Use of blind equalization for teletext broadcast systems

Use of Blind Equalization For Teletext Broadcast Systems

JEYHAN KARAOGUZ, STUDENT MEMBER, IEEE, AND, SASAN H. ARDALAN, MEMBER, IEEE

Abstruct - Severe inter-symbol-interference (ISI) due to multipath fading broadcast channels requires the equalmation of digital teletext signals for reliable operation. Until now, the equalisation of the teletext signals has been achieved by conventional adaptive equalisation. However, these equalisers need a training signal which is sent during the PO- tentiaI data transmission time. Also, conventional adaptive equalisers are very poor in tracking changing channel characteristics which are often encountered in feding multipath environments. This paper suggests the use of blind adaptive equalisation, which removes the mentioned drawbacks of the conventional adaptive equalisation. The paper also deals with the compatibility of the proposed blind equalmer with the existing teletext system standards and cost-effectiveness issues. Computer simulations demonstrate substantial improvements in the bit-error-rates for the received teletext data in a multipath transmission medium

I. INTRODUCTION

Teletext is a new television service which has generated a great deal of interest in the last few years. Most of the system parameters such as data rates, pulse shapes and modulation schemes have been greatly standardized over the past years. However, there is still ongoing research and development in this area. Teletext signals are digital signals; therefore, in the presence of excess noise and, more importantly, inter-symbol-interference (ISI) due to multipath fading channels, the received signal is often detected with many errors. To achieve low bit-error-rates (BER) and a reliable system a channel equalizer is needed. The design of equalizers has been one of the important research areas in teletext systems. This paper deals with the equalization of degraded teletext signals. However, unlike previous works [l, 2,3] we sug- gest the blind adaptive equalization of teletext signals rather than conventional adaptive equalization. Com- mon to all of the previous adaptive equalization methods is the need for an initial training period in which a particular data sequence, known and available in proper synchronization at the receiver, is transmitted. The training duration varies depending on the conver-

a

I

! 1

.4

gence rate of the algorithm being used for adaptive equalization. Practically,' LMS type adaptive equalizers have been used for their cost-effectiveness and easy implementation. However, the training interval for such equalizers takes a significant amount of transmission time at the expense of potential data transmission time for the teletext systems. This inefficiency leads to long waiting times at the receiver. There- fore, fast convergence rates and minimum delay together with reliable bit-error-rates are the paramount goals in designing teletext systems. Another source of long waiting times between consecutive teletext pages is due to the fact that teletext data transmission can only take place during some of the unused vertical blanking interval (VBI) lines. However, the VBI is also used to send other synchroqization and mostly equalization pulses which are used in conventional adaptive equalization. These bverhead pulses, which are transmitted with the digitally coded data, can take nearly half of the vertical blanking interval [4]. Also, the teletext transmitter has to wait 262.5 lines for every VBI to send two lines of teletext data. 'Remedies to these drawbacks are twofold: '(1) the training signals should be eliminated if possi- ble; and (2) the teletext transmission should not be restricted only to the VBI. A solution to the latter problem was suggested in [5] .

The use of blind equalization removes the system inefficiencies mentioned above. The blind adaptive equalizer described in Section IV does not require the use of any training signal. Also, the proposed blind equalization algorithm requires that the teletext signal be 2-Phase PSK or QPSK modulated or, more generally be phase or FM modulated. The blind equalizer uses some invariant properties of the phase modulation technique as described in more detail in Section IV. This type of equalizer is also called a property restoral blind equalizer [6]. This is because it tries to restore the received signals based on their known invariant properties such as constant modulus or magnitude. Section II(B) discusses the use and advantages of 2-Phase PSK and QPSK modulation schemes in teletext data transmission and also their

The authors are with the Center for Communications and Sig- nal Processing, Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC, 27695-7911.

This work W u supported by the Cents for Communications and compatibility with the casting TV systems. Signal Processing. 0018-9316/91~600-001.~ 0 1991 IEEE

IEm TRANSACTTONS ON BROADCASTING, VOL. 37, NO. 2, JUNE 1991 45

Another requirement for radio or TV broadcast channel equalizers is that they be able to track the time-varying fading characteristics of typical channels. Frequently changing multipath fading channel characteristics render conventional adaptive filters in- effective since the adaptation is stopped after the training period [7]. This requires waiting for retransmis- sion of the training sequence which, in turn, causes longer delays at the receiver. Therefore, the use of blind equalization for such broadcast channels is very appropriate since it tracks the channel changes con- tinuously as it restores the invariant properties of the received degraded signal without any interruption.

Any new system modification to current television systems should be easy and cheap to implement (cost- effective) and also, very importantly, should be compatible with the already existing standards to be acceptable in the mass market TV industry. There- fore, minimal change in the current television system should be a goal when a new system is proposed. The use of blind equalization is not only cost-effective and compatible with standard TV systems but it also eliminates some of the extra circuitry in existing TV sets for generating the equalizer reference or training signal, the so-called ghost cancelling reference signal. In Section II(B) and I11 more discussion is presented about the cost-effectiveness and compatibility issues of blind equalizers with existing teletext broadcast systems.

Section II(C) explains how the received teletext data signal is pre-processed before the blind equalizer. Section I11 describes where the blind equalizer and its supplementary blocks would be located in the existing TV set. Section IV provides some background about blind equalization and presents the equalization algorithm with some remarks about its convergence behavior. Finally, Section V provides computer simulation results which indicate substantial improvements in the received teletext signal.

11. BACKGROUND A . Reception of Teletext Under Multipath Conditions

The propagation of TV signals over different paths of unequal lengths from the transmitting station to the receiver is referred to as multipath propagation. The multipath problem results in ghosts superimposed on the main TV picture and causes severe IS1 for the teletext messages. Since digital teletext data is transmitted on standard analog television systems, the teletext data reception also suffers from the same degradations, resulting in poor bit error rates. The

analog TV system is not well suited for data transmission. Therefore, special attention must be given to reducing the BER of the detected teletext signal. This paper examines the blind equalization of teletext data that have been subjected to multipath distortion. The primary goal of the blind equalization will be to reduce the effects of channel distortions due to multipath channels so that the receiver’s de- cisions become more reliable. The IS1 due to multipath is much more important and effective than noise for data transmission. Therefore, the performance of the blind equalizer will be evaluated by signal-to- interference-and-noise (SINR) versus BER curves in Section V.

A multipath channel can be characterized by an impulse response given as

where a k and Q are the effective path gain and path delay for path k, respectively. For a practical channel,

is a complex number with magnitude less than one and Tk, can take any arbitrary real and positive value.

B. Modulation Format

In current TV systems, the teletext input message is in the form of narrow nonreturn-to-zero (NRZ) pulses which are then shaped by a baseband teletext transmit filter. The shaped pulses constituting the message signal are amplitude modulated and filtered by a vestigial side-band filter [8]. Digitally coded information is transmitted during the vertical blanking interval of the analog television signal. However, the existing TV system has been optimized to transmit analog picture information. Common impairments such as nonlinear phase characteristics of the IF filter at the TV receiver, multipath propagation, echoes in TV cable systems and distortions due to envelope detection of the vestigial signal which have relatively tolerable effects on the picture signal will significantly increase the bit error rate of the detected teletext data signal [9]. For these reasons some alternative modulation schemes for transmitting digital information should be considered for better performance. One proposed alternative was a new teletext channel [5] which uses 2-Phase PSK or QPSK modulation for the transmission of the digital teletext message. For the 2-Phase PSK case, a sinusoid with the same phase as the color burst is transmitted when transmitting a ‘1’ and a sinusoid 180” out of phase when transmitting a ‘-1’. Alternatively, two bits of information can be transmitted by using 4-Phase quadrature modulated

46 IEEE TRANSACTIONS ON BROADCASTING, VOL. 37, NO. 2, JUNE 1991

used for QPSK modulation. The 2-Phase PSK modulation can be realized by mixing the baseband pulses with the modulating carrier already being used for

(B-X) -tB-X) , tWr) the analog modulation. Besides the fact that the introduction of the QPSK or %-Phase PSK schemes to teletext systems is very compatible and cost-effective, the constant magnitude property of their constellations allows the use of blind equalization which elim-

(R-X)

* i,,.-----/,,+ ~

% ....____.. 8'

- (8-r) q ~

*. ..._. _.... J

7Pph.Scmx -(R-X)

QPSK

inates the use of training signals, and this increases the useful data transmission time. Therefore, over- all substantial improvement is achieved in the delays

Figure 1: Constant Radius Property of QPSK and 2-Phase PSK Con- stellations

PSK, namely, by the QPSK scheme. Both methods are very well suited for the blind equalization application which uses the a priori information about their constellations. As shown in Figure 1 both constellations have a constant radius. Section IV explains how this a priori information is used for blind equalization purposes.

The QPSK modulation doubles the data rate ca- pability, which significantly decreases the waiting times between consecutive teletext pages at the receiver. In addition, the QPSK modulation scheme is very efficient in using the allocated TV signal bandwidth. The teletext data are sent as complex pairs in the same signal bandwidth which is very convenient for practical compatibility reasons. In [SI, Rzeszewski stated elaborately the advantages and cost-effectiveness of using 2-Phase PSK and QPSK type modulations in teletext systems rather than conventional methods. Also, he explained the compatibility with the current color TV systems, as the transmission of the 2-Phase PSK or QPSK signals would take place as a time division multiplexing (TDM) process in the front and back porches of the color burst signal. The TDM removes the requirement for re- stricting the teletext data transmission only to the VBI. Instead of waiting 262.5 horizontal scanning lines for each VBI, the teletext data can be placed at the beginning of each horizontal scanning line with the color burst signal. The proposed blind equalization algorithm is very effective with both 2-Phase PSK and QPSK type signals according to the results presented in Section V. QPSK is preferred over PSK because it will transmit twice the data rate of the simple PSK approach. However, the 2-Phase PSK approach promises better immunity to inevitable tol- erances that occur in a system intended to service the mass market. The introduction of either of these modulation techniques results in a minimal change to the current system [5]. The already existing quadrature modulator for the color information can also be

and the reliability of the received teletext signal at the receiver.

C. Digital Quadrature Processing For Blind Equalizer

The blind equalization algorithm outlined in Sec- tion IV can process the degraded received signal either in the passband or in the baseband. The only requirement in both cases is that the equalizer should be provided with the modulus information of the signal. Obtaining the modulus information in the passband is quite different from obtaining it in the baseband. A total of three methods will be described. The blind equalizer uses a complex finite impulse response (FIR) filter to equalize the channel. The ad- vantage of complex processing is that the envelope of the received signal can be ascertained on a sample-by- sample basis because the in-phase and quadrature- phase components are in complex pairs. Therefore, the received input signal is assumed to be available in complex digital quadrature form.

Obtaining the quadrature (sometimes called andyt ic) signal representation can be achieved in one of the following three ways [lo, 111,

1) Complex down-conversion followed by low-pass filtering,

2) Quadrature bandpass sampling together with the use of digital Hilbert transform,

3) Passband equalization of the received signal, while the modulus information is obtained by means of a Hilbert transformer.

This section gives a detailed explanation of each method in terms of its practicality and advantages and disadvantages. The first method, complex down- conversion followed by low-pass filtering, is the conventional method of obtaining the complex quadra ture low-pass signal. This method, shown in Figure 2, involves a pair of analog multipliers or mixer in which the received signal at some convenient center frequency is multiplied by a reference signal of frequency equal to the center frequency. Then the

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cos 2Tr f, t I

r = \ + j a t=nT

Figure 2: Conventional Method to Obtain Complex Lowpass Signal

COS 2r(f,-B)t

I

Figure 3: Quadrature Bandpass Sampling Method to Obtain Complex Lowpass Signal

output is low-pass filtered. A 90" phase shift is in- cluded in one of the channels in either the signal or reference path so that the resulting complex low-pass outputs are in quadrature. At that point the magnitude of each complex pair can be calculated and used by the blind equalization algorithm. This will be elaborated upon in Section IV. During the complex down-conversion process the reference frequency input should be phase-locked with the center frequency of the received signal to obtain the correct in-phase and quadrature phase low-pass signals. This requires some complex carrier recovery circuitry at the re-' ceiver, which is a disadvantage.

The second method uses quadrature bandpass sampling together with a Hilbert transform to obtain the complex lowpass signal and uses the periodic repe- tition property of the discrete Fourier transform in such a way that the lowpass complex signal is obtained with little computational effort. As shown in Figure 3, first the received bandpass signal is mixed down to an intermediate frequency, f 1 ~ = B. The high frequency components which result from mixing are removed by a bandpass filter. The resulting signal spectrum is shown in Figure 4(b). Note that the signal is not symmetric around the center frequency therefore it is a complex signal at baseband. The only reason for the down-conversion to IF is to work with relatively smaller sampling rates which are advanta- geous for practical reasons. As indicated in Figure

... , , ... 4 '. W W

t

Figure 4: (a) Received Bandpass Signal (b) After Conversion to IF (e) Sampled IF Spectrum (d) Pre-envelope e€ Sampled IF Spectrum (e) Discrete Complex Lowpass' Signal


I 4L e-j 2 IrfsT

I I & PASSBAND r(t) 0 t=nT EQUALIZER DECISION

Figure 5: Passband Blind Equalization Method

4(a), the signal bandwidth is B . Now sampling is performed on this bandpass signal to produce a sequence of real-valued samples. The sampling rate can be chosen as 4fIF = 4B. Clearly this sampling rate is adequate to avoid aliasing. After the A/D converter the signal is in the discrete domain; therefore, the spectrum is periodic with period 27r, where 21r corresponds to the sampling frequency 4B. At this stage the signal is still real because the spectrum is symmetric around DC. The sampled bandpass spectrum is illustrated in Figure 4(c). Now the trick is to re- move the negative portion Qf the frequency spectrum to obtain a complex signal from which the analytic lowpass signal can be extracted after some simple ma- nipulations. This is equivalent to calculating the pre- envelope of the signal [12]. The pre-envelope is the sum of the signal with its Hilbert transform. Since the Hilbert transform is a 90° phase shifter, after the summation the negative frequency components can- cel each other. Once we have the pre-envelope signal, which is complex, as shown in two branches in Figure 3, then the sampling rate can be decimated. The decimation in sampling rate can be done because the complex bandpass signal in Figure 4(d) can be sampled at or above the Nyquist rate determined by the signal bandwidth, which is B, and not by the highest frequency component of the bandpass signal, which is f l ~ + B / 2 . Therefore as seen in Figure 3, every fourth sample is kept while others are discarded after the Hilbert transformer. This is equivalent to decimating the sampling rate by four. After the decimation in sampling rate, the complex low-pass quadrature signal is obtained as shown in Figure 4(e). As explained above this method avoids carrier recovery and some limitations of the analog multiplier method because the signal is not down-converted to baseband all the way from passband. This technique is better suited for teletext applications because transmitted data is in digital form after A/D conversion of the received bandpass signal. This method is also very advan- tageous for its compatibility with the all digital TV receiver applications [13, 141.

The third method unlike first two, requires equal-

ization in the passband as shown in Figure 5 . There- fore the demodulation process follows the equalization. The modulus information of the received RF signal is extracted by a Hilbert transform block. Let's show how this is achieved. Assume that the received QPSK signal is

(2) T ( t ) = fiC0sWctC~=-, Re{ak}g(t - kT)

- f i s h w,t CK-, Im{Uk}g(t - kT) where g( t ) is the pulse shaping filter. The Hilbert transform of ~ ( t ) can easily be found as

(3) $ ( I ! ) = f i s h W J ! ~ ~ - , Re{ak}g(t - kT)

+fi cos W c t Cfp=-, Im{ab}g(t - kT) where +(t ) is the Hilbert transform of ~ ( t ) . One can verify that the sum of squares of r ( t ) and +(t ) at the optimum sampling instants, t = kT, for the pulse- shape g ( t ) is,

r 2 ( k ~ ) + f 2 ( k ~ ) = Re2{ar} + Im2{ak} (4)

Equation (4) is just the radius of the constellation at each sampling instant. Note that the modulus assess- ments are done at the passband rather than at the baseband. Therefore, the equalization is realized at the passband as well.

111. SYSTEM DESCRIPTION

A block diagram of the system is depicted in Fig- ure 6. The Blind Equalization Unit is an all digital sub-system located between the teletext data demodulator and the IF front-end. The Digital Quadra- ture fiansfomzer (DQT) can be designed with any one of the three methods described in Section II(C). The Cam'er Recovery block is optional depending on the method used in obtaining the complex quadrature signal. After obtaining the in-phase and quadrature components of the received signal by the DQT, the blind equalizer processes the distorted complex signal before the Teletezt Data Processor, which includes all processing functions between the blind equalizer and the Display unit. The Blind Equalization Unit will be elaborated upon in the next section. The Synchronization Processor (SP) is necessary for some

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DIGITAL - i r(k) COMPLEX . QUADRATURE > FIR TRANSFOF34ER 7 )' FILTER

RP

I

I

:

Figure 6: Syetem Block Diagram

A A

I I

I

I

Vl COEFFICIENT ERROR

c. VECTOR

BANDPASS INPUT

*

BLIND ADAPTIVE EQUALIZER .............................................

DETERMINATION : UPDATE - c

DISPLAY

PROCESSOR

Figure 7: Blind Equalizer

cases because the in-phase and quadrature components may be obtained by the DQT (method 2 in Section II(C)) at different time instants 1131. The SP removes this misalignment between the I and Q samples before the blind equalizer.

Due to its all-digital structure the integration of the blind equalizer with the digital TV sets is relatively easy. In an open-architecture expandable digital receiver chassis, the blind equalizer may be in-

dation types such as QPSK and 8-Phase PSK but not for 2-phase PSK, which is a one-dimensional scheme. In this paper, the algorithm is modified to incorpo- rate the equalization of the one-dimensional 2-Phase PSK modulation scheme. For this case the algorithm processes a real data sequence rather than complex. Since the system is no longer two dimensional, the complex FIR filter is reduced to one with real taps. The performance function aesessments are done at the

cluded as an optional user installable board.-For the existing conventional TV sets, it may be added in the form of an external box with analog inputs and digital outputs.

slicing, i.e., the decision instants, rather than for each complex sample pair for the QPSK case or for each sample in the 2-phase PSK case. For data transmission it is only required that distortions be removed at the decision instants (maximum eye opening), which are also called the optimum sampling instants, while in the FM case no phase distortions are tolerable.

The following section gives more insight about the blind equalization algorithm.

IV. BLIND EQUALIZATION ALGORITHM

A . General

The blind equalization algorithm presented in this section is based on the combination of ideas presented in [15, 161. In [16] the property restoral blind equalization algorithm was applied to an FM modulated signal. The amplitude modulation on the FM signal caused by channel distortion due to the multipath problem was removed by the so-called constant modulus algorithm. In [15] Godard derived a self-recovering adaptive equalization algorithm for two-dimensional data communication systems. He also suggested some applications and provided limited amount of simulations for two dimensional mod-

B. System Description

In Figure 7 , r ( k ) is a quadrature sampled bandpass or baseband QPSK modulated teletext signal which can be obtained by any one of the three methods explained in Section II(C). This is equivalent to replacing the Digital Quadrature Trans f mmer block with either Figure 2 or Figure 3 for the baseband equalization case or with Figure 5 for the passband equalization case. Note that the passband equalizer in

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Figure 5 should be replaced with the blind adaptive equalizer in Figure 7. For the 2-Phase PSK modulation, quadrature sampling is not needed since the data is one-dimensional. If passband equalization is desired, the demodulation takes place after the equalization [16]. Otherwise, the blind equalizer processes the baseband analytic signal and removes the distortion. The derivation of the algorithm presented in this section is general and applies to both passband and baseband equalizations.

The distorted analytic input signal, ~ ( k ) , is applied to a finite impulse response filter whose complex coefficients are iteratively adjusted by a simple gradient steepest descent algorithm to minimize the presence of the unwanted signal components at the filter output. Equivalently, for the 2-Phase PSK case the same process is repeated with a real coefficient FIR filter. However, for the sake of generality, the derivation of the algorithm will be given using complex arithmetic.

Given the discrete-time, degraded complex input waveform r(k), the complex filter output s(k) may be written in vector notation as,

5(k) = rt(k)w(k) (5)

where r(k) is a window of data,

r(k) = [~(k) ~ ( k - 1) . . . T ( k - N + 1)It. (6)

N is the number of taps in the complex FIR filter. The adjustable complex FIR tap weights are given by the vector,

w(k) = [WO@) W l ( k ) * * * W N - l ( k ) l t . ( 7 )

The index k indicates that the vector coefficients are updated at each sampling instant. However, this index can be held to any arbitrary number of samples depending on how fast it is desired to update the al- gorit hm .

Since the blind equalizer does not have a training signal, the performance function to be optimized by the steepest descent rule should be only a function of input signal properties and the distorted received signal [16]. In our case the objective is to restore s(k) to a constant modulus signal if it is at passband or to a constant magnitude constellation if it is at baseband. Keeping these facts in mind the performance, or so- called cost function to be minimized is found to be

J@) = E(ls (k)JP - Mp)2 (8) where E ( - ) is the expected value operator. The role of constant Mp will be explained shortly. The minimization of J@) leads to minimization of the IS1 caused

by multipath distortion [15]. Note that the performance function (8) is evaluated and the filter taps are updated at the decision, i.e., optimum sampling instances. For mathematical and practical reasons the parameter p is restricted to '1' or '2' [17]. In our case p was chosen to be '2', therefore, (8) becomes,

J = E(ls(k)12 - M ) 2 (9) M is chosen as the constant amplitude of the signal for the equalization of constant modulus bandpass signals such as FM and as the radius of the constellation for the PSK and QPSK modulation schemes. Intuitively minimization of (9) restores the s ( k ) to a magnitude or modulus of M because deviations of s(k) from M are penalized by the adaptive algorithm as will be explained in the next section.

C. Algorithm

The equalizer tap gains are adjusted according to the classical gradient steepest descent algorithm:

~ ( k + 1) = ~ ( k ) - P v w Jk (10)

where ~ ( k ) is the set of complex FIR filter coefficients used to generate output s(k), 1.1 is the step size or so- called learning rate, and vw is the gradient operator with respect to the elements of the filter coefficient vector w.

Using complex matrix calculus, it can be shown that the gradient of J with respect to the weight vector g is given by

v W J = 2E{(/s(k)l2 - M ) - ~ W [ ~ % * T ' U ] }

= 4E{(ls(k)(2 - M ) -r*(k) * r"k)w(k)} = 4 E { ( l ~ ( k ) 1 ~ - M ) -~(k)r*(k)}

(11) where h and * indicate the conjugate transpose and conjugate operations respectively. The update equation is simplified by replacing the expected value with the instantaneous gradient yielding,

vw J = [ls(k)I2 - MI - ~(k)r*(k). (12)

Also the factor 4 in (11) is removed assuming that the performance function J in (8) is scaled by 4. Substi- tuting equation (12) into (10) yields the desired algorithm,

~ ( k + 1) = ~ ( k ) - ~ { [ l s ( k ) \ ~ - M]s(k ) r* (k ) } . (13)

To simplify the notation, we define e ( k ) as

E(k) {(s(k)12 - M } s ( k ) . (14)

Then the updating algorithm can be compactly writ-

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QUADRATURE MODULATOR

NOISE ? DEMODULATOR

TELETEXT PULSE 2-PHASE PSK VSB XMIT SHAPING MODULATOR FILTER

NOISE =-+ BLIND

(b) EQUALIZER DEMODULATOR

Figure 8: (a) Teletext System with QPSK Modulation Scheme (b) with 2-Phase PSK Scheme

ten as - w(k + 1) = w(k) - pe(k)r*(k) (15)

The update formula in (15) is symbolically the same as the complex LMS algorithm [18]. In the conventional complex LMS algorithm E(k) = d(k) - s(k) where d(k) is the desired signal - namely, the training signal. In blind equalization, there is no training signal. Rather than having a waveform replica or template d(k) in (14), the a priori information that s(k) should have a constant modulus for the passband equalization or have a constant magnitude constellation for the baseband equalization, is employed. Because of its similarity with the well-known LMS algorithm the application and implementation of this algorithm is very practical.

D. Convergence Properties

The convergence duration of the blind equalizer is important because there is only a limited ambunt of time allocated for the data transmission during the VBI. In [15, 161 the convergence time is in the order of milliseconds, which is not acceptable for teletext applications. The reason for slow convergence is due to the very low step size used (2-l') and the initialization of the FIR filter taps. An adaptation constant of 0.009 was used in our simulations. Very rapid convergence (in the order of micro seconds) is achieved for the teletext system simulated in Section

V. The reason for this is that the convergence rate of constant modulus algorithm is very dependent on the initialization region of the FIR weight vector. Due to the unique nature of the performance function (8), that is, its nonlinear characteristics, the weight vector evolves very differently depending on the region in which it is initialized. In [17] it was explained that, depending on the performance function and the initialization region, convergence may occur at a linear (slow) or exponential rate (very fast). In our case, the convergence rate is exponential, which is very ad- vantageous since the transmission of teletext signals occurs in vertical blanking period, which is 1.3 milliseconds.

From the simulations in Section V, it is heuristi- cally shown that to achieve convergence at an exponential rate, the initialization of the FIR filter taps for the equalization of the 2-Phase PSK and QPSK signals should be of A-pass type, that is, an impulse in the time domain. The same convergence rate is obtained for different multipath channel parameters with the same initialization. The full convergence analysis of the constant modulus algorithm has never been understood completely because of the nonlinear characteristics of the performance function J. Al- though there are papers about its convergence behavior [17, 211 they lack practicality and generality as they examine the situation for only two tones, for instance. Therefore, we decided to keep the conver-

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BER

0.0 1.6 3.2 4.8 6.4 8.0 0.0 1.6 3.2 4.8 6.4 8.0

(a) s m (a) (b) STJm (a)

Figure 9: (a) BER for QPSK Scheme Before (Solid Line) and After Equalization (Dotted Line) (b) For 2-Phase PSK Scheme

gence analysis at a heuristic level although a separate paper about the convergence analysis is going to fol- low. In this paper we only aimed at introducing a new application and examining the compatibility of blind equalizers with existing teletext systems.

V. COMPUTER SIMULATIONS

A . General

The performance evaluation of the blind equalizer described in Section IV has been accomplished using a time-driven block-oriented general communication system simulator, CAPSIM (Capture and Simulate) [19]. The simulated teletext system topolo- gies are illustrated in Figure 8.

B. Teletext System

As mentioned earlier, the performance of the proposed blind equalization technique has been evaluated for both 2-Phase PSK and QPSK teletext modulation schemes. Figure 8(a) shows the simulated teletext system topology for the QPSK case and Figure 8(b) for the 2-Phase PSK case. The teletext input message is first generated in the form of narrow NRZ pulses which are then shaped by a baseband teletext transmit filter. For the QPSK modulation scheme the teletext message symbols are represented as complex data, therefore, there are two symbol sequences going through the baseband transmit filter. How- ever, for the 2-Phase PSK modulation scheme there is only one symbol sequence as seen in Figure 8(b). For the QPSK scheme, the two symbol sequences are

modulated by two carriers in quadrature after pulse- shaping. The same procedure is carried out for the 2- Phase PSK case but the modulation is done with one carrier. Next, the modulated signal is passed first through the vestigial side-band transmit filter and then through the multipath broadcast channel. The multipath channel is composed of two paths. Such multipath channels are referred as "one bounce" mod- els described as

h(k) = ~ ( k ) + aejeS(k - T )

where a, 8 , and T are the multipath attenuation, phase shift, and delay respectively. The multipath attenuation and delay are 0.6 and 2 ps (20 samples), respectively. This multipath model corresponds to a specular two-ray multipath problem where the re- flected ray is 4.4 dB down from the direct path and delayed by 600 meters. These parameters are typical, for example, of the aeronautical communication channel described in [20]. Additive Gaussian noise is also present in the system after the channel, as illustrated in Figures 8(a),(b). The noise variance is 0.1. The complex quadrature demodulation of the received teletext signal is simulated by the first method, as explained in Section II(C).

The performance evaluation of the blind equalization is done in terms of signal-to-interference-and- noise-ratio (SINR) versus bit-error-rate (BER) curves. The SINR is defined as

(16)

1 (17) SINR = lOlog( PSignal

P M d t i p n t h + PNoise

where psig, and pM,&ipnth are the signal powers in

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2.5 r ., 2.5

..................

..................

..................

..................

...........................................

...... +

0.5 1.5 2.5 -2.5 -1.5 -0.5 0.5 1.5 2 3 -2.5 -1.5 -0.5

(a) (b)

Figure 10: (a) Received QPSK Constellation Before Equalization (b) After Equalization

-2.5 -1.5 -0.5 0.5 1.5 2.5

(a)

-2.5 3 -1.5 -0.5 0.5 1.5 2.5

(b)

Figure 11: (a) Received 2-Phase PSK Constellation Before Equaliza- tion (b) After Equalization

the direct path and in the interfering multipath, respectively. The B E R is defined as,

NETT07 B E R = - NTotal

where is the number of symbols received with error and N ~ d d is the total number of symbols received.

The simulation results are very satisfactory. A substantial improvement in the BER is observed for both QPSK and 2-Phase PSK modulation schemes as illustrated in Figures 9(a),(b) respectively. Also two example constellations before and after the e,qualiza- tion process, for both modulation schemes are given in Figures 10 and 11. For these particular cases the channel SINR is 2.2 dB. Both figures show excel- lent improvement in the intelligibility of the constellations.

.

VI. CONCLUSION

This paper has presented a new application of blind equalization which appears to offer a great deal of improvement to the delays and reliability of teletext systems. The compatibility with the existing TV system and the cost-effectiveness of this new application were also considered for practical purposes. Equalizer adaptation does not require a knowledge of the transmitted data sequence or carrier phase recovery. Therefore, a reduction in the existing teletext circuitry has also been achieved. In addition, the proposed QPSK modulation scheme doubles the teletext data transmission rate and also uses the allocated TV bandwidth efficiently. The integration of the blind equalizer with the digital TV sets is very cost-effective due to its all-digital architecture. It can also be added to conventional analog TV sets with


a simple A/D converter. The performance of the proposed blind equalizer has been evaluated in the presence of a multipath broadcast channel. Simula- tions have shown that substantial improvements are observed in the bit-error-rates for both 2-Phase PSK and QPSK modulation schemes.

VI. REFERENCES

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1151 D. N. Godard, “Self-Recovering Equalization and Carrier Tracking in Two-Dimensional Date Communication Sys- tems,” IEEE !l“nsactions on Communications, Vol. COM- 28, No. 11, November 1980, pp. 1867-1875.

[16] J. R. Treichler and B. G. Agee, “A New Approach to Multipath Correction of Constant Modulus Signals,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-31, NO. 2, April 1983, pp. 459-472.

[17] M. G. Larimore and J. R. Treichler, “Convergence Behavior of the Constant Modulus Algorithm,” ICASSP 1989 Boston, Vol. 1, pp. 13-16.

[18] B. Widrow, J. McCool, M. Ball, “The Complex LMS Algo- rithm,” Proceedings of the IEEE, April 1975, pp. 719-720.

[2] E. Arnon, “An Adaptive Equalizer for Television Channels”, IEEE Transactions on Communications, Vol. COM-17, No. 6, December 1969, p. 726.

[19] R. A. Nobakht, p. W. Pate, S. H. Ardalan, “CAPSIM: A Graphical Simulation Tool for Communication Systems,” GLOBECOM, 1988, pp. 1692-1696.

[3] S. Makino et al., “A Fully Automatic Ghost Canceller,” IEEE Transactions on Consumer Electronics, Vol. CE-24, No. 3, August 1978, pp. 267.

[20] G. S. Takhar and S. C. Gupta, “Discrete Estimation of Con- tinuous Angle Modulated Channels for Aeronautical Com- munication,” IEEE Transactions on Communications, Vol. COM-24, March 1976. [4] K. B. Benson, “Television Engineering Handbook,” Mc

Graw-Hill, 1980. [21] J. R. Treichler and M. G. Larimore, “The Tone Capture Properties of CMA-Based Interference Suppressors,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-33, No. 4, August 1985.

[5] T. Rzeszewski, “A New Teletext Channel,” IEEE Transac- tions on Communications, Vol. COM-29, No. 2, February 1981, pp. 110-116.

161 B. G. Agee, “The Property-Restoral Approach to Blind Adaptive Signal Extraction,” Ph. D Dissertation , Univer- sity of California, Davis, 1989.

[7] S. U. H. Qureshi, “Adaptive Equalization,” Proceeding of the IEEE, Vol. 73, No. 9, September 1985, pp. 1349-1387.

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[9] E. S. Sousa and S. Pasupathy, “Pulse Shape Design for Tele- text Data Transmission,” IEEE Bansactions on Communi- cations, Vol. COM-31, NO. 7, July 1983, pp. 871-878.

[lo] D. W. Rice and K. H. Wu, “Quadrature Sampling With High Dynamic Range,” IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-18, No. 4, November 1982, pp. 736-739.

C. M. Rader, “A Simple Method for Sampling In-Phase and Quadrature Components,” IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-20, No. 6, November 1984, pp. 821-824.

S. Haykin, “Communication Systems,” John Wiley and Sons, 1978.

G. J. Saulnier et al., “A VLSI Demodulator for Digital R F network Applications: Theory and Results,” IEEE Journal on Selected Areas in Communications, Vol. 8, No. 8, October 1990, pp. 1500-1511.

Jeyhan Karaoguz was born in Ankara, Tiirkiye, in 1966. He received the B.Sc degree in electrical engineering from the Middle East Technical University, Ankara, in 1988, and the M.$c degree from North Carolina State University, Raleigh, in 1989. He is currently studying towards the Ph.D degree in electrical engineering at North Carolina State University.

His research interests include blind adaptive equalization, applications of neural networks to signal

processing and digital communications, and mobile communications. Mr. Karaohz is a member of Eta Kappa Nu, Phi Kappa Phi, and Pi Mu Epsilon.

Saean Houston Ardalan was born in Tehran, Iran, on March 20, 1956. He received the B.Sc., M.Sc., and Ph.D. degrees in electrical and computer engineering from North Carolina State University, Raleigh, in 1977, 1979, and 1983, respectively.

In August 1983 he joined the Center for Communications and Signal Processing (CCSP) at NC State as a Research Engineer. He is currently an Associate Professor of Electrical and Computer Engi- _ _

neering. He is the Principal Investigator of the Physical Layer in the co”unications and Networking BTea in ccsp. Among his research interests are digital communications, adaptive digital signal processing, inchding blind equalization, electromagnetic radiation, and archi- tectures for real-time image formation from synthetic aperture radar. He is also a member of Sigma Xi.

[14] H. Samueli and B. C. Wong, “A VLSI Architecture for a High-speed All-Digital Quadrature Modulator and Demod- ulator for Digital Radio Applications,” IEEE Journal on Selected Areas in Communications, Vol. 8, NO. 8, October 1990, pp. 1512-1519.

Documents

Use of blind equalization for teletext broadcast systems