TRUYỀN THÔNG SỐ DIGITAL COMMUNICATION pptx

Formatting Textual Data Character Coding 2.. Formatting Textual Data Character Coding 2.. Formatting Textual Data Character Coding 2.. Formatting Textual Data Character Coding 2.. Why Ov

TRUYỀN THÔNG SỐ DIGITAL COMMUNICATION

Week 2

1

• Format: The first important step in any DCS:

– Transform the information source to a form

compatible with a digital system

• Pulse modulate: (điều chế xung)

– Transform the digital messages to baseband

waveforms

– Baseband = signal whose spectrum extends from

(or near) DC  some finite value (< a few MHz)

3

Encode Pulse Transmit

Pulse waveforms Bit stream

Content

1 Formatting Textual Data (Character Coding)

2 Messages, Characters, and Symbols

3 Formatting Analog Information

4 Sources of Corruption

5 Pulse Code Modulation

6 Baseband Modulation

5

Content

1 Formatting Textual Data (Character Coding)

2 Messages, Characters, and Symbols

3 Formatting Analog Information

4 Sources of Corruption

5 Pulse Code Modulation

6 Baseband Modulation

6

Character Coding

• The original form of most communicated data =

textual or analog.

• Data = text: Character Coding  digital format

• E.g ASCII (American Standard Code for Information

Interchange), EBCDIC

7

Content

1 Formatting Textual Data (Character Coding)

2 Messages, Characters, and Symbols

3 Formatting Analog Information

4 Sources of Corruption

5 Pulse Code Modulation

6 Baseband Modulation

9

• Characters are encoded into a sequence of bits = “a bit

stream” or “baseband signal”

• Groups of k bits = symbols

• Symbol set size: M = 2 k or M-ary system

– k = 1: binary system

– k = 2: quaternary system

10

11

Example (cont.)

12

Content

1 Formatting Textual Data (Character Coding)

2 Messages, Characters, and Symbols

3 Formatting Analog Information

4 Sources of Corruption

5 Pulse Code Modulation

6 Baseband Modulation

13

Format analog signals

• To transform an analog waveform into a form that is

compatible with a digital communication, the following

steps are taken:

1 Sampling

2 Quantization and encoding

3 Baseband transmission

14

Impulse Sampling

)()

()

x s = δ × X s( f ) = Xδ ( f )∗ X ( f )

| ) (

| Xδ f

| ) (

16

Sampling theorem

• Sampling theorem: A bandlimited signal with no spectral components beyond , can be uniquely

determined by values sampled at uniform intervals of

– The sampling rate, is called

Sampling process

Analog

signal modulated (PAM) signalPulse amplitude

18

Why Oversample?

Transform analog signals to digital signals:

Without Oversampling:

• The signal  high performance analog LPF  sampled at

Nyquist rate  ADC

With Oversampling:

• The signal  low performance (less cost) analog LPF 

sampled at (higher) Nyquist rate  ADC  high performance (low cost) digital filter (resample)

20

• Amplitude quantizing: Mapping samples of a

continuous amplitude waveform to a finite set of

amplitudes.

21

In Out

Content

1 Formatting Textual Data (Character Coding)

2 Messages, Characters, and Symbols

3 Formatting Analog Information

4 Sources of Corruption

5 Pulse Code Modulation

6 Baseband Modulation

22

Sampling and Quantizing Effects

• Quantization Noise: encode PAM signal into a quantized PAM signal

• Quantizer Saturation: The difference between input and

output become large due to input’s range

• Timing Jitter: if there is a slight jitter in the position of the

sample, the sampling is no longer uniform…

24

Channel Effects

• Channel noise

• Inter-symbol Interference (ISI): when the channel BW is close

to the signal BW, the spreading will exceed a symbol duration and cause signal pulses to overlap = ISI

25

Signal-to-Noise Ratio (SNR) for Quantized Pulses

Average quantization noise power

Signal peak power

Signal power to average quantization noise power

26

Quantization levels

Content

1 Formatting Textual Data (Character Coding)

2 Messages, Characters, and Symbols

3 Formatting Analog Information

4 Sources of Corruption

5 Pulse Code Modulation

6 Baseband Modulation

27

– Each quantized sample is digitally encoded into an l bits codeword

where L in the number of quantization levels and

28

( ˆ

)

(

t x t

Statistical of speech amplitudes

• In speech, weak signals are more frequent than strong ones.

• Using equal step sizes (uniform quantizer) gives low for weak signals and high for strong signals.

– Adjusting the step size of the quantizer by taking into account the speech statistics

improves the SNR for the input range

0.0

1.0

0.5

1.0 2.0 3.0Normalized magnitude of speech signal

Uniform and Non-uniform Quantization

– Uniform (linear) quantizing:

– No assumption about amplitude statistics and correlation properties of the input.

– Not using the user-related specifications – Robust to small changes in input statistic by not finely tuned to a specific set of input parameters

– Simply implemented

• Application of linear quantizer:

– Signal processing, graphic and display applications, process control applications

– Non-uniform quantizing:

– Using the input statistics to tune quantizer parameters – Larger SNR than uniform quantizing with same number of levels – Non-uniform intervals in the dynamic range with same quantization noise variance

• Application of non-uniform quantizer:

– Commonly used for speech

32

Non-uniform quantization

• It is done by uniformly quantizing the “compressed” signal

• At the receiver, an inverse compression characteristic, called “expansion” is employed to avoid signal distortion

Content

1 Formatting Textual Data (Character Coding)

2 Messages, Characters, and Symbols

3 Formatting Analog Information

4 Sources of Corruption

5 Pulse Code Modulation

6 Baseband Modulation

34

Baseband transmission

• To transmit information through physical

channels, PCM sequences (codewords) are

transformed to pulses (waveforms).

– Each waveform carries a symbol from a set of size M – Each transmit symbol represents bits of

1 0 1 1 0

0 T 2T 3T 4T 5T

+V -V +V -V +V 0 -V

NRZ-L

Unipolar-RZ

Bipolar-RZ

Bi-Phase-L Delay Modulation Dicode NRZ

36

PCM waveforms …

• Criteria for comparing and selecting PCM waveforms:

– Spectral characteristics (power spectral density and bandwidth efficiency)

– Bit synchronization capability

– Error detection capability

– Interference and noise immunity

– Implementation cost and complexity

37

Spectra of PCM waveforms

38

M-ary pulse modulation

• M-ary pulse modulations category:

• M-ary pulse-amplitude modulation (PAM)

• M-ary pulse-position modulation (PPM)

• M-ary pulse-duration modulation (PDM)

– M-ary PAM is a multi-level signaling where each

symbol takes one of the M allowable amplitude levels,

each representing bits of PCM words.

– For a given data rate, M-ary PAM (M>2) requires less

bandwidth than binary PCM.

– For a given average pulse power, binary PCM is easier

to detect than M-ary PAM (M>2).

M

k = log2

39

PAM example

40