Increasing the capacity of GSM cellular radio usin adaptive antennas

Abstract: The capacity of mobile cellular radio networks can be considerably increased if a higher level of cochannel interference can be tolerated. An adaptive antenna combines the outputs of an array of elements to maximise the wanted signal and suppress interfering signals. The author proposes an adaptive antenna algorithm suitable for GSM and the urban multipath environment. The algorithm is based on iterative reference signal reconstruction froin the GSM training sequence. The average error rate is obtained by simulation for four equal power interfering signals and an urban GSM multipath model. An adaptive antenna can only practically be deployed at the base station. Realising its gains in a real network on both the up and downlinks requires, for the downlink, a number of techniques. This is discussed and a concept of how the full capacity gains can be achieved in an evolutionary manner on both links is proposed. The concept is equally applicable to the DCS 1800 and DCS 1900 systems based on GSM.

1 Introduction

The reuse of spectrum in current cellular radio systems is limited by the level of cochannel interference that can be tolerated. Adaptive antennas provide a means of reducing the susceptibility of the receiver to cochannel interference and, consequently, allow the reuse of fre- quencies in every cell. Such capacity gains are most likely to be required in urban areas. This paper exam- ines the application of adaptive antenna techniques to GSM for the urban multipath environment.

Adaptive antennas (also sometimes known as ai1 adaptive array, smart antenna or optimum combiner) have been employed in military systems for many years as a counter measure for deliberate, high-power jam- ming. It operates by combining the outputs of an array of spatially distributed elements. As such it has much in common with spatial diversity combining. Whereas a maximal ratio combiner maximises the received signal to noise ratio in the absence of interference, an adap-

0 IEE, 1996 IEE Puocc.e&gs online no. 19960743 Paper first reccived 20th June 1995 and in reviscd form 8th May 1996 Roke Manov Research Ltd., Roke Manor, Romsey, Hants SO51 OZN, UK

tive antenna maximises the signal-to-interference-plus- noise ratio (STNR) [I]. Their use in cellular radio to counter cochannel interference has, it is believed, yet to be exploited in practical systems. However, a number of authors have examined this application. [2] shows that sufficient cochannel interference reduction can be achieved using adaptive antennas for reuse of frequen- cies in every cell. This assumed point sources, flat fad- ing and complete knowledge of the wanted signal to enable the optimal Wiener solution for the combining weights to be calculated. In the urban environment propagation often occurs via multipath reflections rather than via a direct path and the delay spread of the multipath is often large enough to cause frequency- selective fading (intersymbol interference) for GSM [3]. The question also remains of how to actually calculate the optimal combining weights without complete a pri- ori knowledge of the wanted signal. Some papers [4, 51 have exploited differences in the directions of arrival of the signals to enable the adaptive antenna to discrimi- nate between the wanted signal and unwanted interfer- ence. Others [6, 71 have based the discrimination on knowing only a part of the wanted signal and using this as a reference signal. Section 2 derives a new refer- ence generation approach suitable for GSM (also DCS1800, DCS1900). In Section 3 the performance of an adaptive antenna using this technique is evaluated for an urban GSM fading environment.

This paper considers applying adaptive antennas to the base station. At the mobile an antenna separation of around hall a wavelength would be needed to give negligible mutual coupling between the elements and independent fading in each channel [8]. The size and cost implications of applying adaptive antenna technology at the mobile are likely to make this approach impractical. In principle the optimum weights used at the base station for the uplink are also the optimum solution for the downlink but both the time and frequency separation of the receive and transmit channels in GSM mean that all the gains achieved for the uplink are likely to be only partially realised for the downlink. To achieve the full gains on both links the adaptive antenna will need to be combined with other interference reduction techniques. This is discussed in Section 4.

2 Adaptive antenna for GSM

Consider urban mobile radio since this is where the highest capacity is generally needed. This presents a particularly complex signal environment owing to both cochannel interference and multipath propagation. Even though base station antennas for conventional

IEE Pi.oc.-C'oii?mLm., Vo/. 143, No. 5, Oclohei 1996 304

cells are mounted above the local skylline, there is often no direct path between the base station and the mobile and communication occurs via reflections off buildings, vehicles, etc. The combination of all these paths creates a spatially fluctuating standing wave field. Movement of the mobile through this field or the movement of nearby vehicles causes the received signal strength to vary with time. The envelope of the received signal is often assumed to have a Rayleigh dktribution. This is superimposed on a slower, spatially varying mean sig- nal level due to the shadowing of buildings, the nature of the terrain, etc. With frequency reuse in neighbour- ing cells the wanted signal may be accompanied by sev- eral interferers of similar power and numerous interfering signals of reduced power. Each of these will propagate with many different paths ;as well.

GSM employs a TDMA structure with eight traffic channels multiplexed onto a carrier. Duplex operation is achieved in frequency with the downlink (base sta- lion to mobile) 45MHz higher than the uplink. The bit rate supported on one carrier is 270.833Kbitis. In deriving the GSM s,pecification extensive measurements of channel impulse responses were made for, amongst others, the urban environment [3]. Significant multip- aths with delays of 5ps or more occur thus intersymbol interference is experienced and GSNII, to counter this, employs equalisation.

.. I..........

resolve coherent multipath., In [4], low-resolution D F methods such a s conventional beamforming are pre- ferred owing to their insensitivity to coherent rriultip- ath. When it conies to selecting the weight vector [5] attempts to combine correlated multipaths of the wanted mobile while cancelling unwanted signals. [4] simply forms a fixed beam in the direction of the most power. Both these approaches are suboptimum. The approximate maximum-likelihood DF method will not be able to resolve all the signals and their individual paths. The maximum that can be achieved equals the number of elements in the array minus one. Also such techniques have demanding computational require- ments and they need accurate knowledge of how the antenna element gains and phases change with direc- tion relative to each other. The second inethod imakes no attempt to combine the wanted signal multipath or to cancel the interference, relying on the sidelobe struc- ture of the fixed beam. Capacity gains are likely to be similar to that obtainable with a corresponding degree of sectorisation. Interfering mobiles of comparable power might even be selected as the wanted signal. This latter problem also occurs with constant-modulus-algo- rithm-driven adaptive antennas. This algorithm adapts to maintain a constant mo'dulus signal at the adaptive antenna output and has been used to cancel long-delay multipath [9]. However, it can capture interfering sig- nals so is unlikely to be suitable for cellular radio with reuse in neighbouring cells.

In [6] an adaptive antenna for cellular radio is con- sidered when a reference signal for the wanted mobile is available. The reference signal does not need to be a full version of the wanted signal, it just needs to corre- late with the wanted signal but not with the interferers. The GSM training sequence meets this criterion. It is selected from eight 16-bit codes with low crosscorrela- tion and with neighbouring base stations, and hence neighbouring mobiles, using different codes. A similar approach is proposed for IS-54 [7], the US digital AMPS cellular system.

Consider the adaptive antenna output signal y( t ) to consist of the known reference signal r(t) plus an error signal e(t) which contains the interference and receiver noise. Thus

e ( t ) = Y ( t ) - r ( t )

1 Fig. 'I Busvic adaptive unteniui urchitectuw

The basic architecture of an adaptive antenna is shown in Fig. 1. Each of the N antenna signals is mod- ified in amplitude ,and phase (multiplied by a complex weight wJ and the resulting signals summed lo give a single array output. The weight vel-tor w(wI, iv2, _.., w,, , )~ needs to be calculated so that the ratio of the wanted signal power to the interference plus noise power is maximised. This requires some method of dis- crimination between mobiles. The use of direction is proposed in [4, 51 ( [ 5 ] also compares the use of direc- tion with using a perfect reference). Both of these employ a direction finding (DF) algorithm to locate the wanted mobile, although the multipath generally means the wanted signal will arrive from a spread of direc- tions. In [5] , a coimplex approximate maximum-likeli- hood D F algorithm is used owing to its ability to

= XTW - r ( t ) where x is a vector containing the N-element signals at the corresponding instant in time. Extending this to p successive time samples:

e and r are now of length p and the p successive sam- ples of the element signals make up the columns of X. The weight vector that minimises the error signal powcr is given by the Wiener-Hopf equation [10[1

where M, ~ = XI'X and b.,,. = X H r , and N indicates com- plex conjugate (Hermitian) transpose. My, is an esti- mate of the covariance matrix of thc element signals. b.yr is an estimate of the crosscorrelation between the reference and the element signals. We can thus calcu- late the weight vector that minimises the squared error between the array output and our reference signal. If the reference signal is a replica of the wanted signal and is correctly synchronised with its transmission then any interfering signals will be minimised and the recep-

e = XWT - r

Mxxw = bz, (1)

tion of the wanted signal maximised. Since the wanted signal originates from a binary sequence it has (at least before the effects of the channel) a finite number of possible waveforms and could, in principle, be found from

mirikllXwk - r k / I 2

where rli is the modulated waveform arising from the kth possible binary sequence and wl, the corresponding weight vector that minimises the mean square error. Consider the format of the GSM burst (Fig. 2). This contains 116 unknown bits which is too many for this approach to be computationally realisable. Instead con- sider the following scheme: (i) Calculate a weight vector (wtJ using just the period when the known training sequence is present. (ii) Apply the weight vector to all the data (y = Xwts) and then pass the result to the GSM equaliser (maxi- mum likelihood sequence estimation) to detect the unknown bits. (iii) Use the detected bits and a GSM modulator to construct a reference waveform for the period of the entire burst. Calculate a new weight vector (w’) using this reference. (iv) Apply the weight vector to all the data (y’ = Xw’) and then pass the result to the GSM equaliser.

Fig.2 GSiM burst structure

By calculating a weight vector just for the samples when the training sequence is transmitted and then applying it to the whole burst we have a scheme for improving the SINR using just knowledge of the train- ing sequence. This is not optimal since the error for the non training sequence bits is not minimised. If an inter- ferer was only present at the beginning or end of the block it would not be affected. To overcome this. the

weight calculation is repeated with the equaliser output from the first pass used to build the reference for the unknown data bits [ l l ] . This is illustrated in Fig. 3. The scheme is similar to decision directed adaptive beamforming except that the training sequence is used to give a good start point and the weights are calcu- lated over a burst and reapplied to the same burst, rather than be updated from symbol to symbol. Digital processing is required to store the burst and, of course, solve eqn. 1. More than one iteration can be per- formed, that is steps (iii) and (iv) repeated. In the simu- lations presented in Section 3 the iteration controller allowed the calculation to repeat until the equaliser returned the same data bits twice or a limit of 20 was reached. In practice the simulation required about three or four iterations.

So far it has been assumed that the transmitted train- ing sequence and the reference are synchronised. In practice, the adaptive time alignment feature of GSM will maintain this quite closely. Initially the weight cal- culation can be performed for several possible reference delays. For references not in synchronisation the wanted signal will also tend to be cancelled and the weights will all tend to zero. Thus a test of the magni- tude of the weight vectors (W,,~~W,,) for the different ref- erence delays should reveal the correct synchronisation.

This algorithm must operate in a mobile communica- tions environment. In minimising the mean square error between the array output and the reference wave- form short delay (compared with the symbol interval) wanted signal multipath will be combined in phase as for a maximal ratio diversity combiner. Longer delay multipath and interference which do not correlate with the constructed reference will be cancelled. The solu- tion required to do this will, of course, be changing at a rate dependent on the speed of the mobiles, the speed of any moving scatterers and the propagation environ- ment. At 900MHz and for a mobile moving at 50km/h the maximum change of phase between two paths is 16.4” over a GSM burst. A high level of cancellation will not be maintained using a single degree of freedom

troining sequence

lterotion control

f I I- t

Fig. 3 Aduptive untennu,fi,r GSM

306 IEE PI-oc.-Conzmiin, Vol. 143, N o 5. October 1996

_~

I interfering GSM signal 1

Fig. 4 Simdution .szructuw SFG = speech frame gcncrator AA = adaptive array TCI = traffic channel inlerleavei- VE = Vim-bi cqualiser/demoduiatoi- BF = burst foriiiatter TCD = tral'lic channel dcinterleaver GM = GSM iiiodulalor CD = channel decoder PC = propagation channel BM = BER meaaurcmcnl C'E = channel estimator

with this rate of change, however this figure is a maxi- mum and is likely to be smaller in practice. Also, high levels of cancellation are generally not required thus applying a single weight vector over the whole burst should not limit performance.

3 Simulation resiults

The architecture of the simulation used to validate these ideas is shown in Fig. 4. Four interfering GSM signals and one wanted GSM signal arle simulated. For each signal, 20ms of random speech d.ata is generated (260 bits). These are split into class 1 and class 2 bits according to their importance to the final speech qual- ity. The class 1 bits are convolutionally coded to bring thc total speech frame length to 456 'bits. The speech frame is then interleaved with a previolus speech frame to produce four 116 bit data blocks per frame. These data blocks, together with the training sequence, start bits, etc., form the normal GSM burs1;s (Fig. 2). Each burst is then gaussi.an-MSK modulat'ed according to the GSM format.

Table 1: Six-tap typiical urban propagation model for GSM

Tap Delay (ps) Power (dB)

1 0.0 -3

2 0.2 0 3 0.5 -2

4 1.6 -6

5 2.3 -a 6 5.0 -1 0

~~~ ~

The simulation assumes a centre frequency of 900MHz. The propagation channel used is based on the six-tap TU50 (typical urban, mobile moving at 50kmih) model [3] (Table 1). This model was derived for a single antenna. The correlation of the fading in

IEE Proc.-C'oinmuri.. Vol. 143, No. 5, Octohei. 1996

the different base station antennas will depend on the angular distribution of the multipath and the separa- tion of the antennas. If most of the scattering occurs in the local vicinity of the mobjde the signal will arrive at the base station within a narrow range of angle and the base station antennas will need to be well separated for independent fading and the corresponding diversity gain [12]. The delay spreads of the urban environment suggest wider scattering and the signal arriving over a wide range of angle. A measured example of this is pre- sented in [9].

To generate realistic fading, in the base station anten- nas the following steps are taken. For tap 1, 20 scatter- ers are randomly located within a circular area around the mobile. The size of this tap 1 area presents a 2" angular spread to the base station. The simulation of 20 equal scatterers with random locations generates fading with approximately Rayleigh amplitude distribu- tion and uniform phase distribution. For each of the remaining taps, 20 scatterers are randomly placed in elliptical areas that have the base station and the mobile as their foci. The boundaries of the ellipses are determined by the tap delays and represent the maxi- mum extra path length for that delay. For the early taps the multipath will be confined to a narrow spread in angle and the fading in each of the base station antennas will be highly correlated. For the later taps the multipath will be more spread in direction of arrival with less correlated fading. The scatterer boca- tions are recalculated from burst to burst.

The distribution of mobiles used is shown in Fig. 5. Each side of the hexagonal structure is lkm long. A trisectored base station is assumed with frequency reuse at each base station but not in each sector. Given the gains of an adaptive antenna and a careful scheduling of channels so that the base station in not transmitting at high and low powers to different sectors at the same frequency and time then frequency reuse in each sector may be possible. An alternative approach to Fig. 5 is for the base stations to operate without sectorisation

307

and with frequency reuse in each cell. The spectrum is no longer constrained to different sectors of the cell but the adaptive antenna is now subjected to interference from all the neighbouring base stations rather than a restricted sector.

1 /

/ /

/

Fig. 5 Mohik distriliution + wantcd mobile 4+ interleriiig mobile 0 neighbouring hasc station

The purpose of Fig. 5 is to derive an example angu- lar distribution of the different signals and their multip- aths. In practice, of course, the mobiles may range throughout their cells. The signals will arrive at the base station with powers dependent on the path loss and the shadowing associated with the particular loca- tions of the mobiles. Since it is not the intention here to perform a complete characterisation of adaptive anten- nas for cellular radio, it is aimed to show the potential of the technique; the example of Fig. 5 is chosen together with the case that the signals from each mobile are received with the same power (after averag- ing short term fading). This gives a signal to total cochannel interference ratio of -6dB. Since the four nearest-neighbour interfering mobiles are generally fur- ther from the base station and all four may not always be present this represents a pessimistic, though not impossible, scenario. Because of this, on average, the adaptive antenna should perform better than the simu- lation results presented here predict. On the other hand, weak interference from more distant cells and adjacent channels will also occur which will degrade the results to some extent.

Fig. 6 compares the adaptive antenna scheme of Section 2, that is just having knowledge of the training sequence and combining this with the decisions of the equaliser, with having prior knowledge of the complete burst. Fig. 6 also plots the BER if the final weights used are those calculated from the training sequence alone. In other words, no iterations are performed using the decisions of the equaliser. Fig. 6 is calculated for an eight-element uniform linear array (wavelength spacing) arranged so that broadside points towards the centre of its cell. The bit error rate is measured for the class 2 bits, i.e. no convolutional coding. Erased frames are not rejected from the BER calculation. Having prior knowledge of just the training sequence and com- bining this with the equaliser decisions degrades per- formance by only 3 to 4dB, compared to prior knowledge of the entire burst. The iterative process (steps iii and iv) achieves little at low signal-to-noise

30x

ratio but at higher signal-to-noise ratio, when less of the equaliser decisions are in error, it improves per- formance by about 3dB.

-3 1 5 9 13 17 Eb/No, dB

Fig. 6 U Training scquence only h Training scquence + equaliser output c Prior knowledge of entire burst

Cuin~~~rrison qfaduptivp Linrmin ixlgorithm with perf2ct reference

1

0.1

a" 001

0.001

0.0001 - 3 1 5 9 13 17

E, I No, dB

Fig.7 Vurying the sire of 'arrl; LI 1 element h 4 elcniciit c 6 elciiieiit d 8 element c 10 element

Fig. 7 shows the effect of varying the number of ele- ments employed in the array. Everything else is kept the same. Cancelling four interferers and maximising one signal requires five degrees of freedom and there- fore a five-element array. However, some of the path delays are long compared with the symbol interval, thus some of the multipath is decorrelated from the ref- erence and appears as extra interference. Thus the per- formance of the six-element array is still limited. Adding further elements significantly improves per- formance. In practice this scenario, four interferers of equal power to the wanted, may be pessimistic. Most of the time the wanted signal may be the strongest or, at least, fewer than four interferers will have compara- ble power. Thus arrays of about six elements may pro- vide sufficient mean performance.

To illustrate the improvement provided by an adap- tive antenna, Fig. 8 compares a ten element array with a single antenna, both with and without interference. The adaptive antenna not only restores the perform- ance lost owing to the presence of the cochannel inter- ference, but an even lower error rate results compared

IEE Proc.-Cominmi., Vu1 143, No. 5 , Octoh~.r 1996

with a single antenna without any interference. This is due to the gain from coherently corrtbiiiing (for the wanted signal) a number of antenna outputs. For this example a ten element array would allow the mobile to transmit with approximately lOdB less power, com- pared with a single-antenna base station, and still be received with a sufficient bit error rate. This lowers the interferencc for other base stations and has additional benefits such as prolonging the battery life of handheld mobiles.

I

0.1

lY 0 0 1

0.001

0.0001 - 3 1 5 9 13 17

Eb/ No, dB

Fig. 8 ( i Current system 4 interferers h Current system 110 interferers c 10 clement adaptive beamfnrrner 4 inlerfei-crs

Cbii?pui.ison with current system

Multipaths of the wanted signal that are sufficiently delayed so that they do not correlate with the reference will bc cancelled. This is a waste of wanted signal energy. These paths can be exploited if, instead of sim- ple amplitude and phase wcighting of the antennas, tapped delay-line filters are employed. The adaptive antenna is then ablle to effectively present different delays in different directions and optimally combine the delayed paths. Consider the addition (of tapped delay- line processing with L taps. Let X(i) represent the matrix of element signals from the ith taps. Let the weight vector for tapped delay-line processing be denoted w’ (now of length NL) . This is, given by

Mxf,, = X”X’ X’ = [XjO) X(1) .. x;(L - l)]

and r’ is simply the reference signal ‘(r) delayed by a fixed number of sampling intervals. In the simulation an integer value close to Li2 was used to centre the impulse response in the middle of the tapped delay lines.

Fig. 9 gives an example of how the proposed algo- rithm performs with tapped delay lines for the case of applying three taps during the second and subsequent iterations (steps iii and iv). There are insufficient sam- ples to apply tapped delay lines during the first, train- ing sequence only, iteration. Fig. 9 was calculated for an eight-element linear array. The application of these simple tapped delay lines and the proposed algorithm has clearly reduced the error rate further, particularly at high signal-to-noise ratio.

IEE Proc.-C‘oii?i?iiin , Vol. 14.r. N o 5 , Ortobrr 1990

1

0 1

a” 0 01

0 001

0 0001 -3 1 5 9 13 17

Eb/ No, dB

Fig. 9 L1 I lap h 3 taps

Elfkt o/ u.ving tripped delay lines

I t I

fixed

beamforme beamformer

Ld Ls l l transmit beam select i!LIzz+ 1

signal received from mobile

Fig. 10 Artding fixed be~i~cmi/oriiirri,~/or don nlinh

signal to transmit to mobile

4 The downlink

Consider next how the downlink can be improved. In principle, the weights calculated for the uplink are also ideal for the downlink since they would maximise the signal received by the wanted mobile and minimise that received by ‘interfering’ mobiles. In practice the fre- quency shift and the time delay between the two links is likely to make these weights less effective. In the absence of multipath, maximising the wanted sigrial is equivalent to forming a beam in its direction. At the base station multipath will not be absent but it is likely to be confined to a narrow angular sector, at leas8t for the paths with the shorter delays. Thus forming fixed (not adaptive) beams for transmit, although not opti- mum, should increase the signal received by the wanted mobile and reduce (to the beam’s sidelobe level) that transmitted towards other imobiles. For longer delay paths ihat are spread in angle, the signal energy trans- mitted in these directions will also be reduced to the beam’s sidelobe level which will reduce the multipath spread at the mobile. A possible scheme to implement this idea is illustrated in Fig. 10. For the uplink burst, as well as optimising the received signal, the adaptive antenna also calculates the siignals that are received in a

309

number of overlapping fixed receive beams. These are then correlated with the adaptive antenna’s best esti- mate of the wanted waveform to determine which receive beam contains the most wanted (not interfer- ence) power. This beam is then selected for the down- link. The transmit beamforming can be performed at RF.

Fixed beamforming for the downlink will not realise the same level of SINR improvement that adaptive beamforming achieves for the uplink. Thus fixed beam- forming on its own is unlikely, unless a very large transmit array is employed, to enable frequency reuse in every cell for the downlink. A combination of fixed beamforming and other techniques are required. One such technique is presented in [13]. In GSM a Viterbi equaliser is used to resolve the intersymbol interference introduced by the delay spread of the channel and the modulation format used. Knowledge of a training sequence in the centre of each data burst is used by the equaliser to estimate the impulse response of the clian- nel. Nearby interfering GSM signals will have a differ- ent training sequence and can, consequently, also be demodulated by the Viterbi equaliser. Their contribu- tion to the demodulation of the wanted signal can then be removed. This leads to the concept of Fig. 1 1 where an adaptive antenna at the base station is combined with handsets incorporating more sophisticated equalis- ers to achieve the full capacity gains on both the up and down links. Initially, of course, most handsets would not incorporate this and would need to be allo- cated channels with lower levels of cochannel interfer- ence. The carrier with the GSM broadcast control channel would need to keep its current reuse pattern anyway since beamforming cannot be applied to this without disrupting the signal strength measurements made by the mobiles for handover.

v. * 1

‘ Y

Y Y

interference

station

Fig. 11 Concep.pr,fbv cavvier uewe in every cell

Also gains may be realised by the careful scheduling of channels so that, for example, closely located mobiles connected to differing base stations do not share simultaneous time slots. In practice no one tech- nique is likely to enable loo%, reuse of carriers. A com- bination is required which needs to be integrated with the operation of the network and which can be deployed in an evolutionary, not revolutionary, manner as capacity demands increase.

5 Conclusions

This paper has considered how an adaptive antenna can be implemented in a GSM mobile radio network.

In particular, an algorithm suitable for urban GSM has been derived and its performance illustrated for the urban multipath environment. It is in these areas that capacity is most likely to be in demand. The simula- tions have shown, at least for the scenario considered, that adaptive antennas enable frequency reuse at every base station for the uplink. The capacity of a system currently using a 3/9 reuse pattern (three trisector sites per cluster) could almost be tripled if, for example, adaptive antennas and a 1/3 reuse pattern were employed. We have also proposed a solution to the problem of achieving comparable gains on the down- link. Further work. of course, needs to be done. The performancc of the adaptive antenna needs to be char- acterised for a range of mobile scenarios and network configurations, not just the example presented, as does the performance OF the proposed downlink ideas. How- ever the results presented clearly demonstrate that large capacity gains can be obtained from second-generation TDMA cellular networks using adaptive antenna tech- niques.

6 Acknowledgment

The tapped delay line work has been carried out with the support of DRA Malvern.

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310 IEE Pioc.-Conzmun., Vol. 143, No 5. Octoher 1996