Intro Digital Communication

8/10/2019 Intro Digital Communication

1/33

1

DIGITAL COMMUNICATIONS(ADVANCED LEVEL)

Chapter 1:An Introduction to Digital Communications

Lectured by Assoc. Prof. Dr. Thuong Le-TienNational Distinguished Lecturer

Cell: 0903 787 989Email: [email protected]

August, 2014


2/33

2

Historical Background

*Information theory and coding:

Shannons 1948 paper was followed by three

ground-breaking advances in coding theory

1. Development of the first nontrivial error correcting codesby Golay 1949 and Hamming 1950

1. Development of Turbo Codes by Berou, Glavieux and

Thitimjshima 1993 provide near-optimum error-correcting

coding and decoding performance in additive white

Gaussian noise (AWGN)

1. Rediscovery of Low Density Parity Check (LDPC) codes

(by first original by Gallager 1962) by Tanner 1981


3/33

3

The Internet::

Advanced Research Project Agency Network (ARPANET) 1971 by

US Department of Defence* The pioneering work in packet switching was done on the ARPANET

1985: ARPANET renamed the Internet

Wireless Communications

1864: James Clerk Maxwell formulated the elctromagnetic theoryof light and predicted the existence of radio waves; then set four

equations connect electric and magnetic quantities

1884 Henrich Hertz demonstrated the existence of radio waves

experimentally

Dec 12, 1901, Guglielmo Marconi received a radio signal at Signal

Hill in Newfoundland from Cornwall England (2100miles away)

1906, Fessenden, a self-educated academic, made history by

conducting the first radio broadcast, transmitting music and voice (AM)

* 1988, the first digital cellular system in Europe GSM and AMPS in US


4/33

4

There are two basic models of communications* Broadcasting* Point-to-point

Transmitter*Modulation

*Coding

Channel*Attenuation

*Noise*Distortion*Interference*Fading

Receiver*Detection (Demod+Decod)

*Filtering (Equalization)


5/33

5

(a) FDMA (b) TDMA) (b) CDMA (freq.-hop)

In multiple access, same channel is used to transmit multiple informationchannels transported by multiple messages to different users

TDMA (Time division multiple access), users occupy different time slotFDMA (Freq. division multiple access), users occupy different freq. bands

CDMA (Code division multiple access), users occupy the same frequencyband but modulate their messages with different codes

WDMA (Wavelength division multiple access): another category ofFDMA but used in optical communications

SDMA (Space division multiple access)

MULTIPLE ACCESS TECHNIQUE


6/33

6

Communication Networks

Operated by OpenSystem Interconnection(OSI)composed of 7 layers

7.

6.

5.

4.

3.

2.

1.


7/33

7


8/33

8

Digital Communications

There are 3 layers of the OSI model where itcan effect the design of DCS

Physical Layer: communications between nodes

through MODEMData-link Layer: Error Detection and Correction; a

portion of the data link layer, called the Medium

access control (MAC) sub-layer, allowing frames to

be send over the shared transmission channel

Network Layer: Routing, quality of services

and Flow Control


9/33

9

General Block Diagram of a DCS


10/33

10

Important features of a DCS:

Transmitter sends a waveform from a finiteset of possible waveforms during a limitedtime

Channel distorts, attenuates the transmitted

signal and adds noise, interferences to it. Receiver decides which waveform was

transmitted from the noisy received signal

Probability of erroneous decision (or BER) is

an important measure for the systemperformance


11/33

11

Digital versus analog

Advantages of digital communications: Regenerator at receiver

Different kinds of digital signal are treated

identically.

Data

Voice

Media

Propagation distance

Original

pulse

Regenerated

pulse

A bit is a bit!


12/33

12

Shannons information capacity theorem

Bit error rate (BER) The information capacity of channel or the

maximum rate of transmission withouterrors:

C=B log2(1+SNR)=3.32B log10(1+SNR) b/s

The efficiency of Dig-Com uses =R/C withR is the sampling rate.

C provide the approach of trade-offbetween B and received SNR


13/33

13

Classification of signals

Deterministic and random signals

Deterministic signal: No uncertainty with

respect to the signal value at any time.

Random signal: Some degree of uncertaintyin signal values before it actually occurs.

Thermal noise in electronic circuits due to

the random movement of electrons

Reflection of radio waves from differentlayers of ionosphere


14/33

14

(a) Transmitted signal(b) Effects of distortion(c) Effects of interference(d) Effects of noise


15/33

15

Periodic and non-periodic signals

Analog and discrete signals

A discrete signal

Analog signals

A non-periodic signalA periodic signal


16/33

16

Energy and power signals

A signal is an energy signal if, and only if, it has

nonzero but finite energy for all time:

A signal is a power signal if, and only if, it has

finite but nonzero power for all time:

General rule: Periodic and random signals are power

signals. Signals that are both deterministic and non-periodic are energy signals.


17/33

17

Random process

A random process is a collection of time functions, orsignals, corresponding to various outcomes of a

random experiment. For each outcome, there exists a

deterministic function, which is called a sample function

or a realization.

Sample functions

or realizations

(deterministic

function)

Randomvariables

time (t)

Realnumber


18/33

18

Strictly stationary: If none of the statistics of the random processare affected by a shift in the time origin.

Wide sense stationary (WSS): If the mean and autocorrelationfunction do not change with a shift in the origin time.

Cyclostationary: If the mean and autocorrelation function areperiodic in time.

Ergodic process: A random process is ergodic in mean andautocorrelation, if

and

, respectively.


19/33

19

Autocorrelation

Autocorrelation of an energy signal

Autocorrelation of a power signal

For a periodic signal:

Autocorrelation of a random signal

For a WSS process:


20/33

20

Spectral density

Energy signals:

Energy spectral density (ESD):

Power signals:

Power spectral density (PSD):

Random process: Power spectral density (PSD):


21/33

21

Properties of an autocorrelation function

For real-valued (and WSS in case of

random signals):

1. Autocorrelation and spectral density form

a Fourier transform pair.2. Autocorrelation is symmetric around zero.

3. Its maximum value occurs at the origin.

4. Its value at the origin is equal to the

average power or energy.


22/33

22

Noise in communication systems

Thermal noise is described by a zero-mean Gaussianrandom process, n(t).

Its PSD is flat, hence, it is called white noise.

[w/Hz]

Probability density function

Power spectral

density

Autocorrelation

function


23/33

23

Signal transmission throughlinear systems

Deterministic signals:

Random signals:

Ideal distortionless transmission:All the frequency components of the signal not only arrive

with an identical time delay, but also are amplified orattenuated equally.

Input Output

Linear system


24/33

24

Bandwidth of signal

Baseband versus bandpass:

Baseband

signal

Bandpass

signal

Local oscillator


25/33

25

Different definition of bandwidth:

a) Half-power bandwidth

b) Noise equivalent bandwidthc) Null-to-null bandwidth

d) Fractional power containment bandwidth

e) Bounded power spectral densityf) Absolute bandwidth

(a)

(b)

(c)(d)

(e)50dB


26/33

26

Mediums and Electromagnetic Spectra


27/33

27

Modeling Transmission Channels

Channel transfer function

/linear/nonlinear

Channel transfer function/linear/nonlinear +

( )n t

( ) ( ) ( ) ( )r t s t c t n t (AWGN channel (usually transfer

function is linear) and n(t) is Gaussian,white noise)

( )s t

channel

( ) ( ) ( ) ( ) ( ) ( )u

r t s c t n t s t c t dt n t

Channels as radio path (wireless cellular channel,

microwave link, satellite link); sounds in underwater linkor in wireline channels as coaxial cable, fiber optic cableor wave guides.

Most common channels are linearAdditive, WhiteGaussian Noise (AWGN) channels or linear fadingchannels

Note that the AWGN channel output is convolution ofchannel impulse response c(t) and channel input signals(t) and has the noise termn(t) as additive component:


28/33

28

Linear and Nonlinear Channels

( )iv t

( )ov t

( )iv t

( )ov t

Linear channel Nonlinear channel

1

( ) ( )N

u

o o u i

u

v t a a v t

with ( ) sin( ), 2iv t t N produces 0 1 2( ) sin( ) / 2(1 cos(2 ))ov t a a t a t

0( ) ( )iv t Kv t M Linear channels:

generate never new frequency components characterized by transfer function

Non-linear systems:

characterized by transfer characteristics

Note: Often non-linearity in transmission is generated by transmitter or

receiver, not by the channel itself

Non-linear systems can generate new frequency components, example:


29/33

29

Time-variable

Channel Most information channels are time-variable (fading) channels:

cable, microwave link, cellular channel. Received signal is

In frequency domain, (in differential time instant) there exists a

frequency response and for this instance we maywrite

Channel variations / transmission errors compensated at the

receiver:

equalization flattens frequency response (tapped delay line,

decision feedback equalizer (DFE))

equalization assisted by channel estimation

channel errors can be compensated by channel coding (block

and convolutional codes)

( ) ( ) ( ) ( ; )r t n t s t c t

1 1( ; ) ( )C f C f

1 1( ) ( ) ( ) ( )R f N f S f C f


30/33

30

Interleaving In fading channels, received data can experience burst

errors that destroy large number of consecutive bits.

This is harmful in channel coding

Interleaving distributes burst errors along data stream

A problem of interleaving is

introduced extra delay Example below shows block

interleaving:time

received

power

Reception after

fading channel

1 0 0 0 1 1 10 1 0 1 1 1 0

0 0 1 1 0 0 1

1 0 0 0 1 1 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1

1 0 0 0 1 0 0 0 1 0 1 1 1 1 0 1 1 0 1 0 1

Received interleaved data:

Block deinterleaving :

Recovered data:


31/33

31

Unmodulated and

Modulated Sinusoidals

( ) co (s( )) cx t A t t t Amplitude modulation

(AM)...,

Amplitude Shift Keying

(ASK)...

Frequency modulation

(FM),

Frequency/Phase Shift

Keying (FSK,PSK)...

Carrier-term

some digital carriers [5]unmodulated sinusoidal

The unmodulated sinusoidal wave is

parameterized by constant amplitude,

frequency and phase

In unmodulated sinusoidal all parameters known, conveys no information

Mathematically and experimentally convenient formulation whose

parameterization by variables enables presenting all carrier wave

modulation formats by


32/33

32

Coding

Channel coding is done ... For detection and/or correction of errors produced by the

channel (as block and convolutional coding) by

noise

interference

distortion

linear nonlinear

To alleviate synchronization problems (as Manchester coding)

To alleviate detection problems (as differential coding)

To enable secrecy and security (as scrambling or ciphering)

Channel coding principles:

ARQ (Automatic Repeat Request) as go-back-N ARQ

FEC (Forward Error Correction) as block & convolutional coding


33/33

33

Coding is classified to two flavors

source coding: makes transmitted bits equal probable -maximizes channel capacity

channel coding: protects message & adapts it to channel

Channel coding means adding extra bits for message for error

detection and/or correction

In systematic coding message bits remain the same in codedword:

In coded systems soft decision can be used that calculates the

distance of the received code word to the allowed code words forinstance by using a least-square metric

Message bitsError detection

/correction bits

Intro Digital Communication

Documents

Transcript of Intro Digital Communication