Marc MoonenDept. E.E./ESAT, K.U.Leuvenmarc.moonen@esat.kuleuven.ac.bewww.esat.kuleuven.ac.be/sista/~moonen/ Module-3 : Transmission

Module-3 : Transmission

Marc Moonen

Dept. E.E./ESAT, K.U.Leuven

marc.moonen@esat.kuleuven.ac.be

www.esat.kuleuven.ac.be/sista/~moonen/

Module-3 : Transmission

• Module 1: Introduction to Telecommunications & Networks

• Module 2: Telecommunication Networks and Technologies

• Module 3: Transmission Techniques

• Module 4: Transmission of Sound, Video & Data

Aims/Scope

• Basic Digital Communication principles

modulation/demodulation, detection

(for the regraders/sidegraders)

• New/Advanced Topics

CDMA, multicarrier modulation, smart antennas

(for the regraders/upgraders)

• Mostly `bird’s-eye view’

skip mathematical details (if possible) selection of topics (non-exhaustive)

New/Advanced Topics?

• Analog & 1G Digital Communication Systems:

communication over fairly `simple’ (e.g. AWGN) channels emphasis on modulation/demodulation/timing/etc... circuitry

• Present Day/Future Communication Systems = box full of mathematics & signal processing

for communication over highly bandwidth constrained channels, fading channels, etc...

New/Advanced Topics ?

• Example: Telephone Line Modems

voice-band modems : up to 56kbits/sec in 0..4kHz band ADSL modems : up to 8Mbits/sec in 30kHz…1MHz band VDSL modems : up to 52Mbits/sec in …10MHz band

xDSL communication impairments:

channel attenuation/distortion, echo, cross-talk, RFI,...

see Lecture-7/8

New/Advanced Topics?

• Example : Wireless Communications

Typical spectral efficiency : ...1 bits/sec/Hz MIMO-transmission (`smart antennas’ & co):

example : V-BLAST (Lucent Techn. 1998) …40bits/sec/Hz

exploits a `rich scattering environment’

see Lecture-2, Lecture-10

New/Advanced Topics?

Enabling Technology is

• Signal Processing 1G-SP: analog filters

2G-SP: digital filters, FFT’s, etc.

3G-SP: full of mathematics, linear algebra, statistics, etc...

• VLSI

Overview (I)

• 20/4/2000

Lecture-1 : General Intro

Lecture-2 : Limits of Communication

• 27/4/2000

Lecture-3 : Transmitter Design/Modulation Lecture-4 : Receiver Design/Detection

• 4/5/2000

Lecture-5 : Channel Equalization Lecture-6 : Adaptive Equalization

Overview (II)

• 11/5/2000

Lecture-7 : Multicarrier Modulation (I) Lecture-8 : Multicarrier Modulation (II)

• 18/5/2000

Lecture-9 : Multiple Access/CDMA

Lecture-10: Smart Antennas/MIMO-transmission

Assignments & Exam

• Assignments

Pen & paper exercises Self-study material

• Exam 25/5/2000

• WWW-site : telecom.europace.be

Prerequisites

• Module-1 - M. Goossens : Yes (?)

• Module-2 - P. DeMeester : No

• Digital Communications Background : No (?)

• Mathematics Background : Yes

statistics, linear algebra -> see assignments

• Signal Processing Background : Yes

digital filters, transforms, stochastic processes -> see assignments

Literature

• E.A. Lee & D.G. Messerschmitt

`Digital Communication’ (Kluwer AP 1994)

• J.G. Proakis

`Digital Communications’ (McGraw Hill 1989)

• B. Sklar

`Digital Communications’ (Prentice-Hall 1988)

• S. Haykin

`Communication Systems’ (Wiley 1994)

• H. Meyr, M. Moeneclaey & S. Fechte

`Digital Communication Receivers’ (Wiley 1998)

• etc...

Acknowledgement

Many of the slides/text/figures/graphs are adopted from the handouts of

Module T2

`Digital Communication Principles’

M.Engels, M. Moeneclaey, G. Van Der Plas

1998 Postgraduate Course on Telecommunications

Special thanks to Prof. Marc Moeneclaey

Lecture-1 : General Introduction

• Analog vs. Digital Communication

• Digital Communication Systems Description Transmitter

Channel Receiver

• Preview Lectures 2->10

Analog vs. Digital Communication (I)

Analog Communication:

• Transmission of signals that are inherently analog (speech, video, etc..)

• Baseband or passband (AM, FM, ..)

• Bandwidth = signal bandwidth

Example: speech signal 0..4kHz -> BW=4kHz

• Received signal subject to channel

impairments, transmitter/receiver impairments, etc..

Analog vs. Digital Communication (II)

Digital Communication:

• Transmission of signals that are inherently digital (`data’) or analog (speech, video, etc..)

• Analog signals are converted into digital signals by sampling & quantization (A-to-D conversion)

Example :

- speech 0…4kHz

- sampled at 8kHz (cfr. Nyquist criterion),

- each sample converted into 8 bits number-> 64kbits/sec

=PCM (pulse code modulation)

Analog vs. Digital Communication (III)

Digital Communication

• What?

A principle feature of a digital communication system is that during a finite interval of time, it sends a

waveform from a finite set of possible waveforms.

The objective of the receiver is not to reproduce the transmitted waveform, but (only) to determine

which of the possible waveforms has been sent.

Analog vs. Digital Communication (IV)

Digital Communication Key Features:

• source coding/compression:

Example: speech signal

64kbits/sec-> 11kbits/sec…4kbits/sec (through `signal modeling’)

• channel coding/error correction see also Module- 4

• increased spectral efficiency through coding, signal processing, etc.

Example: v.34 voice-band modem

33.6 kbits/sec in 4kHz voice-band (=8bits/sec/Hz)

Digital Communication System (I)

• Block Diagram

• Digital Information is digital signal (data) or

`sampled+quantized’ analog signal (speech,..)

digital

information s(t) r(t)

digital information channel

Tx Rx

Transmitter with D-to-A

Receiver with A-to-D continuous-time

channel

Digital Communication System (II)

Transmitter

• converts bit sequence into waveform s(t) (=`modulation’)

• bits are grouped into `symbols’

(n bits per symbol, hence M=2^n different symbols) (=`symbol alphabet’, `constellation’)

• each symbol corresponds to a different waveform segment

• symbol rate = # transmitted symbols/sec = Rs

(`Baud rate’, after Baudot, French telegraph engineer)

Digital Communication System (III)

Channel

• physical medium :

twisted pair, coax, optical fiber, radio

• channel impairments :

noise, attenuation/distortion, cross-talk, interference, etc…

) (

)

( t s t

r 

Digital Communication System (IV)

Receiver

• Converts received signal r(t) into bit sequence (=`demodulation/detection’)

• Receiver performance :

Bit Error Probability (BEP) or Bit Error Rate (BER) BER = (#bit errors) / (#transmitted bits)

example : voice : BER <1E-3 data : BER <1E-10

Transmitter (I)

• Transmitted bits are grouped into symbols

(n bits per symbol, hence M=2^n symbols)

• Transmitted symbols are

• Transmitted signal is

where p(t) is transmit pulse, and is symbol energy

,....

, ,

,

1

a a a

a

 ^

^

^

^



Es

a





k

k s

s p t kT a

E t

s( ) . ( ; )

Transmitter (II)

• Transmitted signal is

• Linear modulation (e.g. PAM, QAM, PSK)

all signal segments are proportional to the same pulse p(t)

see Lecture-3 for pulse design

• Non-linear modulation (e.g. FSK)





k

s k

s a p t kT

E t

s( ) . . ( )





k

k s

s p t kT a

E t

s( ) . ( ; )

empha

sis o

n this

see a

ssignm

ents

Transmitter (III)

• Constellations for linear modulation

(=`symbol alphabet’)

PAM PSK QAM

pulse amplitude modulation phase-shift keying quadrature amplitude modulation

4-PAM (2bits) 8-PSK (3bits) 16-QAM (4bits)

ps: complex constellations for passband transmission (see Lecture-3)

I

R

I I

R R

Channel (I)

Channel impairments:

• attenuation/distortion (linear/non-linear)

• noise (linear/non-linear)

• cross-talk (1 or many)

• echo (e.g. hybrid impedance mismatch)

• RFI (e.g. amateur radio)

Channel (II)

• Mostly simple linear channel models

• Example: AWGN-channel

(additive white Gaussian noise channel)

n(t) is zero-mean Gaussian process with power spectrum No/2 for |f|<B (B=bandwidth)

r(t)=Ho.s(t)+n(t)

channel

s(t)

n(t)

Ho +

Channel (III)

• PS: Gaussian noise model justified through central limit theorem (ex: 1 cross-talker is non-Gaussian, 30 cross-talkers approx. Gaussian)

• PS: `White’ actually means `white within useful bandwidth’ i.o. truly

`white’ (->infinite power hence ill-defined)

• Example: frequency-selective channel

frequency-dependent channel attenuation/phase distortion (example: twisted pair, coax)

channel n(t) s(t)

R(f)=H(f).S(f)+N(f)

H(f) +

Receiver (I)

• Receiver retrieves transmitted symbols from received signal r(t)

• This leads to an optimization problem Example: minimum distance receiver

where p’(t) is transmit pulse p(t), modified by channel

,....

,

,

1

a a

a





k

s k

s a

a

a _{, , ,...} | r(t) E . a .p'(t kT ) |² dt min 1 2 3

Receiver (II)

• For AWGN channels (<->frequency-selective channels), a receiver may consist of :

- (a front-end `(whitened) matched filter’, WMF)

- a symbol-rate sampler (i.e. 1 sample/symbol interval)

- a (memory-less) decision device that decides on the nearest symbol in the symbol alphabet

• Timing instant for symbol-rate sampling is crucial, hence synchronization scheme needed !

WMF

cture 3-4

r(t) 1/Ts aˆ^k

Receiver (III)

• For frequency-selective channels, the receiver may consist of

- WMF + symbol-rate sampling front-end, or

- anti-alias filtering + Nyquist-rate sampling front-end

followed by more complicated processing:

- Maximum-likelihood sequence estimation (e.g. Viterbi algorithms)

- Equalization + decision device - …

• See Lecture 4-5

Preview Lectures 2->10

Lecture-2 : Limits of Communication

• Given a communication channel, an amount of transmit power and transmit bandwidth, what is the maximum achievable transmission bit- rate (bits/sec), for which the bit-error-rate is sufficiently (infinitely) small ?

• Shannon theory (1948)

• Recent topic: MIMO-transmission (e.g. V-BLAST, cfr. supra)

Preview Lectures 2->10

Lecture-3 : Transmitter Design/Modulation

• Baseband vs passband modulation

• Constellations for linear modulation

• Transmit pulse p(t) design:

`(root) raised cosine pulses’

• Simple receiver structures, eye diagrams, etc.

Preview Lectures 2->10

Lecture-4 : Receiver Design/Detection

• Inter-symbol interference

• Receiver front-ends :

- (whitened) matched filtering + symbol-rate sampling - anti-alias filtering + Nyquist-rate sampling

• Optimum detection

- MAP/Maximum Likelihood-detection - MLSE/Viterbi algorithm

Preview Lectures 2->10

Lecture-5 : Receiver Design/Equalization

• Equalization vs. inter-symbol interference

• Equalizer structures : - Linear equalizers

- Decision-feedback equalizers - Fractionally spaced equalizers

• Design criteria

- Zero-forcing equalization

Preview Lectures 2->10

Lecture-6 : Adaptive Equalization

• Equalization when channel is unknown and/or time- varying

• Least-mean squares algorithm (Widrow 1965) - MMSE and stochastic gradient

• Recursive Least Squares algorithms - Least squares criterion

- Introduction to Fast RLS algorithms

Preview Lectures 2->10

Lecture-7/8 : Multicarrier Modulation

• Applications

- ADSL modems (VDSL modems)

• Combination of frequency-shift keying (FSK) and quadrature amplitude modulation (QAM)

• Multicarrier modulation/demodulation based on fast Fourier transforms IFFT/FFT

• Alleviate (?) equalization problem through usage of cyclic prefix

Preview Lectures 2->10

Lecture-9 : Multiple Access/CDMA

• Multiple Access:

- TDMA/FDMA (e.g. GSM)

- CDMA (e.g. IS-95, 3G mobile comms)

• CDMA code sequences

• CDMA receivers

- Single-user detection - Multi-user detection

Preview Lectures 2->10

Lecture-10 : Smart Antennas/MIMO transmission

• Antenna array receivers

- Beamforming

- Channel modeling

• SDMA : `spatial dvision multiple access’

allows different users to use the same frequencies/codes at the same time. Signal separation performed based on spatial properties.

• MIMO-transmission (e.g. V-BLAST)

Preview Lectures 2->10

Lecture-10 (reserve) : Echo Cancellation

• Echo generation in full-duplex modems

- Line echo

- Acoustic echo

• Echo cancellation

• Adaptive echo cancellation