spatial processing, space-time-
user and hence this user can be ”heard” much processing, and space-time detection. Then we
better. The interference from the high data rate will present an overview of the adaptation
interferers is reduced by setting broad nulls algorithms and, finally, we will show the effects
of the introduction of Smart Antennas on radio
network planning.
II. SMART ANTENNA RECEIVER CLASSIFICATIONS
Smart Antennas can basically be divided
into: switched beam, spatial processing, space-
time-processing, and space-time detection. The
simplest implementation is the so-called
switched beam system, in which a single
transceiver is connected to the RF-
beamforming unit. If the number of antenna
elements is M, one out of the predefined set of
beams (N ≤ M) is selected, based on maximum
received signal power or minimum bit error
ratio (BER) [1] [2]. The best signal is selected
for further processing by a standard receiver.
This technique benefits from its simplicity.
Fig. 2. Antenna pattern of a eight-element
However, maxima and nulls of the antenna
uniform linear array. The signal arrives at 10°.
pattern can not be put into arbitrary directions,
Two interfering signals are shown, one at -35°
but can only be chosen from one of N possible
and a stronger one at 32°. The smart antenna
positions.
algorithms compute the antenna weights for all
A more sophisticated approach is the spatial
eight antenna elements so that the Signal-to-
Noise-and-Interference ratio (SNIR) becomes
filter or spatial processing. The received signals
are converted down to base band and sampled.
an optimum.
This procedure requires M receiver chains. The
signals of each receiver chain are multiplied
in the antenna pattern towards their main with complex weights w, and then summed up.
direction of arrival. This interference reduction The resulting output signal can then be
corresponds to an increase in the uplink processed like any signal from a normal
coverage in a UMTS network. This is also antenna. In wideband systems like UMTS, the
shown in Figure 2. signal is fed into a conventional equalizer1,
Further benefits include a possible which combines the signal components with
reduction of the delay spread, allowing higher different delays, leading to the term time or
data rates, and a reduction of the transmission temporal processing. The combination of these
power in both uplink and downlink. The latter two involves simultaneous filtering in space and
is responsible for the downlink capacity time and is called space-time processing.
limitation in UMTS networks. The less base Space-only processing works best if each
station transmission power is required for a antenna element shows the same time
single link, the more users can be served. dispersion, i.e. the same shape of the impulse
Hence, Smart Antennas can increase both the response. If this is not true, each antenna
uplink and the downlink capacity of UMTS element should have a separate equalizer. If we
radio networks. use a linear equalizer of length L , the total
structure has then M spatial and L temporal
Having reviewed how a Smart Antenna can complex weights, leading to a complexity of
improve the performance of a mobile system, M * L . Instead of calculating the spatial and
we shall now look at how to achieve the
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individual improvements. In the following text In narrowband systems, a decision device can
follow immediately
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3. temporal weight vectors in a sequential combining methods for the diversity signals,
manner, we can calculate them jointly, leading the SNIR can finally be optimized [4] [3].
to a weight matrix of size M * L . The receiver is In beam forming, one exploits the close
then also known as joint space-time receiver or proximity of antenna elements in order that an
joint space-time equalizer. The output signal is appreciable correlation between the antenna
then fed into a decision device for recovering elements is present. The close proximity of
the received bitstream. antenna elements allows forming a unique
Finally, we could also do the space-time antenna pattern that enhances the desired
equalization and the detection jointly, leading signal and suppresses the interference.
to a so called joint space-time detection.
Space-time detection offers best performance, III. WEIGHT ADAPTATION ALGORITHMS
but also the highest degree of complexity.
In the beamforming case the major question
Figure 3 shows block diagrams of both a
is: How to calculate the complex weights w for
decoupled space-time and a joint space-time
the individual antenna elements for each user?
receiver2.
Before answering this question one should
Smart Antennas can also be classified in a
reflect upon the different processes in the
different way: whether they use diversity or
baseband signal processing unit, before the
beamforming. Diversity relies essentially upon
antenna weights can be adapted. Basically the
the statistical independence of the signals at
signal processing unit is responsible for the
different antenna elements. In the simplest
user identification, user separation and beam-
case, one exploits the high improbability that
forming. First, the base station has to estimate
the signals of all the elements are
the directions of arrival of all multipath
simultaneously in a fading dip.
components. Next, it has to determine whether
the echo from a certain direction comes from a
desired user or from an interferer. Finally, it
can compute the antenna weights in order to
increase the SNIR as much as possible.
Adaptation algorithms are designed to
process the above mentioned demands. They
can basically be classified as temporal
reference (TR), spatial reference (SR) and blind
(BA) algorithms.
A. Temporal Reference Algorithms (TR)
TR algorithms are based on the prior
knowledge of the time structure of parts of the
received signals. The training sequences of
both 2G (a midamble in GSM) and 3G (pilot
bits in UMTS) systems fulfill this requirement.
The receiver adjusts the complex weights in
Fig. 3. Space-Time receiver structures. (a) such a way that the difference between the
separate space and time domain weight combined signal at the output and the known
adaptation, (b) joint space time filtering. training sequence is minimized. Those weights
are then used for the reception of the actual
data. The temporal reference approach can be
In order to achieve statistical independence
used in conjunction with both diversity and
various diversity techniques can be applied [3].
beamforming methods, although it is more
By using more advanced, but well known
common with the former.
2
In literature, the separated space-time receiver B. Spatial Reference Algorithms (SR)
structure is also named ”decoupled space-time
rake”, ”beamformer rake”, ”2D-rake” and ”vector SR algorithms estimate the direction of
Rake - single beamformer”. arrival (DOA) of both the desired and interfering
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4. signals. They are based on the prior knowledge structure of the transmitted signal, e.g. finite
of the physical antenna geometry. In most alphabet, or cyclostationarity. If training
mobile communication systems, the time a sequences are used in combination with blind
wavefront takes to pass through the antenna algorithms, they are called semi-blind
array is much smaller than the bit (or chip) algorithms which show better performance than
interval Tb (Tc). Therefore, the narrowband temporal reference algorithms or blind
assumption for antenna arrays is valid (see algorithms alone [5]. Currently, all blind or
Figure 4). This makes it possible to model the semi-blind algorithms require too much
time delays of the wave between the antenna computation time to be employed in real time,
elements as phase shifts. Hence, a received but semi-blind algorithms are close to real-time
signal impinging at the antenna array at angle θ implementation.
can be expressed as
IV. EFFECTS ON RADIO NETWORK PLANNING
T
− j 2π λ sin (θ )
d
− j 2π sin (θ )( M −1)
d
The effects of Smart Antennas on the radio
c(θ ) = 1, e ,K, e λ
(1) network planning process are various. The most
important technical innovation regarding smart
antenna radio network planning is the
where c(θ) is the array steering vector, d, λ¸ consideration of the spatial behavior of the
and M denote the inter-element spacing, the mobile radio propagation channel. Within the
wavelength and the number of antenna European research initiative COST 259 [6]
elements. The notation (.)T indicates the several channel models have been developed.
transpose. For the estimation of the individual They are aimed at UMTS and HIPERLAN3,
DOAs no additional information is needed. with particular emphasis on Smart Antennas
After user identification (e.g. by utilizing the and directional channels. They have been
training sequence) the signals can be separated introduced in the 3rd generation
and detected. standardization process by 3GPP [7].
The spatial behavior of the received
C. Blind Algorithms (BA) interference is another significant issue
regarding the complex smart antenna radio
Instead of using a training sequence or the
network planning. If the interference is
properties of the receiver array, “blind”
spatially white, i.e. the interferers are equally
algorithms can be applied for weight adaptation
distributed in the coverage area, the gain due
as well. Blind Algorithms basically try to extract
to Smart Antennas only has to be taken into
the unknown channel impulse response and the
account in the link budget. This can be easily
unknown transmitted data from the received
implemented by utilizing look-up-tables, where
signal at the antenna elements. Even though
the smart antenna gains are listed in order of
they do not know the actual bits, Blind
the experienced signal to noise and
Algorithms use additional knowledge about the
interference ratio (SNIR).
The simplifying assumption of spatial
. . . .
whiteness holds in second generation CDMA
. systems at least approximately, where mainly
speech users with almost identical data rates
are served. It can be shown that this is no
longer true in multi-service high data rate
UMTS networks [8]. The consequence is that
smart antenna adaptation algorithms have to be
Fig. 4. Principle of SR algorithms. The phase considered even in the planning process! While
shift between two antenna elements is defined simple beamsteering algorithms only consider
by the antenna geometry and the angle of the desired signal, more sophisticated
incidence. k=2π/ λ, where λ is the wavelength, algorithms take the interferers into account.
d is the interelement spacing and M is the Finally, Smart Antennas also affect the radio
number of antenna elements. resource management (RRM). The RRM
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HIgh PERformance Local Area Network
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5. algorithms are important for the planning [9] A. Paulraj and C. B. Papadias, “Space-time
process when the main concerns are about the processing for wireless communications”, IEEE
number of served packet switched users and Signal Processing Mag., vol. 14, pp. 49–83,
November 1997.
the quality of service (QoS) in the network.
[10] P. H. Lehne and M. Pettersen, “An overview of
smart antenna technology for mobile
Literature available on smart antenna communications systems”, IEEE
systems is vast and covers aspects such as Communications Surveys, vol. 2,
capacity evaluation, identification and pp. 2–13, 1999.
implementation of algorithms for array [11] A. F. Naguib and A. Paulraj, “Performance of
processing. Good overviews are given in [9], wireless CDMA with m-ary orthogonal
[10], [11], [12], [13], [14], [15], [16], [17]. modulation and cell site antenna arrays”, IEEE
Journal on Selected Areas in Communications,
vol. 14, pp. 1770–1783, Dec. 1996.
V. SOLUTIONS OFFERED BY SYMENA
[12] R. Rheinschmitt and M. Tangemann,
For a fast and efficient roll-out of Smart “Performance of sectorised spatial multiplex
Antennas, enhanced planning tools are systems”, Proc. IEEE Vehicular Technology
necessary. SYMENA offers a full range of Conference, 46th VTC 1996, vol. 1, pp. 426–
430, 1996.
software solutions for Smart Antenna radio
[13] J. Fuhl, A. Kuchar, and E. Bonek, “Capacity
network planning and optimization. SYMENA’s increase in cellular PCS by smart antennas”,
software solutions help operators to invest their Proc. IEEE Vehicular Technology Conference,
money where it is needed and to avoid it where 47th VTC 1997, vol. 3, pp. 1962– 1966, May
it is not. 1997.
Detailed information about the products can [14] A. F. Naguib, A. Paulraj, and T. Kailath,
be found on the web-site “Capacity improvement with base-station
http://www.symena.com antenna arrays in cellular CDMA”, IEEE
Transaction on Vehicular Technology, vol. 43,
pp. 691–698, August 1994.
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v4.0.0”, June 2001, http://www.3gpp.org. Fax [+43-1] 585 51 01-99
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