Smart Antennas - A Technical Introduction                              SYMENA Software & Consulting GmbH                  ...
requirements at the receiver. Using Smart           we will provide an overview of Smart AntennaAntennas means the antenna...
temporal weight vectors in a sequential                 combining methods for the diversity signals,manner, we can calcula...
signals. They are based on the prior knowledge                   structure of the transmitted signal, e.g. finiteof the ph...
algorithms are important for the planning               [9]    A. Paulraj and C. B. Papadias, “Space-timeprocess when the ...
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Smart antennas for umts

  1. 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, Abstract— Smart Antennas are recognized characteristics. This weight adaptation is theas a key technology for capacity increase in ”smart” part of the Smart Antennas, which3G radio networks. Smart Antennas offer a should hence (more precisely) be calledmixed service capacity gain of more than ”adaptive antennas”.100% and hence reduce to less than halfthe number of base stations required. Theyare one of the most promising technologiesfor the enabling of high capacity wirelessnetworks. Since Smart Antennas are moreexpensive than conventional base stations,they should be used where they are trulyneeded. In this paper we provide a brief overviewof Smart Antennas, their benefits and howthey actually work. I. SMART ANTENNA BASICS Conventional base station antennas inexisting operational systems are eitheromnidirectional or sectorized. There is a wasteof resources since the vast majority oftransmitted signal power radiates in directionsother 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 ratewill be experienced as interference by any other interferers and desired low data rate users.user than the desired one. Concurrently thebase station receives ”interference” emanating Smart Antennas can be used to achievefrom the individual users within the system. different benefits. The most important is higherSmart Antennas offer a relief by transmitting / network capacity, i.e. the ability to serve morereceiving the power only to / from the desired users per base station, thus increasingdirections. revenues of network operators, and giving A Smart Antenna consists of M antenna customers less probability of blocked orelements, whose signals are processed dropped calls. Also, the transmission qualityadaptively in order to exploit the spatial can be improved by increasing desired signaldimension of the mobile radio channel. In the power and reducing interference. A schematicsimplest case, the signals received at the model of how Smart Antennas work is shown indifferent antenna elements are multiplied with Figure 1. The example cell serves several lowcomplex 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 terminalsitself, but rather the complete antenna system with large screen and keyboard. Let us considerincluding the signal processing is adaptive or the uplink first: Without Smart Antennas thesmart. All M elements of the antenna array high data rate users heavily interfere with thehave to be combined (weighted) in order to more distant desired user. The former have toadapt to the current channel and user send with higher TX power in order to fulfill the 1
  2. 2. requirements at the receiver. Using Smart we will provide an overview of Smart AntennaAntennas means the antenna beams are classifications such as switched beamdirected 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 webetter. The interference from the high data rate will present an overview of the adaptationinterferers 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 antennauniform 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 multipliedin 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 becorresponds to an increase in the uplink processed like any signal from a normalcoverage in a UMTS network. This is also antenna. In wideband systems like UMTS, theshown in Figure 2. signal is fed into a conventional equalizer1, Further benefits include a possible which combines the signal components withreduction of the delay spread, allowing higher different delays, leading to the term time ordata rates, and a reduction of the transmission temporal processing. The combination of thesepower in both uplink and downlink. The latter two involves simultaneous filtering in space andis 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 eachstation transmission power is required for a antenna element shows the same timesingle link, the more users can be served. dispersion, i.e. the same shape of the impulseHence, Smart Antennas can increase both the response. If this is not true, each antennauplink and the downlink capacity of UMTS element should have a separate equalizer. If weradio 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 ofimprove the performance of a mobile system, M * L . Instead of calculating the spatial andwe shall now look at how to achieve the 1individual improvements. In the following text In narrowband systems, a decision device can follow immediately 2
  3. 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 closethen also known as joint space-time receiver or proximity of antenna elements in order that anjoint space-time equalizer. The output signal is appreciable correlation between the antennathen fed into a decision device for recovering elements is present. The close proximity ofthe received bitstream. antenna elements allows forming a unique Finally, we could also do the space-time antenna pattern that enhances the desiredequalization and the detection jointly, leading signal and suppresses the a so called joint space-time detection.Space-time detection offers best performance, III. WEIGHT ADAPTATION ALGORITHMSbut also the highest degree of complexity. In the beamforming case the major questionFigure 3 shows block diagrams of both a is: How to calculate the complex weights w fordecoupled 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 thedifferent way: whether they use diversity or baseband signal processing unit, before thebeamforming. Diversity relies essentially upon antenna weights can be adapted. Basically thethe statistical independence of the signals at signal processing unit is responsible for thedifferent 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 estimatethe signals of all the elements are the directions of arrival of all multipathsimultaneously 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 andvarious diversity techniques can be applied [3]. beamforming methods, although it is moreBy 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-timerake”, ”beamformer rake”, ”2D-rake” and ”vector SR algorithms estimate the direction ofRake - single beamformer”. arrival (DOA) of both the desired and interfering 3
  4. 4. signals. They are based on the prior knowledge structure of the transmitted signal, e.g. finiteof the physical antenna geometry. In most alphabet, or cyclostationarity. If trainingmobile communication systems, the time a sequences are used in combination with blindwavefront takes to pass through the antenna algorithms, they are called semi-blindarray is much smaller than the bit (or chip) algorithms which show better performance thaninterval Tb (Tc). Therefore, the narrowband temporal reference algorithms or blindassumption for antenna arrays is valid (see algorithms alone [5]. Currently, all blind orFigure 4). This makes it possible to model the semi-blind algorithms require too muchtime 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-timesignal 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 radioc(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 theand M denote the inter-element spacing, the mobile radio propagation channel. Within thewavelength 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 AntennasAfter user identification (e.g. by utilizing the and directional channels. They have beentraining sequence) the signals can be separated introduced in the 3rd generationand detected. standardization process by 3GPP [7]. The spatial behavior of the receivedC. 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 isproperties of the receiver array, “blind” spatially white, i.e. the interferers are equallyalgorithms can be applied for weight adaptation distributed in the coverage area, the gain dueas well. Blind Algorithms basically try to extract to Smart Antennas only has to be taken intothe unknown channel impulse response and the account in the link budget. This can be easilyunknown transmitted data from the received implemented by utilizing look-up-tables, wheresignal at the antenna elements. Even though the smart antenna gains are listed in order ofthey do not know the actual bits, Blind the experienced signal to noise andAlgorithms 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! Whileshift between two antenna elements is defined simple beamsteering algorithms only consider by the antenna geometry and the angle of the desired signal, more sophisticatedincidence. 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. 5. algorithms are important for the planning [9] A. Paulraj and C. B. Papadias, “Space-timeprocess when the main concerns are about the processing for wireless communications”, IEEEnumber 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”, IEEEsystems 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 ofprocessing. 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 multiplexAntennas, enhanced planning tools are systems”, Proc. IEEE Vehicular Technologynecessary. SYMENA offers a full range of Conference, 46th VTC 1996, vol. 1, pp. 426– 430, solutions for Smart Antenna radio [13] J. Fuhl, A. Kuchar, and E. Bonek, “Capacitynetwork 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, Mayit 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 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 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, England, 2000.[5] J. Laurila, Semi-Blind Detection of Co-Channel Contact Signals in Mobile Communications, PhD thesis, Technische Universität Wien, March 2000, 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, Fax [+43-1] 585 51 01-99[8] T. Neubauer and E. Bonek, “Smart-antenna space-time UMTS uplink processing for system capacity enhancement”, Annales of telecommunications, Special Issue on UMTS, May-June 2001, vol. 5-6, pp. 306–316, 2001. 5