new applications of digital signal processing in communications - paper ieee

8/6/2019 New Applications of Digital Signal Processing in Communications - Paper IEEE

http://slidepdf.com/reader/full/new-applications-of-digital-signal-processing-in-communications-paper-ieee 1/8

NEH APPLICATIONS OF DIGITAL SIGNAL PROCESSING IN COMMJNICATIONS

1 .

Maurice G. BELLANGER

Laboratoire des Signaux et Sys tbes, EsE, 91190 Gif-Sur-Yvette, FRANCE

Abstract :

The evolution of telecommunications

towards an Integrated Services Digital

Network (ISDN) offers new opportunities for

signalrocessingpplications. Recent

progress in basic techniques, like perfect

signal ecomposition nd reconstruction or

Least Squares adaptive filtering, are

crucial inhatvolution.eyondhe

emergence of new

processing advances are

artificial intelligence

willccompanyhe

promised by the ISDN.

Introduction

equipment, signal

paving the way for

techniques, which

"information age"

Progress in the field of telecommunications

is guided by the concept of the ntegrated

Services Digital Network ISDN) hich an e

presented as fully digital general-purpose

communication network ith tandardized access

c11.

The ISDN isrimarilyharacterized by

digital transmission throughout, from subscriber

to subscriber. A number of consequences result.

First, a high and uniform quality is achieved,

independently of transmission media or distances.

In act, apart from economics, this ind fobjective has always been a major stimulation for

developing and applying digital techniques, Next,

the network can become general-purpose and handle

a wide range of ignals: oice or elephone

conversations, data between computer terminals,

text for private matters, business or diffusion,

ICASSP 86 , TOKYO

sound for recreational activities, and image for

person-to-person information and contact o r

diffusion. Many new terminals and new services

can therefore be expected to be imagined and to

emerge n he uture. Also, from he etwork

exploitation point f iew, improvements are

expected n he monitoring, maintenance, and

efficiency f ransmission edia. But to eet

these expectations and implement such a network

world-wide, a high levelfnternational

cooperation is mandatory. These aspects are the

responsibilityfhe International

Telecommunication Union (ITU), f which most

independent countries in the world are members.

More precisely, ithin ITU, the ISDN is the

responsibility of the International Telegraph and

Telephone Consultative Committee (CCITT),. which

is in charge of ssuing recommendations for

network interconnection and operation. A set of

recommendations f o r the ISDN has been issued as

Series I C23.

Several areas of digital signal processing

are important for the evolution of the ISDN, and

particularly igital ilters, daptive filters,

and signal analysis and recognition in connection

with artificial intelligence techniques. To begin

with,ome aspects ofeveralmportant

applications are reviewed in the context of the

ISDN evolution.

11. Evolution of telecommunication networks

The basic service is provided hrough he

conventional elephone ets. Attractive options

are provided by loud-speaking o r hands-free sets,

which give more comfort to he subscriber,

particularly in the office, and are ecessary for

usefulelephone conferences, forxample.

PRE. 3.

CH2243-4/86/0000-3127 $1.00 0 986 IEEE 3127



However, these evices re ubject o evere

disturbances aused by electrical nd coustic

couplings, which produce echos and instability.

Designndmplementationfdequate

echo-cancellers are a great challenge, from both

the theoretical nd practical points of view31 .

The channels are limited to the frequency

bandwidth 300-3400 Hz, which is the inimum

compatibleithncceptableevelf

conversation uality. It would e seful nd

appreciated in a number of situations to have a

larger bandwidth, not only for sound transmission

but alsooromfortndoreccurate

restitution of the nformation ontent of a

conversation. With bit-rate reduction techniques,

it is becoming ossible o ffer 0-7 kHz

audio-bandwidth [41 .

A growing number of subscribers wish to be

able to be reached at any time. Mobile services

can be generalized and will offer the same level

of quality as the fixed network in the future,

through portable sets or mobile radio terminals

in ars. Here gain, numerous dverse ffects

have to be countered; noise-cancelling techniques

are involved as well as means to achieve privacy

C5l.

Video terminals are expected to be needed

for wo inds of application, amely ideo

communications and man-machine interaction. The

bit ate or ideosignals is enormous, and

efficientechniques of multi-dimensional

processing ave o e orked ut o each

marketable terminals 161.

Combiningudio,magendata

transmission, t ecomes ossible to think of

intelligentetsxploitingecognitionnd

identification echniques. There is ample oom

for imagination here and the corresponding market

is likely to grow fast. However, great care has

to be exercised in order to provide services in

line with customer needs and which are socially

acceptable.

Another aspect of evolution worth pointing

out oncerns he xploitation of transmission

media. Digital signals are difficult to transmit

and additional cost may result. or example, high

speed ata ransmission on subscriber airs

requires sophisticated signal processing methods

in order to be efficient and practical. The same

applies o adio links, because he requency

spectrum is a scarce resource. Adaptive antenna

arrays may have to be used, for example. But,

overall,igitalechniquesringmproved

service uality hrough etwork ffectiveness,

complete supervision feasibility and disturbance

control.

In view of this evolution, crucial progress

in several signal processing areas will now be

reviewed.

111. Digital filter banks

Signals can be analyzed by dividing their

spectra into narrow frequency bands and examining

the content of each such band. Filter banks can

be used to carry out the operation accurately.n

important recent result is that such a signal

decomposition,ombinedithorresponding

sampling rate reduction, can be followed by a

virtuallyerfectignaleconstruction. The

operation is shown in Figure 1 .

DtmHPOSlTlMI RECMISTRLCTIW

Fig. 1 Signal decomposition and reconstruction

The signal x(n) is decomposed by a iscrete

Fourier Transform (DFT) OF order N completed by

an N patholyphaseetwork. It is

reconstructed as x ( n ) by an inverse DFT and an

path polyphase network [TI.

PRE. 3. 2

3128 ICASSP 86. TOKYO



TN=

If T N is the order DFT matrix:

1 1 1 . . . . . . .1 w w 2 . . . . . . .1 w2 w 4 . . * . . w

W N- 1

2 ( N - 1 )

1 W(N- l 1 W2(N-1 . . . . (N-l (N-1

The Z transform of the reconstructed signal is

expressed by a matrix equation [ 8 ] :

The condition of absence of aliasing components,

due to the intermediate subsampling at frequency

fs/N, is that ? ( Z ) be only a function of X ( Z ) ,

which in turn eads to the equation :

whose solution is :

, -l.GO

- 1

H1 H 2 . . ' HN-l

Ho H 2 . . * HN-l

N -1

i=OII Hi

GN- 1Ho H 1 . . * HN-2

\

..A straight implementation of that solution

is obtained for N=2 through the Quadrature Mirror

Image Filter (QMF) concept [SI. For higher

orders, n > 2 , a practical solution should lead to

Gi 2 ) and i(Z ) having imilar numbers of

coefficients and a global system transfer

N N

function withrbitrarymallipple. The

solution is obtained, starting from a prototype

low-pass filter ith requency response H(f),

such that :

as an extension of the QMF concept [ l o , 11 1. For

example, a bank of 1 6 filters and 3 2 coefficients

is given in Figure 2 .

-38.

-40.

-58.

-68.

NORMALIZED FREWEW

Fig. 2 Bank of 1 6 filters and 3 2 coefficients

Typical potential applications of such

filter banks can be found in medium-rate speech

coding [ 1 2 ] , like the 1 6 kbit/s coder fo r toll

quality telephone signals or in noise cancelling

for mobile radio. In the atter case, the N

outputs of he decomposition filter ank re

tested or tationarity nd a damping factor

proportional to the level of stationarity s

appliedoachneefore reconstruction.

Spurious signals much larger in magnitude than

the useful signals can be removed in that way

C131.

Many applications of digitalignal

processing in communications involve filters with

variable coefficients [ 1 4 ] . These are daptive

filters and the algorithms are considered first.

PRE. 3. 3

ICASSP 86, TOKYO 3129



IV. Adaptive filter algorithms

The principle of an adaptive filteris shown

in Figure 3. The adaptation algorithm uses the

error signal e(n) and the input x(n) to update

the coefficients according to a criterion which

is to be minimized. The criterion employed in

most cases is the east Mean Squares (LMS) of the

errorequence e(n), foromputational

complexity reasons. ut i t is now possible to

adsptive Algorithmfor Cmfficient Updating

Fig. 3 Principle of an adaptive filter

envisage the Least Squares (LS) criterion with

the same order of magnitude of complexity, due to

the availability of Fast LS algorithms.

Let X(n) be the vector of the N coefficients

hi(n) of he rogrammable ilter and X(n) the

vectorfhe N mostecentnputignal

samples:

I ; x(n)=I

(n) j-1 ,

n

p=J(n) = 1 I Y(P) - H(n)X(p) I ( 7 )

t

with W (O<<w<l) the weighting factor.

The solution t ime is the et of N

coefficients H(n) given b y the normal equations

C151 :

where R (n) is an estimation of the input signal

autocorrelation matrix and R (n) the vector of

intercorrelation coefficients between input and

reference signals :

N

YX

The coefficients are computed recursively y :

If the inverse matrix -l (n) has to be computed,N

it is clear hat or ncreasing rder N, the

complexity becomes enormous and theMS algorithm

consists of the simplifying approximation

RN (n) = 6 I1

N(12)

where 6 is a ositive onstant alled he

adaptation step size and IN the unity matrix of

order N. The coefficients are then updatedy :

Then the error signal (n) is expressed by:

te(n) = y(n) - H(n) X(n) (6)

In adaptive filters, a function of e(n), called

the cost function, has to be minimized, and the

weighted least squares criterion corresponds to

the minimization at time n of the cost function

J(n) given by :

PRE.

which eads o just doubling he omputations

with respect to fixed coefficient filters. That

algorithm has proved very useful and effective

many applications;owever,taseveral

important limitations in performance, among which

E161 :

. the initial convergence is slow

. convergenceepends on inputignal

characteristics

. a esidual rrortill xistsfter

3.4

3130 ICASSP 86, TOKYO



coefficients can actually be implemented in real

hardware.

VI. Echo cancellation

Echo ancellation s odelling roblem:

the model of the disturbing echo path is found

andpdated so thathepuriouscho is

subtracted from the received signal to produce a

clean useful signal. The method is used in full

duplex two wire data transmission [23,241, voice

echo control in satellite communications 251 and

hands-free elephone erminals for video nd

audio conference rooms [ 2 6 ] . The latter example

is illustrated in Figure 6.

Fig. 6 Audio conference system

In general very long filters are necessary, with

several undreds, nd ometimes housands, of

coefficients. Adaptation speed and accuracy are

important. For example, acoustic couplings in a

room are haracterized by a apidly volving

spectrum with numerous nd deep zones as hown in

Figure 7 [27]. Moreover, signals, like speech nd

sound, are non-stationary. The fastest and most

accurate lgorithms re eeded n ombination

withophisticatedodelling,orxample

pole-zero modelling of the echo path.

FFig. 7 Acoustic coupling in n audio conference

A similar approach can be used when it is

not an echo hich as o be removed, but

disturbing noise signals, like engine noise in

mobile radio, for xample.

VII. Adaptive array receivers

Adaptive array receivers are used for beam

forming in radar. They can also be implemented n

communications,omproveadio-receiver

performance,orxample,roeject

interferences.

A beamformer combines the outputs from the

elements of an antenna array to produce a beam

pattern hich ptimizes he eception. igital

adaptiveechniquesremplementedhrough

adaptive equalizers following receivers connected

to ntenna lements [28]. An illustration is

given in Figure 8. The algorithms are applied to

Y !

RECEIVER 1 EQUALIZER DECISIONLOolC

YRECEIVER2 T R

EQUALIZER

Fig. 8 Adaptive array receiver

multidimensional signals, which, in spite of the

previously-mentioned fast techniques, can lead to

high levels of computational complexities.

VIII. Artificial intelligence techniques

The interactions of signal processing with

Artificial Intelligence (AI) techniques take on

different aspects [29]. First, signal processing

is employed as a basic tool to build the data

bases on which AI works. Here the improvements in

accuracy and speed achieved in analysing signals

are ofrimarymportance.edundancy

reduction,hich is usednierarchical

methods, is alsonnterestingeature.

Illustrations can be found in speech or image

recognition.

Artificial Intelligence techniques can also

DATA

PRE. 3.6




be used in communication equipment to improve its

efficiency. Vector quantization for reduced rate

signal ncoding is an xample f edundancy

reduction, and results in less storage capacity

or transmission rate. But the most exciting field

for AI techniques is the et of intelligentterminals which will be deployed in the future.

They will be used, for example, by the employees

of operating companies in supervising the network

and managing the resources available. Intelligent

terminals will also provide new services to the

subscriber, not only in man-machine interaction,

but also in person-to-person communication [301.

That field is open to imagination and initiative,

and here is no oubt hat he atest ignal

processing echniques ill be instrumental n

that evolution.

IX. Conclusion

The ISDN is an ambitious step in improving

and extending communication means in the world.

The information is carried in the network by all

sorts ofignals. The importancefignal

processing techniques is therefore obvious, and

in fact these techniques are part of the whole

project. The evolution is characterized by the

fact that progress, inolvingundamental

problems, has mademportant applications

feasible. Examples have een given, uch s

signal decomposition and reconstruction or least

squares adaptations. A major observation is that

signal rocessing ill ave he ay or he

application of artificialntelligence

techniques.

Overall digitalignalrocessingill

contribute to the advent of the information age"

promised by the ISDN.

References

c1 I

C21

R. POKRESS, ltIntegrated services digital

networks1T, special issue of IEEE

Communications Magazine, Vo.l.22, n O1, Jan.1984.

CCITT Red Book, "Integrated Services DigitalNetwork", Fasc.III.5, Geneva, 1985.

C31

C41

C51

E61

E71

C81

C91

C.R. SOUTH, C.E. HOPPIT and A .V. LEWIS,

lfAdaptive filters to improve loudspeaking

telephoneTr, Electronic Letters, Vol. 15,

NO21 , 1979.

CCITT Study Group VIII eport n Q26,Wideb and speech codingT1, eneva, June 1985.

John OETTING, t7Cellular obile radio", IEEE

Communications Magazine, Vol.21, N0 8, Nov.

1983.

A.N. NETRAVALInd.O. LIMB, IIPicturecoding - a review", Proceedings of IEEE,

v01.68, N03, March 1980.

M BELLANGER, "Multirate filtering", Chap. 10

in Digital Signal Processing, John Wiley,

New York, 984.

T. RAMSTAD, llAnalysis synthesis filterbanks

withritical sampling", Digital Signal

Processing-84, Florence Conference, North

Holland, 1984, pp. 130-1 4.

C. GALAND and H. NUSSBAUMER, "New QMF filter

structures", IEEE Trans. ASSP, Vol. 32, N03,

June 1984, pp.522-531.

[lo] P.L CHU, "QMF filter design for an arbitrary

number of equal andwidth channels", IEEE

Trans. ol.ASSP-33, NO1 , pp.203-218, eb.

1985.

11 11 J. MASSON and Z. PICEL, "Flexible design ofnearly erfect MF ilter anks", rocs.

ICASSP-85, Tampa, FL, USA, March 1985,pp.541-544.

[12] R.E. CROCHIERE and L.R. RABINER, "Multirate

digital signal processingt1, Prentice-Hall,

Englewood Cliffs, New ersey, 1983.

[I31 P. VARY, lNoise suppression by spectral

magnitude estimlation", Signal Processing,

v01.8, N'4, July 1985, pp.387-400.

E141 T. CLAASEN and W. MECKLENBRAUKER, "Adaptivetechniques forignalrocessingn

communicationsR, IEEE Communications

Magazine, Vo1.23, N O 1 l , Nov.1985.

[15] C. COWAN and P. GRANT, "Adaptive filters",

Prentice-Hall, New Jersey, 1985.

[I61 M. BELLANGER, "Adaptive filters'', Chap.11 inDigital Signal Processing, John Wiley, New

York, 1984.

[ 1 7 ] D. FALCONER and L. LJUNG, "Application offastalman estimation to adaptive

equalization", IEEErans. onCommunications, Vol.COM-26, N o l o , Oct. 1978,

pp. 1439-1 446.

PRE. 3. 7

ICASSP 86 , TOKYO 3133



[l8] G. CARYANNIS, D. MONOLAKISnd.

KALOUPTSIDIS, "A fast sequential lgorithmfor least squares filtering and prediction",

IEEE Trans. on ASSP, Vol.ASSP-31, N06, Dee

1983, pp.1394-1402.

[lg] D. LIN, "On digital implementation of the

fast alman algorithmstt, IEEE rans. onASSP,ol.ASSP-32, N05, OCt.984,

pp.998-1005.

[20] J. CIOFFI and T. KAILATH, "Fast recursive Stransversal filters for adaptive filtering",IEEE Trans., Vol.ASSP-32, 02, April 1984.

[21] J .G. DUNN, "Signal processing: technologyand prospects", lectrical ommunication,

V01.59, N03, 1985, pp.252-259.

[22] . RICHARD, A. BENVENISTE and F. KRETZ,ltRecursivestimation ofocal

characteristics of edges in TV pictures as

applied to ADPCM oding", IEt'E Trans.,Vol.COM-32, N06, June 1984, p.718-728.

[23] M. STEIN, "Sematrans odems, a study",Philips Technical Review, V01.40, No4, Dee.

1982, pp.291-300.

[241 M. HONIG, ltEcho cancellation of voicebanddataignalssingLS and gradient

algorithms", EEE rans., ol.COM-33, NO 1,

Jan. 1985, pp.65-73.

[25] S.J. CAMPANELLA, H.UYDERHOLID and M.

ONUFRY, IAnalysis f an adaptive mpulseresponse cho cancellert1, OMSAT echnicalReview, 2(2), 1972.

[ 2 6 ] N. FURUYA, Y. ITOH, Y.MARUYAMA nd T.

ARASEKI, "Audio onference quipment ithacoustic echo cancellertt, EC Res. and Dev.

Journal, N"76, an. 1985.

[27] A GILLOIRE, J .P. JULLIEN and . SALIOU,ItCharacteristics f eleconference rooms",

CNET Technical Report 182 (in French), Nov.

1984.

[28] .G. TAYLOR, Ed.Adaptiventennas",

Special ssue, Procs. IEE, Vol.130,Pt. F,

NO 1, Jan. 1983, pp.1-151.

[29] J. VINCENT-CARREFOUR, Ed. 9tArtificial

intelligence", pecial ssue of "Echo esrecherches", N0119-120, 1985.

[3O] Proceedings of he th orld elecomm.Forum, Geneva, witzerland, Oct. 29, 1983.

PRE. 3. 8


new applications of digital signal processing in communications - paper ieee

Documents