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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

as a key technology for capacity increase in 3G radio networks. Smart Antennas offer a mixed service capacity gain of more than 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, signal power radiated throughout the cell area will be experienced as interference by any other user than the desired one. Concurrently the base station receives ”interference” emanating from the individual users within the system. Smart Antennas offer a relief by transmitting / receiving the power only to / from the desired directions.

A Smart Antenna consists of M antenna elements, whose signals are processed adaptively in order to exploit the spatial dimension of the mobile radio channel. In the simplest case, the signals received at the different antenna elements are multiplied with complex weights, and then summed up; the weights are chosen adaptively. Not the antenna itself, but rather the complete antenna system including the signal processing is adaptive or smart. All M elements of the antenna array have to be combined (weighted) in order to adapt to the current channel and user

characteristics. This weight adaptation is the ”smart” part of the Smart Antennas, which should hence (more precisely) be called ”adaptive antennas”.

Fig. 1. Smart antenna patterns in a multi-service UMTS system with high data rate

interferers and desired low data rate users.

Smart Antennas can be used to achieve different benefits. The most important is higher network capacity, i.e. the ability to serve more users per base station, thus increasing revenues of network operators, and giving customers less probability of blocked or dropped calls. Also, the transmission quality can be improved by increasing desired signal power and reducing interference. A schematic model of how Smart Antennas work is shown in Figure 1. The example cell serves several low data rate users and a few high data rate users. The latter are indicated by mobile terminals with large screen and keyboard. Let us consider the uplink first: Without Smart Antennas the high data rate users heavily interfere with the more distant desired user. The former have to send with higher TX power in order to fulfill the

requirements at the receiver. Using Smart Antennas means the antenna beams are directed towards and focused on the desired user and hence this user can be ”heard” much better. The interference from the high data rate interferers is reduced by setting broad nulls

Fig. 2. Antenna pattern of a eight-element uniform linear array. The signal arrives at 10°. Two interfering signals are shown, one at -35° and a stronger one at 32°. The smart antenna

algorithms compute the antenna weights for all eight antenna elements so that the Signal-to-Noise-and-Interference ratio (SNIR) becomes

an optimum.

in the antenna pattern towards their main direction of arrival. This interference reduction corresponds to an increase in the uplink coverage in a UMTS network. This is also shown in Figure 2.

Further benefits include a possible reduction of the delay spread, allowing higher data rates, and a reduction of the transmission power in both uplink and downlink. The latter is responsible for the downlink capacity limitation in UMTS networks. The less base station transmission power is required for a single link, the more users can be served. Hence, Smart Antennas can increase both the uplink and the downlink capacity of UMTS radio networks.

Having reviewed how a Smart Antenna can

improve the performance of a mobile system, we shall now look at how to achieve the individual improvements. In the following text

we will provide an overview of Smart Antenna classifications such as switched beam antennas, spatial processing, space-time-processing, and space-time detection. Then we will present an overview of the adaptation 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 sw ched 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. However, maxima and nulls of the antenna pattern can not be put into arbitrary directions, but can only be chosen from one of N possible positions.

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A more sophisticated approach is the spatial filter or spatial processing. The received signals are converted down to base band and sampled. This procedure requires M receiver chains. The signals of each receiver chain are multiplied with complex weights w, and then summed up. The resulting output signal can then be processed like any signal from a normal antenna. In wideband systems like UMTS, the signal is fed into a conventional equalizer1, which combines the signal components with different delays, leading to the term time or empora processing. The combination of these

two involves simultaneous filtering in space and time and is called space- me p ocessing.

Space-only processing works best if each antenna element shows the same time dispersion, i.e. the same shape of the impulse response. If this is not true, each antenna element should have a separate equalizer. If we use a linear equalizer of length L , the total structure has then M spatial and L temporal complex weights, leading to a complexity of

LM * . Instead of calculating the spatial and

1 In narrowband systems, a decision device can follow immediately

2

temporal weight vectors in a sequential manner, we can calculate them jointly, leading to a weight matrix of size LM * . The receiver is then also known as joint space- me receiver or joint space time equal zer. The output signal is then fed into a decision device for recovering the received bitstream.

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Finally, we could also do the space-time equalization and the detection jointly, leading to a so called joint space-time detection. Space-time detection offers best performance, but also the highest degree of complexity. Figure 3 shows block diagrams of both a decoupled space-time and a joint space-time receiver2.

Smart Antennas can also be classified in a different way: whether they use diversity or beamforming. Diversity relies essentially upon the statistical independence of the signals at different antenna elements. In the simplest case, one exploits the high improbability that the signals of all the elements are simultaneously in a fading dip.

Fig. 3. Space-Time receiver structures. (a) separate space and time domain weight adaptation, (b) joint space time filtering.

In order to achieve statistical independence various diversity techniques can be applied [3]. By using more advanced, but well known

combining methods for the diversity signals, the SNIR can finally be optimized [4] [3].

In beam forming, one exploits the close proximity of antenna elements in order that an appreciable correlation between the antenna elements is present. The close proximity of antenna elements allows forming a unique antenna pattern that enhances the desired signal and suppresses the interference.

III. WEIGHT ADAPTATION ALGORITHMS

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In the beamforming case the major question is: How to calculate the complex weights w for the individual an enna elements for each user? Before answering this question one should reflect upon the different processes in the baseband signal processing unit, before the antenna weights can be adapted. Basically the signal processing unit is responsible for the user identification, user sepa ation and beamforming. First, the base station has to estimate the directions of arrival of all multipath 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 refe ence (TR), spatial reference (SR) and blind (BA) algorithms.

A. Temporal Re e ence Algori hms (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 such a way that the difference between the combined signal at the output and the known training sequence is minimized. Those weights are then used for the reception of the actual data. The temporal reference approach can be used in conjunction with both diversity and beamforming methods, although it is more common with the former.

2 In literature, the separated space-time receiver structure is also named ”decoupled space-time rake”, ”beamformer rake”, ”2D-rake” and ”vector Rake - single beamformer”.

B. Spatial Reference Algorithms (SR)

SR algorithms estimate the direction of arrival (DOA) of both the desired and interfering

3

structure of the transmitted signal, e.g. finite alphabet, or cyclostationarity. If training sequences are used in combination with blind algorithms, they are called semi-blindalgori hms which show better performance than temporal reference algorithms or blind algorithms alone [5]. Currently, all blind or semi-blind algorithms require too much computation time to be employed in real time, but semi-blind algorithms are close to real-time implementation.

signals. They are based on the prior knowledge of the physical antenna geometry. In most mobile communication systems, the time a wavefront takes to pass through the antenna array is much smaller than the bit (or chip) interval Tb (Tc). Therefore, the narrowband assumption for antenna arrays is valid (see Figure 4). This makes it possible to model the time delays of the wave between the antenna elements as phase shifts. Hence, a received signal impinging at the antenna array at angle θ can be expressed as

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IV. EFFECTS ON RADIO NETWORK PLANNING

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The effects of Smart Antennas on the radio network planning process are various. The most important technical innovation regarding smart antenna radio network planning is the consideration of the spatial behavior o the mobile radio propaga on channel. Within the European research initiative COST 259 [6] several channel models have been developed. They are aimed at UMTS and HIPERLAN3, with particular emphasis on Smart Antennas and directional channels. They have been introduced in the 3rd generation standardization process by 3GPP [7].

where c(θ) is the array steering vector, d, λ¸

and M denote the inter-element spacing, the wavelength and the number of antenna elements. The notation (.)T indicates the transpose. For the estimation of the individual DOAs no additional information is needed. After user identification (e.g. by utilizing the training sequence) the signals can be separated and detected.

The spatial behavior of the received interference is another significant issue regarding the complex smart antenna radio network planning. If the interference is spatially white, i.e. the interferers are equally distributed in the coverage area, the gain due to Smart Antennas only has to be taken into account in the link budget. This can be easily implemented by utilizing look-up-tables, where the smart antenna gains are listed in order of the experienced signal to noise and interference ratio (SNIR).

C. Blind Algorithms (BA)

Instead of using a training sequence or the properties of the receiver array, “blind” algorithms can be applied for weight adaptation as well. Blind Algorithms basically try to extract the unknown channel impulse response and the unknown transmitted data from the received signal at the antenna elements. Even though they do not know the actual bits, Blind Algorithms use additional knowledge about the

. . . ..

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 considered even in the planning process! While simple beamsteering algorithms only consider the desired signal, more sophisticated algorithms take the interferers into account.

Fig. 4. Principle of SR algorithms. The phase shift between two antenna elements is defined

by the antenna geometry and the angle of incidence. k=2π/ λ, where λ is the wavelength,

d is the interelement spacing and M is the number of antenna elements.

Finally, Smart Antennas also affect the radio resource management (RRM). The RRM

3 HIgh PERformance Local Area Network

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algorithms are important for the planning process when the main concerns are about the number of served packet switched users and the quality of service (QoS) in the network.

Literature available on smart antenna

systems is vast and covers aspects such as capacity evaluation, identification and implementation of algorithms for array processing. Good overviews are given in [9], [10], [11], [12], [13], [14], [15], [16], [17].

V. SOLUTIONS OFFERED BY SYMENA

For a fast and efficient roll-out of Smart Antennas, enhanced planning tools are necessary. SYMENA offers a full range of software solutions for Smart Antenna radio network planning and optimization. SYMENA’s software solutions help operators to invest their money where it is needed and to avoid it where it is not.

Detailed information about the products can be found on the web-site http://www.symena com

REFERENCES

[1] H. Novak, Switched-Beam Adaptive Antenna System, PhD thesis, Technische Universität Wien, Vienna, Austria, Nov.1999, www.nt.tuwien.ac.at/mobile/

[2] S. Anderson, B. Hagerman, H. Dam, U. Forssen, J. Karlsson, F. Kronestedt, S. Mazur, and K.J. Molnar, “Adaptive antennas for GSM and TDMA systems”, IEEE Personal Communications, vol. 6, Issue 3, pp. 74–86, June 1999.

[3] J. G. Proakis, Digital Communications, McGraw Hill Book Comp. Inc., 1995.

[4] J. D. Parsons, The Mobile Radio Propagation Channel, John Wiley and Sons, Ltd, Chichester, England, 2000.

[5] J. Laurila, Semi-Blind Detection of Co-Channel Signals in Mobile Communications, PhD thesis, Technische Universität Wien, March 2000, www.nt.tuwien.ac.at/mobile/

[6] L. M. Correia, Wireless Flexible Personalized Communications - COST 259: European Co-Operation in Mobile Radio Research, J. Wiley and Sons Ltd., 2001.

[7] 3GPP, “Deployment aspects - TR 25.943, v4.0.0”, June 2001, http://www.3gpp.org.

[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.

[9] A. Paulraj and C. B. Papadias, “Space-time processing for wireless communications”, IEEESignal Processing Mag., vol. 14, pp. 49–83, November 1997.

[10] P. H. Lehne and M. Pettersen, “An overview of smart antenna technology for mobile communications systems”, IEEE Communications Surveys, vol. 2, pp. 2–13, 1999.

[11] A. F. Naguib and A. Paulraj, “Performance of wireless CDMA with m-ary orthogonal modulation and cell site antenna arrays”, IEEE Journal on Selected Areas in Communications, vol. 14, pp. 1770–1783, Dec. 1996.

[12] R. Rheinschmitt and M. Tangemann, “Performance of sectorised spatial multiplex systems”, Proc. IEEE Vehicular Technology Conference, 46th VTC 1996, vol. 1, pp. 426–430, 1996.

[13] J. Fuhl, A. Kuchar, and E. Bonek, “Capacity increase in cellular PCS by smart antennas”, Proc. IEEE Vehicular Technology Conference, 47th VTC 1997, vol. 3, pp. 1962– 1966, May 1997.

[14] A. F. Naguib, A. Paulraj, and T. Kailath, “Capacity improvement with base-station antenna arrays in cellular CDMA”, IEEE Transaction on Vehicular Technology, vol. 43, pp. 691–698, August 1994.

[15] A. O. Boukalov and S. G. Häggman, “System aspects of smart-antenna technology in cellular wireless communications - an overview”, IEEE Transactions on Microwave Theory and Techniques, vol. 48, pp. 919–929, June 2000.

[16] R. M. Buehrer, A. G. Kogiantis, S. Liu, J. Tsai, and D. Uptegrove, “Intelligent antennas for wireless communications - uplink”, Bell Labs Technical Journal, vol. July-September 1999, pp. 73–103, 1999.

[17] J. Fuhl, Smart Antennas for Second and Third Generation Mobile Communications Systems, PhD thesis, Technische Universtitaet Wien, 1997, www.nt.tuwien.ac.at/mobile/

Contact SYMENA Software & Consulting GmbH Wiedner Hauptstraße 24/15 A-1040 Vienna, Austria Tel. [+43-1] 585 51 01-0 Fax [+43-1] 585 51 01-99 info@symena.com www.symena.com