Data Communications: Principles, Transmission Media, Encoding, and Synchronization, Lecture notes of Communication

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Lecture 1. Data communications:

Basic concepts, terminology and

theoretical foundations

Objectives

• Improve your knowledge on data communications principles and

foundations

Main Reference

[1] William Stallings, Data and Computer Communications ,

Eighth edition, Pearson Prentice Hall, 2007.

Basic Model

Consists of:

• Source – generates data to be transmitted
• Transmitter – encodes information
• Transmission systems – line or complex network connecting source

and destination

• Receiver – converts the received signal in an interpretable form for

destination

• Destination – takes incoming data

Data transmission

Transmission media :

• Guided – along a physical path, e.g. twisted pair, optical fiber
• Unguided – without a guided path, e.g. wireless

Direct link - path between two devices with no intermediate devices

Guided transmission :

• Point to point – direct link between two and only two devices sharing

the same medium

• Multipoint – more than two devices share the same medium

Transmission type :

• Simplex - transmission in only one direction
• Half-duplex - transmission in both directions but not at the same time
• Full-duplex - transmission in both directions at the same time

Signal representation: periodic signals

If and only if s  t^ ^ T ^  s (^ t ), t ^ ,^ 

s ( t )  A sin 2  ft    e.g. sine wave:

 A – amplitude , peak value (strength)

 f – frequency , repeat rate

 T – period , the amount time until signal repeats

 φ - phase , the relative position in time, within a period

 λ – wavelength , the distance occupied by a single cycle

 v – velocity of the signal (usually we used light speed)   vT

Time-domain representation

The signal is represented as a function of time

Take a look at the following signals in time-domain, and identify

them in plots

 

 

 

 

  

  



 



4

( ) sin 2

( ) sin 2 2

( ) 0. 5 sin 2

( ) sin 2

4

3

2

1

 







s t t

s t t

s t t

s t t

0.5 1 1.5 2

-0.

1

0.5 1 1.5 2

-0.

-0.

0.5 1 1.5 2

-0.

1

0.5 1 1.5 2

-0.

1

0.5 1 1.5 2

-0.

1

Remark : in computer networks and computer science bandwidth (digital

bandwidth) is defined as the capacity for a given system to transfer data

over a connection; and measured as a bit rate expressed in bits per

seconds, e.g. Kb/s Mb/s etc (this is actually the data rate). The previous

definition of bandwidth is often used in signal processing

Spectrum – the range of the frequencies in a signal

Absolute bandwidth – the width of the spectrum

Effective bandwidth (or just bandwidth) – the band of frequencies that

contain most of the energy in the signal

DC component – components that have a zero frequency

Baseband signals – signals whose range of frequencies is measured

from 0, for baseband signals bandwidth is equal to the upper cutoff

frequency

Data rate - in data communication and computing is the quantity of data

that is conveyed or processed per unit of time,

Signal properties

Bandwidth and data rate

Suppose we are transmitting a square wave with f=2 MHz which

corresponds to an alternating sequence of 0’s and 1’s

For the given f we have a data rate of 4Mbps since 2 bits are sent in each

period

6

f

T

s t  ft  sin 2 3 ft 

( )sin 2   

Now suppose we are approximating the square wave with the sum of the

first 2 terms of the Fourier transform, i.e.

0.5 1 1.5 2

-0.

-0.

-0.

A bandwidth of 4Mhz is required since: 3 ft  ft  2 ft  4 Mhz

Maximum data rate on ideal

channels (no noise)

Nyquist has proved that the number of pulses that can be put on a

telegraph line is:

f B P

 2

V

A

M

2B (B- bandwidth) is also called Nyquist rate

Hartley stated that the number of distinct pulses that can be transmitted is

limited by the signal amplitude and precision to distinguish between

different levels of amplitude, i.e.

R f M R B M P 2 2

 log   2 log

From this, the maximum data rate can be computed as follows (Hartley’s

law)

Maximum data rate on noisy

channels

Shannon proved that the maximum data rate on a noisy channel (also

called channel capacity) is:

Where:

• C – channel capacity
• B – bandwidth
• S – signal power
• N – noise power











  

N

S C B log 1 2

S/N is also called signal to noise ratio

By comparing Hartley’s law and Shannon's channel capacity, we can

compute the maximum number of distinguishable levels as:

Example : Consider a 3000 MHz channel bandwidth with 30db signal to

noise ratio, what is the channel capacity? How many distinguishable levels

can be transmitted?

N

S

M  1 

Digital Data – Digital Signal

(Digital Encoding)

Digital signal encoding Characteristics

Nonreturn to Zero-Level (NRZ-L) 0 – high, 1 – low

Nonreturn to Zero Inverted (NRZI) 0 – no transition at the beginning of interval, 1 – transition at the beginning o

interval

Bipolar-AMI 0 – no line signal, 1 – positive or negative level (alternating for successive 1)

Pseudoternary 0 – positive or negative level (alternating for successive 0), 1 – no line signal

Manchester 0 – transition from high to low in the middle of interval, 1 – transition from low

to high in the middle of interval

Differential Manchester Always a transition in middle of interval. 0 – transition at the beginning of

interval, 1 – no transition at the beginning of interval

B8ZS Same as bipolar AMI, except that any string of eight zeros is replaced by a string

with two code violations

HDB3 Same as bipolar AMI, except that any string of four zeros is replaced by a string

with one code violation

Non-return to zero encodings

Non-return-to-Zero-Level (NRZL)

• “0” is represented as one physical level
• “1” is represented as another physical level

Non-return-to-Zero-Inverted (NRZI)

• “0” is represented as no transition
• “1” is represented as a transition

Remark: NRZI is a case of differential encoding (the signal is decoded by

comparing two consecutive signal elements)

For more details and variants see http://en.wikipedia.org/wiki/Non-return-to-

zero

Main limitations: lack of synchronization, presence of a dc component

Polarity of

preceding pulse

Odd number of ones since

previous substitution

Even number of ones since

previous substitution

• 000 - +00+

• 000+ - 00 -

19

Bipolar with 8 zeros substitution (B8ZS)

Intended to overcome the lack of synchronization when 0’s are transmitted:

• An octet of 0’s is represented as 000+- 0 - + if the last voltage pulse was positive
• An octet of 0’s is represented as 000-+0+- if the last voltage pulse was negative

High-density bipolar 3 zeros (HDB3)

Used in Japan, Europe, Australia

A nibble (4 consecutive bits) of 0’s is represented as 0001 or 1001

according to the table

Remark : In order to distinguish real sequences from scrambled sequences,

code violations are forced.

Show what are the code violations for B 8 ZS and HDB 3

Binary encodings (continued)

The Big Picture, cf. [Stallings]