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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

                                                                                                       1
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
                                                       1
individual improvements. In the following text           In narrowband systems, a decision device can
                                                    follow immediately


                                                                                                         2
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



                                                                                                            3
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
                                                                    3
                                                                        HIgh PERformance Local Area Network


                                                                                                                     4
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.
                      REFERENCES
                                                        [15]   A. O. Boukalov and S. G. Häggman, “System
[1]   H. Novak, Switched-Beam Adaptive Antenna                 aspects of smart-antenna technology in cellular
      System, PhD thesis, Technische Universität               wireless communications - an overview”, IEEE
      Wien,       Vienna,      Austria,    Nov.1999,           Transactions on Microwave Theory and
      www.nt.tuwien.ac.at/mobile/                              Techniques, vol. 48, pp. 919–929, June
[2]   S. Anderson, B. Hagerman, H. Dam, U.                   2000.
      Forssen, J. Karlsson, F. Kronestedt, S. Mazur,    [16] R. M. Buehrer, A. G. Kogiantis, S. Liu, J. Tsai,
      and K.J. Molnar, “Adaptive antennas for GSM            and D. Uptegrove, “Intelligent antennas for
      and     TDMA      systems”,     IEEE   Personal        wireless communications - uplink”, Bell Labs
      Communications, vol. 6, Issue 3, pp. 74–86,            Technical Journal, vol. July-September 1999,
      June 1999.                                             pp. 73–103, 1999.
[3]   J. G. Proakis, Digital Communications, McGraw     [17] J. Fuhl, Smart Antennas for Second and Third
      Hill Book Comp. Inc., 1995.                            Generation Mobile Communications Systems,
[4]   J. D. Parsons, The Mobile Radio Propagation            PhD thesis, Technische Universtitaet Wien,
      Channel, John Wiley and Sons, Ltd, Chichester,         1997, www.nt.tuwien.ac.at/mobile/
      England, 2000.
[5]   J. Laurila, Semi-Blind Detection of Co-Channel    Contact
      Signals in Mobile Communications, PhD thesis,
      Technische Universität Wien, March 2000,
      www.nt.tuwien.ac.at/mobile/                       SYMENA
[6]   L. M. Correia, Wireless Flexible Personalized     Software & Consulting GmbH
      Communications - COST 259: European Co-           Wiedner Hauptstraße 24/15
      Operation in Mobile Radio Research, J. Wiley
      and Sons Ltd., 2001.
                                                        A-1040 Vienna, Austria
[7]   3GPP, “Deployment aspects - TR 25.943,            Tel.   [+43-1] 585 51 01-0
      v4.0.0”, June 2001, http://www.3gpp.org.          Fax    [+43-1] 585 51 01-99
[8]   T. Neubauer and E. Bonek, “Smart-antenna          info@symena.com
      space-time UMTS uplink processing for system      www.symena.com
      capacity    enhancement”,      Annales     of
      telecommunications, Special Issue on UMTS,
      May-June 2001, vol. 5-6, pp. 306–316, 2001.



                                                                                                                  5

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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 1
  • 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 1 individual improvements. In the following text In narrowband systems, a decision device can follow immediately 2
  • 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 3
  • 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 3 HIgh PERformance Local Area Network 4
  • 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. REFERENCES [15] A. O. Boukalov and S. G. Häggman, “System [1] H. Novak, Switched-Beam Adaptive Antenna aspects of smart-antenna technology in cellular System, PhD thesis, Technische Universität wireless communications - an overview”, IEEE Wien, Vienna, Austria, Nov.1999, Transactions on Microwave Theory and www.nt.tuwien.ac.at/mobile/ Techniques, vol. 48, pp. 919–929, June [2] S. Anderson, B. Hagerman, H. Dam, U. 2000. Forssen, J. Karlsson, F. Kronestedt, S. Mazur, [16] R. M. Buehrer, A. G. Kogiantis, S. Liu, J. Tsai, and K.J. Molnar, “Adaptive antennas for GSM and D. Uptegrove, “Intelligent antennas for and TDMA systems”, IEEE Personal wireless communications - uplink”, Bell Labs Communications, vol. 6, Issue 3, pp. 74–86, Technical Journal, vol. July-September 1999, June 1999. pp. 73–103, 1999. [3] J. G. Proakis, Digital Communications, McGraw [17] J. Fuhl, Smart Antennas for Second and Third Hill Book Comp. Inc., 1995. Generation Mobile Communications Systems, [4] J. D. Parsons, The Mobile Radio Propagation PhD thesis, Technische Universtitaet Wien, Channel, John Wiley and Sons, Ltd, Chichester, 1997, www.nt.tuwien.ac.at/mobile/ England, 2000. [5] J. Laurila, Semi-Blind Detection of Co-Channel Contact Signals in Mobile Communications, PhD thesis, Technische Universität Wien, March 2000, www.nt.tuwien.ac.at/mobile/ SYMENA [6] L. M. Correia, Wireless Flexible Personalized Software & Consulting GmbH Communications - COST 259: European Co- Wiedner Hauptstraße 24/15 Operation in Mobile Radio Research, J. Wiley and Sons Ltd., 2001. A-1040 Vienna, Austria [7] 3GPP, “Deployment aspects - TR 25.943, Tel. [+43-1] 585 51 01-0 v4.0.0”, June 2001, http://www.3gpp.org. Fax [+43-1] 585 51 01-99 [8] T. Neubauer and E. Bonek, “Smart-antenna info@symena.com space-time UMTS uplink processing for system www.symena.com capacity enhancement”, Annales of telecommunications, Special Issue on UMTS, May-June 2001, vol. 5-6, pp. 306–316, 2001. 5