PDC_Chap1

8/12/2019 PDC_Chap1

1/26

Principles Of Digital CommunicationsA Top-Down Approach

Bixio Rimoldi

School of Computer and Communication SciencesEcole Polytechnique Federale de Lausanne (EPFL)

Switzerland

c 2000 Bixio Rimoldi Version January 7, 2013

8/12/2019 PDC_Chap1

2/26

ii

8/12/2019 PDC_Chap1

3/26

Contents

Preface vii

1 Introduction and Objectives 1

1.1 The Big Picture through the OSI Layering Model . . . . . . . . . . . . . . . . 11.2 The Bit as the Universal Information Currency . . . . . . . . . . . . . . . . . 5

1.3 Problem Formulation and Preview . . . . . . . . . . . . . . . . . . . . . . . . 8

1.4 Digital vs Analog Communication . . . . . . . . . . . . . . . . . . . . . . . . 13

1.5 A Few Anecdotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2 Receiver Design for Discrete-Time Observations 17

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.2 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.1 Binary Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . 22

2.2.2 m -ary Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . 24

2.3 The Q Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.4 Receiver Design for Discrete-Time AWGN Channels . . . . . . . . . . . . . . 26

2.4.1 Binary Decision for Scalar Observations . . . . . . . . . . . . . . . . . 27

2.4.2 Binary Decision for n -Tuple Observations . . . . . . . . . . . . . . . 29

2.4.3 m -ary Decision for n -Tuple Observations . . . . . . . . . . . . . . . 32

2.5 Irrelevance and Sufficient Statistic . . . . . . . . . . . . . . . . . . . . . . . . 34

2.6 Error Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.6.1 Union Bound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

iii

8/12/2019 PDC_Chap1

4/26

iv CONTENTS

2.6.2 Union Bhattacharyya Bound . . . . . . . . . . . . . . . . . . . . . . . 41

2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.A Facts About Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

2.B Densities after One-To-One Diff erentiable Transformations . . . . . . . . . . . 502.C Gaussian Random Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.D A Fact About Triangles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.E Spaces: Vector; Inner Product; Signal . . . . . . . . . . . . . . . . . . . . . . 57

2.E.1 Vector Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.E.2 Inner Product Space . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

2.E.3 Signal Space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

2.F Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

3 Receiver Design for the Waveform AWGN Channel 91

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

3.2 Gaussian Processes and White Gaussian Noise . . . . . . . . . . . . . . . . . 93

3.2.1 Dirac-Delta-Based Denition of White Gaussian Noise . . . . . . . . . 93

3.2.2 Observation-Based Denition of White Gaussian Noise . . . . . . . . . 94

3.3 Observables and Sufficient Statistic . . . . . . . . . . . . . . . . . . . . . . . 97

3.4 Transmitter and Receiver Architecture . . . . . . . . . . . . . . . . . . . . . . 100

3.4.1 Alternative Receiver Structures . . . . . . . . . . . . . . . . . . . . . 103

3.5 Continuous-Time Channels Revisited . . . . . . . . . . . . . . . . . . . . . . 108

3.6 Summary and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

3.A Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

4 Signal Design Trade-O ff s 1214.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

4.2 Design Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121

4.3 Bandwidth Denitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

4.4 Isometric Transformations Applied to the Codebook . . . . . . . . . . . . . . 124

4.5 The Energy-Minimizing Translation . . . . . . . . . . . . . . . . . . . . . . . 126

8/12/2019 PDC_Chap1

5/26

CONTENTS v

4.6 Isometric Transformations Applied to the Waveform Set . . . . . . . . . . . . 128

4.7 Time Bandwidth Product versus Dimensionality . . . . . . . . . . . . . . . . 128

4.8 Building Intuition about Scalability: n versus k . . . . . . . . . . . . . . . . 132

4.8.1 Keeping n Fixed as k Grows . . . . . . . . . . . . . . . . . . . . . . 1324.8.2 Growing n Linearly with k . . . . . . . . . . . . . . . . . . . . . . . 134

4.8.3 Growing n Exponentially With k . . . . . . . . . . . . . . . . . . . . 136

4.8.4 Bit-By-Bit Versus Block-Orthogonal . . . . . . . . . . . . . . . . . . . 139

4.9 Conclusion and Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

4.10 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

5 Nyquist Signalling 1535.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153

5.2 The Ideal Lowpass Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

5.3 Power Spectral Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

5.4 Nyquist Criterion for Orthonormal Bases . . . . . . . . . . . . . . . . . . . . 161

5.5 Symbol Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

5.5.1 Maximum Likelihood Approach . . . . . . . . . . . . . . . . . . . . . 166

5.5.2 Delay Locked Loop Approach . . . . . . . . . . . . . . . . . . . . . . 167

5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

5.A L1 , L2 , and Lebesgue Integral: A Primer . . . . . . . . . . . . . . . . . . . 171

5.B Fourier Transform: a Review . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

5.C Fourier Series: a Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

5.D Proof of the Sampling Theorem . . . . . . . . . . . . . . . . . . . . . . . . . 179

5.E Square-Root Raised-Cosine Pulse . . . . . . . . . . . . . . . . . . . . . . . . 180

5.F The Picket Fence Miracle . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

5.G Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

6 Convolutional Coding and Viterbi Decoding 195

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

6.2 The Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

8/12/2019 PDC_Chap1

6/26

vi CONTENTS

6.3 The Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

6.4 Bit Error Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

6.4.1 Counting Detours . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

6.4.2 Upper Bound to P b . . . . . . . . . . . . . . . . . . . . . . . . . . . 2066.5 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

6.A Formal Denition of the Viterbi Algorithm . . . . . . . . . . . . . . . . . . . 212

6.B Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

7 Passband Communication via Up/Down Conversion 225

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

7.2 The Baseband-Equivalent of a Passband Signal . . . . . . . . . . . . . . . . . 2287.3 Analog Amplitude Modulations: DSB, AM, SSB, QAM . . . . . . . . . . . . 233

7.4 Receiver for Passband Communication over the AWGN Channel . . . . . . . . 236

7.5 Baseband-Equivalent Channel Model . . . . . . . . . . . . . . . . . . . . . . 240

7.6 Parameter Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244

7.7 Noncoherent Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 249

7.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251

7.A Relationship Between Real and Complex-Valued Operations . . . . . . . . . . 252

7.B Complex-Valued Random Vectors . . . . . . . . . . . . . . . . . . . . . . . . 254

7.B.1 General Statements . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

7.B.2 The Gaussian Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255

7.B.3 The Circularly Symmetric Gaussian Case . . . . . . . . . . . . . . . . 256

7.C Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263

Notation and Symbols 275

8/12/2019 PDC_Chap1

7/26

Preface

This text is intended for a one-semester course on the foundations of digital communi-cation. It assumes that the reader has basic knowledge of linear algebra, probabilitytheory, and signal processing, and has the mathematical maturity that is expected froma third-year engineering student.

The text has evolved out of lecture notes that I have written for EPFL students. Therst pass of my notes greatly proted from three excellent sources, namely the bookPrinciples of Communication Engineering by Wozencraft and Jacobs [ 1], the lecture noteswritten by ETH Prof. J. Massey for his course Applied Digital Information Theory , andthe lecture notes written by Profs. B. Gallager and A. Lapidoth for their MIT courseIntroduction to Digital Communication . Through the years the notes have evolved andalthough the inuence of these sources might still be recognizable, the text has now itsown personality in terms of content , style , and organization .

The content is what I can cover in a one-semester course at EPFL. The focus is thetransmission problem. By staying focused on the transmission problem (rather than alsocovering the source digitalization and compression problem), I have just the right contentand amount of material for the goals that I deem most important, specically: (1) coverto a reasonable depth the most central topic of digital communication; (2) have enoughmaterial to do justice to the beautiful and exciting area of digital communication; and(3) provide evidence that linear algebra, probability theory, calculus, and Fourier analysisare in the curriculum of our students for good reasons. Regarding this last point, the areaof digital communication is an ideal showcase for the power of mathematics in solvingengineering problems. Of course the problems of digitizing and compressing a source arealso important, but covering the former requires a digression into signal processing toacquire the necessary technical background, and the results are less surprising than those

related to the transmission problem (which can be tackled right away). The latter iscovered in all information theory courses and rightfully so. A more detailed account of the content is given below, where I discuss the text organization.

In terms of style , I have paid due attention to proofs. The value of a rigorous proof goesbeyond the scientic need of proving that a statement is indeed true. From a proof we cangain much insight. Once we see the proof of a theorem, we should be able to tell why theconditions (if any) imposed in the statement are necessary and what can happen if theyare violated. Proofs are also important because the statements we nd in theorems and

vii

8/12/2019 PDC_Chap1

8/26

viii

the like are often not in the exact form needed for a particular application. Therefore, wemight have to adapt the statement and the proof as needed.

However, this text is written for people with the mathematical background of an engineer,which means that I cannot assume Lebesgue integration. Lebesgue integration is needed

to introduce the space of L2 functions, which in turn is needed for a rigorous statementof the sampling theorem and of Nyquists criterion (Chapter 5). The compromise I makeis to introduce these terms informally in Appendix 5.A.

I think that an instructor should not miss the opportunity to share useful tricks. One of my favorites is the trick I learned from Prof. Donald Snyder (Washington University) onhow to label the Fourier transform of a rectangle. (Most students remember that it is asinc but tend to forget how to determine its height and width. See Appendix 5.B.)

I do not use the Dirac delta function except for alternative derivations and illustrativeexamples. The Dirac delta function is widely used in communication books to introduce

white noise and to prove an imprecise formulation of the sampling theorem. The Diracdelta function is a generalized function and we do not have the background to use itrigorously. Furthermore, students nd themselves on shaky ground when something goeswrong in a derivation that contains the Dirac delta function. Fortunately we can avoidDirac deltas. In introducing white noise (Section 3.2) we avoid the use of the Dirac deltafunction by modeling not the white noise itself but the e ff ect that white noise has onmeasurements. In Appendix 5.C we prove the sampling theorem via Fourier series.

The remainder of this preface is about organization . There are various ways to organizethe discussion around the diagram of Figure 1.3. The approach I have chosen is top-downwith successive renements. It is top-down in the sense that we begin by considering the

communication problem seen by the encoder/decoder pair of Figure 1.3 and move downin the diagram as we go, each time considering a more realistic channel model. It containssuccessive renements in the sense that we pass a second time over certain blocks: thefocus of the rst pass is on where to do what , whereas in the second pass we concentrateon how to do it in a cost and computationally e ff ective manner. The renements willconcern the top two layers of Figure 1.3.

In Chapter 2 we acquaint ourselves with the receiver design problem for channels thathave a discrete output alphabet. In doing so, we hide all but the most essential aspect of a channel, specically that the input and the output are related stochastically. Startingthis way takes us very quickly to the heart of digital communication the decision ruleimplemented by a decoder that minimizes the error probability. The decision problem is anexcellent place to begin as the problem is new to students, it has a clean-cut formulation,the solution is elegant and intuitive, and and the topic is central to digital communication.After a rather general start, the communication problem is specialized for the discrete-time AWGN (additive white Gaussian noise) channel, which plays a key role in subsequentchapters. In Chapter 2 we also learn how to determine (or upper-bound) the probabilityof error and we develop the notion of suffi cient statistic, needed in the following chapter.The appendices provide a review of relevant background material on matrices, on how

8/12/2019 PDC_Chap1

9/26

ix

to obtain the probability density function of a variable dened in terms of another, onGaussian random vectors, and on inner product spaces. The chapter contains a ratherlarge collection of homework problems.

In Chapter 3 we make an important transition concerning the channel used to communi-

cate, specically from the rather abstract discrete-time channel to the realistic continuous-time AWGN channel. The objective will remain the same, i.e., develop the receiver struc-ture that minimizes the error probability. The theory of inner product spaces, as wellas the notion of suffi cient statistic developed in the previous chapter, give us the toolsneeded to make the transition elegantly and swiftly. We discover that the decompositionof the transmitter and the receiver, as done in the top two layers of Figure 1.3, is generaland natural for the continuous-time AWGN channel.

Up until Chapter 4 we assume that the transmitter has been given to us. In Chapter 4we prepare the ground for the signal-design problem . We introduce the design parame-ters that we care about, namely transmission rate, delay, bandwidth, average transmittedenergy, and error probability, and we discuss how they relate to one another. We intro-duce the notion of isometry to change the signal constellation without a ff ecting the errorprobability: It can be applied to the encoder to minimize the average energy withoutaff ecting the other system parameters such as transmission rate, delay, bandwidth, errorprobability; alternatively, it can be applied to the waveform former to vary the signalstime/frequency features. The chapter ends with three case studies aimed at developingintuition. In each case, we x a signaling family parameterized by the number of bitsconveyed by a signal and determine the probability of error as the number of bits growsto innity. For one family, the dimensionality of the signal space stays xed and the con-clusion is that the error probability goes to 1 as the number of bits increases. For another

family, we let the signal space dimensionality grow exponentially and we will see that inso doing we can make the error probability become exponentially small. Both of thesetwo cases are instructive but have drawbacks that make them unworkable approaches.From the case studies, the reasonable choice seems to be the middle-ground solutionthat consists in letting the dimensionality grow linearly with the number of bits.

In Chapter 5 we do our rst renement that consists in zooming into the waveform formerand the n -tuple former of Figure 1.3. We pursue a design strategy based on the lessonslearned in the previous chapter. We are now in the realm of signaling based on Nyquists criterion . We also learn how to compute the signals power spectral density . In thischapter we also take the opportunity to underline the fact that the sampling theorem,

the Nyquist criterion, the Fourier transform and the Fourier series are applications of the same idea: signals of an appropriately dened space can be synthesized via linearcombinations of a set of vectors that form an orthogonal basis.

The second renement takes place in Chapter 6 where we zoom in on the encoder/decoderpair. The idea is to expose the reader to a widely-used way of encoding and decoding.Because there are several coding techniques su ffi ciently many to justify a dedicated one-or two-semester course we approach the subject by means of a case study based onconvolutional coding. The minimum error probability decoder incorporates the Viterbi

8/12/2019 PDC_Chap1

10/26

x

algorithm. The content of this chapter was selected as an introduction to coding andto introduce the reader to elegant and powerful tools, such as the previously mentionedViterbi algorithm and the tools to assess the resulting bit-error probability, notably detourow graphs and generating functions.

Chapter 7 introduces the layer that deals with passband AWGN channels.A nal note to the instructor who might consider taking a bottom-up approach withrespect to Figure 1.3: Specically, one could start with the passband AWGN channelmodel and, as the rst step in the development, reduce it to the baseband model bymeans of the up/down converter. In this case the natural second step is to reduce thebaseband channel to the discrete-time channel and only then address the communicationproblem across the discrete-time channel. I nd such an approach to be pedagogically lessappealing as it puts the communication problem last rather than rst. As formulated byClaude Shannon, the father of modern digital communication, The fundamental problemof communication is that of reproducing at one point either exactly or approximatelya message selected at another point. This is indeed the problem that we address inChapter 2. It should be said that beginning with the communication problem across thediscrete-time channel, as we do, requires that students accept the discrete-time channelas a channel model worthy of consideration. This is an abstract channel model and thestudent will not immediately see its connection to reality. I motivate this choice two ways:(i) by asking the students to trust that the theory we develop for that abstract channelwill turn out to be exactly what we need for more realistic channel models and (ii) byreminding them of the (too often overlooked) problem-solving technique that consists inaddressing diffi cult problem by considering rst simplied toy versions of the same.

8/12/2019 PDC_Chap1

11/26

Chapter 1

Introduction and Objectives

Apart from this introductory chapter, this book focuses on the system-level engineeringaspects of digital point-to-point communication. In a way, digital point-to-point com-munication is the building block we use to construct complex communication systemsincluding the Internet, cellular networks, satellite communication, etc. The purpose of this chapter is to provide contextual information. Specically, we do the following:

(i) Place digital point-to-point communication into the bigger picture. We do so inSection 1.1 where we discuss the Open System Interconnect (OSI) layering model;

(ii) Provide the background that justies how we often model the information to betransmitted, namely as a sequence of independent and uniformly distributed bits

(Section 1.2);(iii) Give a preview for the rest of the book (Section 1.3);

(iv) Clarify the di ff erence between analog and digital communication (Section 1.4);

(v) Conclude the chapter with a few amusing and instructive anecdotes related to thehistory of communication (Section 1.5).

The reader eager to get started can skip this chapter without losing anything essential tounderstand the rest of the text.

1.1 The Big Picture through the OSI Layering Model

When we communicate using electronic devices, we produce streams of bits that typicallygo through various networks and are processed by devices from a variety of manufacturers.The system is very complex and there are a number of things that can go wrong. Dueto their complexity, it is amazing that we can communicate as easily and reliably as

1

8/12/2019 PDC_Chap1

12/26

2 Chapter 1.

SendingProcess

ReceivingProcess

Application ApplicationAH Data

Presentation PresentationPH

Session SessionSH

Transport TransportTH

Network NetworkNH

Data Link Data LinkDH DT

Physical PhysicalDH NH TH SH PH AH Data DT

Physical Medium

Figure 1.1: OSI Layering Model.

we do. This could hardly be possible without layering and standardization. The OpenSystem Interconnect (OSI) layering model of Figure 1.1 describes a standard data-owarchitecture. Although it did not become a commercial success, it inspired other protocols,notably the TCP/IP used for the internet. In this section we use the OSI layering modelto convey the basic idea of how modern communication networks deals with the keychallenges, notably routing, ow control, reliability, privacy, and authenticity.

For the sake of concreteness, let us take e-mailing as a sample activity. Computers usebytes (8 bits) or multiples thereof to represent letters. So the message of an e-mail isrepresented by a stream of bytes that we call a data segment. Received e-mails usuallysit on a remote server. When we launch a program to read e-mail hereafter referred toas the client it checks into the server to see if there are new e-mails. It depends on theclients setting whether a new e-mail is automatically downloaded to the client or justa snippet is automatically downloaded until the rest is explicitly requested. The clienttells the server what to do. For this to work, the server and the client not only need to

8/12/2019 PDC_Chap1

13/26

1.1. The Big Picture through the OSI Layering Model 3

be able to communicate the content of the mail message but they also need to talk toone another for the sake of coordination. This requires a protocol. If we use a dedicatedprogram to do email (as opposed to using a web browser), the common protocols used forretrieving e-mail are the IMAP (Internet Message Access Protocol) and the POP (PostOffi ce Protocol), whereas for sending e-mail it is common to use the SMTP (Simple MailTransfer Protocol).

The idea of a protocol is not specic to e-mail. Every application that uses the Inter-net needs a protocol to interact with a peer application. The OSI model reserves theapplication layer for programs (also called processes) that implement application-relatedprotocols. In terms of data tra ffi c, the protocol places a so-called application header (AH)in front of the data packet. The top arrow in the gure indicates that the two applicationlayers talk to one another as if they had a direct link.

Typically, there is no direct physical link between the two application layers. Instead, thecommunication between application layers goes through a shared network, which createsa number of challenges. To begin with, there is no guarantee of privacy for anythingthat goes through a shared network. Furthermore, networks carry data from many usersand can get congested. Hence, if possible, the data should be compressed to reducethe tra ffi c. Finally, there is no guarantee that the sending and the receiving computersrepresent letters the same way. Hence the application header and the data need to becommunicated by using a universal language. Translation to/from a universal language,compression, and encryption are done by the presentation layer . The presentation layeralso needs a protocol to talk to the peer presentation layer at the destination. The protocolis implemented by means of the presentation header (PH).

For the presentation layers to talk to one another, we need to make sure that the twohosting computers are connected. Establishing, maintaining and ending communicationbetween physical devices is the job of the session layer . The session layer also managesaccess rights. Like the other layers, the session layer uses a protocol to interact with thepeer session layer. The protocol is implemented by means of the session header (SH).

The layers we have discussed so far would suffi ce if all the machines of interest wereconnected by a direct and reliable link. In reality, links are not always reliable. Makingsure that from an end-to-end point of view the link appears reliable is one of the tasks of the transport layer . By means of parity check bits, the transport layer veries that thecommunication is error-free and if not, it requests retransmission. The transport layer

has a number of other functions, not all of which are necessarily required in any givennetwork. The transport layer can break and reassemble long data packets into shorterones or it can multiplex several sessions between the same two machines into a single one.The transport layer uses the transport header (TH) to communicate with the peer layer.

Now assume that packets have to go through intermediate nodes. The network layer will provide routing and ow control services. Flow control refers to the need to queueup packets at a node if the network is congested or if the receiving end cannot absorbdata su fficiently fast. Unlike the above layers, which operate on an end-to-end basis,

8/12/2019 PDC_Chap1

14/26

4 Chapter 1.

the network layer and the layers below have a process also at intermediate nodes. Theprotocol of the network layer is implemented in the network header (NH). The networkheader contains the destination address.

The next layer is the data link control (DLC) layer. Unlike the other layers, the DLC puts

a header at the beginning and a tailer at the end of each packet. Some of the overheadbits are parity check bits meant to determine if errors have occurred in the link betweennodes. If the DLC detects errors, it might ask to retransmit or drop the packet altogether.If it drops the packet, it is up to the transport layer, which operates on an end-to-endbasis, to request retransmission. The other important function of the DLC is to create asynchronous bit stream for the next layer, which works synchronously. So the DLC notonly has to output bits at the rate determined by the next layer but it has to ll in withdummy bits when there is nothing to be transmitted. The header and the tailer insertedby the DLC make it possible for the peer processor to identify and remove dummy bits.

The physical layer the subject of this text is the bottom layer of the OSI stack.The physical layer creates a more-or-less reliable bit pipe out of the physical channelbetween two nodes. It does so by means of a transmitter/receiver pair, called modem 1 ,on each side of the physical channel. For best performance, the sender on one side andthe receiver on the other side of the physical channel need to work synchronously. Tomake this possible, the service provided by the DLC layer is the ability to send data ata constant rate. We will learn that the physical layer designer can trade reliability forcomplexity and delay.

In summary, the OSI model has the following characteristics. Although the actual datatransmission is vertical, each layer is programmed as if the transmission were horizontal.For a process, whatever is not part of its own header is considered as being actual data.In particular, a process makes no distinction between the headers of the higher layers andthe actual data segment. For instance, the presentation layer translates, compresses, andencrypts whatever it receives from the application layer, attaches the PH, and sends theresult to its peer presentation layer. The peer in turn reads and removes the PH anddecrypts, decompresses, and translates the packet which is then passed to the applicationlayer. What the application layer receives is identical to what the peer application layerhas sent up to a possible language translation. The DLC inserts a tailer in addition to aheader. All layers, except the transport and the DLC layer, assume that the communica-tion to the peer layer is error-free. If it can, the DLC layer provides reliability betweensuccessive nodes. Even if the reliability between successive nodes is guaranteed, nodes

might drop packets due to queueing overow. The transport layer, which operates at theend-to-end level, will detect missing packets and will request retransmission.

1 Modem is the result of contracting mod ulator and dem odulator. In analog modulation, such asFrequency Modulation (FM) and in Amplitude Modulation (AM), the modulator is the heart of thetransmitter. It determines how the information signal a ff ects the transmitted signal. In AM it is thecarriers amplitude, and in FM the carriers frequency that is modulated by the information signal.The operation is undone at the receiver by the demodulator. Although in digital communication it is nolonger appropriate to talk about modulator and demodulator, the term modem has remained in use.

8/12/2019 PDC_Chap1

15/26

1.2. The Bit as the Universal Information Currency 5

It should be clear that a layering approach drastically simplies the tasks of designingand deploying communication infrastructure. For instance, a programmer can test theapplication layer protocol with both applications running on the same computer thusbypassing all networking problems. Likewise, a physical layer specialist can test a mo-dem on point-to-point links, also disregarding networking issues. Each of the tasks of compressing, providing reliability, privacy, authenticity, routing, ow control, and phys-ical layer communication require specic knowledge. Thanks to the layering approach,each task can be accomplished by people specialized in their respective domain. Simi-larly, equipments from di ff erent manufacturers work together, as long as they respect theprotocols.

The OSI model is a generic standard that does not prescribe a specic protocol. TheInternet uses the TCP/IP protocol, which is more or less compatible with the OSI archi-tecture but uses 5 instead of 7 layers. The reduction is essentially obtained by combiningthe OSI application, presentation, and session layers into a single layer called application

layer. Below the application layer is the TCP layer , which provides end to end servicesand corresponds to the OSI transport layer. Below the TCP layer is the IP layer , whichdeals with routing. The DLC and the physical layers complete the stack.

1.2 The Bit as the Universal Information Currency

We will often assume that the message to be communicated is the realization of a sequenceof independent and identically distributed binary symbols. The purpose of this sectionis to justify this assumption. In so doing, we will see why the bit has the status of a

universal information currency.Source coding is about the representation of a signal by a string of symbols from a nite(often binary) alphabet. Some applications require the ability to faithfully reconstructthe original from the representation. In some cases, only an approximate reconstructionis required. In general, a more accurate reconstruction requires a longer representation.The goal of source coding is to provide the shortest possible representation for a desiredreconstruction accuracy. We could dedicate an entire course to source coding, but themain idea can be summarized rather quickly.

A source output is a realization of a stochastic process. Modeling the source as a stochas-tic process makes it possible to rigorously study some fundamental questions such as thefollowing. Let . . . , B 0 , B 1 , B 2 , . . . be the (discrete time and alphabet) stochastic processrepresenting the source and R(t) be the (continuous-time) stochastic processes represent-ing the channel output observed by a receiver. Is it possible for the receiver to reconstructthe realization of . . . , B 0 , B 1 , B 2 , . . . from the realization of R(t) ?

If we model the source signal as a deterministic function, say as the output of a pseudoran-dom generator of a xed initial state, then we can always reproduce the same function atthe receiver and reconstruct the source signal. Hence, in this case the answer to the ques-

8/12/2019 PDC_Chap1

16/26

6 Chapter 1.

tion posed in the preceding paragraph is always yes , even without observing the channeloutput R(t) . Clearly there is something wrong with this setup, namely that the sourceoutput is deterministic. If we dene the source output as a random process, we can nolonger cheat about the way we reconstruct the source output. We now describe threekind of sources.

Discrete Sources: A discrete source is modeled by a discrete-time random process thattakes values in some nite alphabet. A computer le is represented as a sequence of bytes,each of which that can take on one of 256 possible values. So when we consider a leas being the source signal, the source can be modeled as a discrete-time random processtaking values in the nite alphabet {0, 1, . . . , 255} . Alternatively, we can consider the leas a sequence of bits, in which case the stochastic process takes values in {0, 1} .

For another example, consider the sequence of pixel values produced by a digital camera.The color of a pixel is obtained by mixing various intensities of red, green and blue.Each of the three intensities is represented by a certain number of bits. One way toexchange images is to exchange one pixel at a time, according to some predeterminedway of serializing the pixels intensities. Also in this case we can model the source as adiscrete-time process.

A discrete-time sequence taking values in a nite alphabet can always be converted into abinary sequence. The resulting average length depends on how we do the conversion andon the source statistic. The statistic matters because to obtain the minimum average,the length of the binary sequence depends on the probability of the original sequence.In principle we could run through all possible ways of making the conversion; for eachway we could determine the average number of bits per source symbol; and if we carrythis program out to the end we will nd the minimum average length. Surprisingly, wecan bypass this tedious process and nd the result by means of a simple formula thatdetermines the so-called source entropy . Typically the entropy is denoted by the letter H and has bits as units. If the entropy of a discrete source is H bits, then it is possible toencode blocks of source symbols into strings of bits in such a way that it takes H bits persource symbol in average. Conversely, with a smaller average number of bits per sourcesymbol, it is not possible to have a one-to-one map from source symbols to bits.Example 1. For a discrete memoryless source that produces symbols taking values in anm -letter alphabet the entropy formula is

m

i =1 pi log2 pi ,

where pi , i = 0, 1, . . . , m 1 is the probability that the source outputs the i th alphabetletter. For instance, if m = 3 and the probabilities are p1 = 0.5 , p2 = p3 = 0.25 thenH = 1.5 . In this case, in average we can encode 2 ternary source symbols into 3 binary symbols (but not fewer). One way to achieve this is to map the most likely source letter into 1 and the other two letters into 01 and 00, respectively.

Any book on information theory will prove the stated relationship between the entropy

8/12/2019 PDC_Chap1

17/26

1.2. The Bit as the Universal Information Currency 7

of a memoryless source and the minimum average number of bits needed to represent asource symbol. A standard reference is [2].

From the above result, it is not hard to see that the only way for a binary source to haveentropy 1 [bit per symbol] is if it produces symbols that are independent and uniformly

distributed. Such a source is called a Binary Symmetric Source (BSS) . We conclude thata binary source is either a BSS or its output can be compressed. Compression is a wellunderstood technique, widely used in modern systems. Hence communication devices aretypically designed by assuming the the source is a BSS.

Discrete-Time Continuous-Alphabet Sources: They are modeled by a discrete-time ran-dom process that takes values in some continuous alphabet. If we measure the temperatureof a room at regular intervals of time, we obtain a sequence of real-valued numbers. Wewould model it as the realization of a discrete-time continuous alphabet random pro-cess. To store or to transmit the realization of such a source, we rst round up or downthe number to the nearest element of some xed discrete set of numbers. This is calledquantization and the result is the quantized process with the discrete set as its alphabet.Quantization is irreversible, but by choosing a su ffi ciently dense alphabet, we can makethe diff erence between the original and the quantized process as small as desired. As de-scribed in the previous paragraph, the quantized sequence can be converted into a binarysequence that has the same statistic as the output of a BSS.

Continuous-Time Sources: They are modeled by a continuous-time random process. Theelectric signal produced by a microphone can be seen as a sample path of a continuous-time random process. In all practical applications, such signals are either band-limited orcan be lowpass-ltered to make them band-limited. For instance, even the most sensitivehuman ear cannot detect frequencies above some value (say 25 KHz). Hence any signalmeant for the human ear can be made band-limited through a lowpass lter. The samplingtheorem (Theorem 72) asserts that a band-limited signal can be represented by a discrete-time sequence, which in turn can be made into a binary sequence as described.

In summary, given that we compute and communicate through binary symbols, thatelectronic circuits are becoming more and more digital, and that every information sourcecan be described in terms of binary symbols, it is not surprising that the bit has becomethe universal currency for the exchange of information. In fact every memory deviceand every communication device has a binary interface these days.

The practical benets of agreeing to communicate by means of a standard (binary) al-phabet are obvious. But is there a performance reduction associated with the rigidity of a xed alphabet? Yes and no. It depends on the specic scenario and on how we measureperformance; but there is an important case for which the answer is no. We briey sum-mary this result as it is one of the most celebrated results of information theory and itconstitutes the single most authoritative fundamental justication for giving the bit thestatus of a universal currency.

Like sources, channels too have a fundamental quantity assigned to them, namely thechannel capacity, denoted by C , and typically expressed in bits per second. But what

8/12/2019 PDC_Chap1

18/26

8 Chapter 1.

is a channel? Einstein made the following analogy: You see, wire telegraph is a kindof a very, very long cat. You pull his tail in New York and his head is meowing in LosAngeles. The important point here is that the cats reaction is always a bit di ff erent evenwhen we pull his tail the same way. More concretely, a channel can be a cable of twistedcopper wires, a phone line, a coaxial cable, an optical ber, a radio link, etc. Channelsare noisy, hence the output is not a deterministic function of the input. Surprisingly, aslong as the channel output is not statistically independent of the input we can use it totransmit information reliably. Additional noise can slow down the transmission rate butcannot prevent us from communicating reliably.

For every channel model and set of constraints (e.g. limits on the power and bandwidth of the transmitted signal) we can compute the channel capacity C in, say, bits per second.A fundamental theorem in information theory proves the existence of a transmitter and areceiver that make it possible to send C bits per second while keeping the error probabilityas small as desired. Conversely, if we try to send at a rate higher than C bits per second

then errors will be inevitable.From the discussion on the source entropy H and channel capacity C , we conclude thatwe can turn the source signal into a sequence of H bits per seconds and the channel into abit pipe capable of transporting C bits per second. As long as H < C , we can send thesource bits through the channel. A source decoder will be able to reconstruct the sourcesignal from the compressed source bits reproduced by the receiver. What if H > C ?Could we still reproduce the source output across the channel if we are not constrainedto a binary interface? From information theory, we know that it is not possible.

To summarize, the bit is not only a binary symbol used to exchange information. It is alsoa unit of measure. It is used to quantify the rate at which a source produces informationand to quantify the ultimate rate at which a physical channel is capable of transmittinginformation reliably when equipped with an appropriate transmitter/receiver pair. Theseare results from information theory. In this text we will not need results from informationtheory but we will often assume that the source is a BSS or equivalent.

1.3 Problem Formulation and Preview

Our focus is on the system aspects of digital point-to-point communications. By the termsystem aspect we mean that we will remain at the level of building blocks rather thangoing into electronic details; Digital means that the message is taken from a nite set of possibilities; and we restrict ourselves to point-to-point communication as it constitutesthe building block of all communication systems.

Digital communications is a rather unique eld in engineering in which theoretical ideasfrom probability theory, stochastic processes, linear algebra, and Fourier analysis havehad an extraordinary impact on actual system design. The mathematically inclined willappreciate how these theories seem to be so appropriate to solve the problems we will

8/12/2019 PDC_Chap1

19/26

8/12/2019 PDC_Chap1

20/26

10 Chapter 1.

Thus it can be modeled by a linear lter as shown in the gure. 3 Additional lteringmay occur due to the limitations of some of the components at the sender and/or atthe receiver. For instance, this is the case of a linear amplier and/or an antenna forwhich the amplitude response over the frequency range of interest is not at and thephase response is not linear. The lter in Figure 1.2 accounts for all linear time-invarianttransformations that act upon the communication signals as they travel from the senderto the receiver. The channel model of Figure 1.2 is meaningful for both wireline andwireless communication channels. It is referred to as the band-limited Gaussian channel .

Mathematically, a transmitter implements a one-to-one mapping between the message setand a set of signals. Without loss of essential generality, we may let the message set beH = {0, 1, . . . , m 1} for some integer m 2 . For the channel model of Figure 1.2the signal set W = {w0 (t), w1 (t), . . . , wm 1 (t)} consists of continuous and nite-energysignals. We think of the signals as stimuli used to excite the channel input, chosen insuch a way that from the channels reaction at the output the receiver can tell, with high

probability, which stimulus was applied.Even if we model the source as producing an index from H = {0, 1, . . . , m 1} ratherthan a sequence of bits, we can still measure the communication rate in terms of bitsper second (bps). In fact the elements of the message set may be labeled with distinctbinary sequences of length log2 m . Hence every time that we communicate a message,we equivalently communicate that many bits. If we can send a signal every T seconds,then the message rate is 1 /T [messages per second] and the bit-rate is (log 2 m)/T [bitsper second].

Digital communication is a eld that has seen many exciting developments and is still invigorous expansion. Our goal is to introduce the reader to the eld, with emphasis onfundamental ideas and techniques. We hope that the reader will develop an appreciationfor the tradeo ff s that are possible at the transmitter, will understand how to design (atthe building block level) a receiver that minimizes the error probability, and will be ableto analyze the performance of a point-to-point communication system.

3 If the scattering and reecting objects move with respect to the transmit/receive antenna, then thelter is time-varying. We do not consider this case.

8/12/2019 PDC_Chap1

21/26

1.3. Problem Formulation and Preview 11

Encoder

Decoder

TRANSMITTER

RECEIVER

WaveformFormer

n -TupleFormer

Up-Converter

Down-

Converter

R (t )

N (t )

PassbandWaveforms

BasebandWaveforms

n -Tuples

Messages

Figure 1.3: Decomposed transmitter and receiver.

We will discover that a natural way to design, analyze, and implement a transmit-ter/receiver pair for the channel of Figure 1.2 is to think in terms of the modules shown inFigure 1.3. Like in the OSI layering model, peer modules are designed as if they were con-nected by their own channel. The bottom layer reduces the bandpass channel to the morebasic baseband channel. The middle layer further reduces the channel to a discrete-timechannel that can be handled by the encoder/decoder pair.

We conclude this introduction with a very brief overview of the various chapters.

Chapter 2 addresses the receiver design problem for discrete-time observations , in par-ticular in relationship to the channel seen by the top layer of Figure 1.3, which is thediscrete-time additive white Gaussian noise (AWGN) channel. Throughout the text thereceivers objective will be to minimize the probability of an incorrect decision.In Chapter 3 we upgrade the channel model to a continuous-time AWGN channel. Wewill discover that all we have learned in the previous chapter has a direct application forthe new channel. In fact, we will discover that without loss of optimality, we can insertwhat we call waveform former at the channel input and the corresponding n -tuple formerat the output and, in so doing, we turn the new channel model into the one alreadyconsidered.

8/12/2019 PDC_Chap1

22/26

12 Chapter 1.

Chapter 4 develops intuition about the high-level implications of the signal set used tocommunicate. It is in this chapter that we start shifting attention from the problem of designing the receiver for a given set of signals to the problem of designing the signal setitself.

The next two chapters are devoted to practical signaling. In Chapter 5 we focus on thewaveform former for what we call symbol-by-symbol on a pulse train . Chapter 6 is a casestudy on coding . The encoder will be of convolutional type and the decoder will be basedon the Viterbi algorithm .

Chapter 7 is about passband communication. A typical passband channel is the radiochannel. What we have learned in the previous chapters can, in principle, be applieddirectly to passband channels; but there are several reasons in favor of a design thatconsists of a baseband transmitter followed by an up-converter that shifts the spectrumto the desire frequency interval. The receiver reects the transmitters structure. Anobvious advantage of this approach is that we decouple most of the transmitter/receiverdesign from the center frequency of the transmitted signal. If we decide to shift the centerfrequency, like when we change channel in a walkie-talkie, we just act on the up-converterand on the corresponding structure of the receiver, and this can be done very easily.Furthermore, having the last stage of the transmitter operate in its own frequency bandprevents the output signal from feeding back over the air into the earlier stages andcreate the equivalent of the annoying audio feedback that occurs when we put a mikenext to the corresponding speaker.

As it turns out, the best way to design and analyze passband communication systems is toexploit the fact that real-valued passband signals can be represented by complex-valuedbaseband signals. This is the reason that throughout the text we learn to work withcomplex-valued signals.

8/12/2019 PDC_Chap1

23/26

1.4. Digital vs Analog Communication 13

1.4 Digital vs Analog Communication

The meaning of digital versus analog communication needs to be claried, in particularbecause it should not be confused with their meaning in the context of electronic circuits.We can communicate digitally by means of analog or digital electronics and the same istrue for analog communication.

We speak of digital communication when the message to be communicated is one of a niteset of possible choices. For instance, if we communicate 1000 bits, we are communicatingone out of 21000 possible binary sequences of length 1000. To communicate our choice,we use signals that are appropriate for the channel at hand. No matter which signals weuse, the result will be digital communication. One of the simplest ways to do this is thateach bit determines the amplitude of a carrier over a certain duration of time. So the rstbit could determine the amplitude from time 0 to T , the second from 2T to 3T , etc.

This is the simplest form of pulse amplitude modulation. There are many sensible waysto map bits to waveforms that are suitable to a channel, and whichever way we choose itwill be a form of digital communication.

We speak of analog communication when the choice to be communicated is one of acontinuum of possibilities. This choice could be the signal at the output of a microphone.Any tiny variation of the signal can constitute another valid signal. Two popular waysto do analog communication are amplitude modulation (AM) and frequency modulation(FM): In AM we let the amplitude of a carrier be a function of the information signalsamplitude. In FM it is the carriers frequency that varies as a function of the informationsignal.

The diff erence between analog and digital communication might seem to be minimal atthis point, but actually it is not. It all boils down to the fact that in digital communicationthe receiver has a chance to exactly reconstruct the choice. The receiver knows that thereis a nite number of possibilities to choose from. The signals used by the transmitter arechosen to facilitate the receivers decision. One of the performance criteria is the errorprobability, and we can design systems that have such a small error probability that forall practical purposes it is zero. The situation is quite di ff erent in analog communication.As there is a continuum of possible source signals, roughly speaking, the transmitter hasto describe the source signal in all its details. The channel noise will alter the description.Any change, no matter how small, will correspond to the description of an alternativesource signal. There is no chance for the receiver to reconstruct an exact replica of theoriginal. It no longer makes sense to talk about error probability. If we say that an erroroccurs every time that there is a di ff erence between the original and the reproduction,then the error probability is 1.

The diff erence, which may still seem a detail at this point, is made signicant by thenotion of channel capacity discussed in Section 1.2. Recall that for every channel there isa largest rate below which we can make the error probability as small as desired and abovewhich it is impossible to reduce the error probability below a certain value. Now we can

8/12/2019 PDC_Chap1

24/26

14 Chapter 1.

see where the diff erence between analog and digital communication becomes fundamental.For instance, if we want to communicate at 1 giga bit per second (Gbps) from Zurich toLos Angeles by using a certain type of cable, we can cut the cable into pieces of lengthL , chosen in such a way that the channel capacity of each piece is greater than 1 Gbps.We can then design a transmitter and a receive that allow us to communicate virtuallyerror-free at 1 Gbps over distance L . By concatenating many such links, we can coverany desired distance at the same rate. By making the error probability over each linksuffi ciently small, we can meet the desired end-to-end probability of error. The situation isvery diff erent in analog communication, where every piece of cable contributes irreversiblyto degradation.

Need another example? Compare faxing a text to sending an e-mail over the same tele-phone line. The fax uses analog technology. It treats the document as a continuum of graylevels (in two dimensions). It does not di ff erentiate between text or images. The receiverprints a degraded version of the original. And if we repeat the operation multiple times

by re-faxing the latest reproduction it will not take long until the result is dismal. E-mailon the other hand is a form of digital communication. Most of the time, the receiverreconstructs an identical replica of the transmitted text.

Because we can turn a continuous-time source into a discrete one, as described in Sec-tion 1.2, we always have the option of doing digital rather than analog communication.In the conversion from continuous to discrete, there is a deterioration that we controland can make as small as desired. The result can, in principle, be communicated overunlimited distance and over arbitrarily poor channels with no further degradation.

1.5 A Few AnecdotesThis text is targeted mainly at engineering students. Throughout their careers some willmake inventions that may or may not be successful. After reading The Information:A History, a Theory, a Flood by James Gleick 4 [3], I felt that I should pass on someanecdotes that nicely illustrate one point, specically that no matter how great an ideaor an invention is, there will be people that will criticize it.

The printing press was invented by Johannes Gutenberg around 1440. It is now recognizedthat it played an essential role in the transition from medieval to modern times. Yet in the16th century, the German priest Martin Luther decried that the multitude of books [were]a great evil; in the 17th century, referring to the horrible mass of books Leibniz feareda return to barbarism for in the end the disorder will become nearly insurmountable; in1970 the American historian Lewis Mumford predicted that the overproduction of bookswill bring about a state of intellectual enervation and depletion hardly to be distinguishedfrom massive ignorance.

4 A copy of the book was generously o ff ered by our dean, Martin Vetterli, to each professor as a 2011Christmas gift.

8/12/2019 PDC_Chap1

25/26

1.5. A Few Anecdotes 15

The telegraph was invented by Claude Chappe during the French Revolution. A telegraphwas a tower for sending optical signals to other towers in line of sight. In 1840 measure-ments were made to determine the transmission speed. Over a stretch of 760 Km, fromToulon to Paris comprising 120 stations, it was determined that two out of three messagesarrived within a day during the warm months and that only one in three arrived in winter.This was the situation when F. B. Morse proposed the French government a telegraphthat used electrical wires. Morses proposal was rejected beause No one could interferewith telegraph signals in the sky, but wire could be cut by saboteurs. [ 3, Chapter 5].

In 1833 the lawyer and philologist John Pickering, referring to the American version of theFrench telegraph on Central Wharf (a Chappe-like tower communicating shipping newswith three other stations in a twelve-mile line across Boston Harbor) asserted that Itmust be evident to the most common observer, that no means of conveying intelligencecan ever be devised, that shall exceed or even equal the rapidity of the Telegraph, for,with the exception of the scarcely perceptible relay at each station, its rapidity may be

compared with that of light itself. In todays technology we can communicate over opticalber at more than 10 12 bits per second, which may be 12 orders of magnitude faster thanthe telegraph referred to by Pickering. Yet Pickerings awed reasoning may have seemedcorrect to most of his contemporaries.

The electrical telegraph eventually came and was immediately a great success, yet somefeared that it would put newspapers out of business. In 1852 it was declared that All ideasof connecting Europe with America, by lines extending directly across the Atlantic, is ut-terly impracticable and absurd. Six years later Queen Victoria and President Buchananwere communicating via such a line.

After the telegraph came the telephone. The rst experimental applications of the elec-trical speaking telephone were made in the US in the 1870s. It quickly became a greatsuccess in the US, but not in England. In 1876 the chief engineer of the General PostOffi ce, William Preece, reported to the British Parliament: I fancy the descriptions weget of its use in America are a little exaggerated, though there are conditions in Americawhich necessitate the use of such instruments more that here. Here we have a superabun-dance of messengers, errand boys and things of that kind . . . . I have one in my o ffi ce, butmore for show. If I want to send a messageI use a sounder or employ a boy to take it.

Compared to the telegraph, the telephone looked like a toy because any child could useit. In comparison, the telegraph required literacy. Business people rst thought that the

telephone was not serious. Where the telegraph dealt in facts and numbers, the telephoneappealed to emotions. Seeing information technology as a threat to privacy is not new.Already at the time one commentator said, No matter to what extent a man may close hisdoors and windows, and hermetically seal his key-holes and furnace-registers, with towelsand blankets, whatever he may say, either to himself or a companion, will be overheard.

In summary, the printing press has been criticized for promoting barbarism; the electricaltelegraphy for being vulnerable to vandalism, a threat to newspapers, and not superior tothe French telegraph; the telephone for being childish, of no business value, and a threat

8/12/2019 PDC_Chap1

26/26