ele 745 digital communications xavier fernando ryerson communications research lab (rcl)...

ELE 745 ELE 745 Digital CommunicationsDigital Communications

Xavier Fernando

Ryerson Communications Research Lab (RCL)http://www.ee.ryerson.ca/~courses/ele745

Why DIGICOM?Why DIGICOM?Basic DIGICOM knowledge is needed

for all electrical/computer engineers◦Power systems rely more & more

communications to become Smart Grids◦Inter chip and intra-chip communications

connect micro electronic systems◦Multimedia, control and instrumentation

systems use communications◦Biomedical engineers use ‘body area

networks’ for communications

DIGICOM is everywhereDIGICOM is everywhereWireless has become a necessity

Wireless LANs, 802.11, 15, 16, Cellular, LTE, 3G, 4G…

Optical Communications: ◦ Almost all phone calls, Most Internet traffic,

and Television channels travels via optical fiber

Copper wires: ◦ Coaxial cable and twisted pair telephone

wires (DSL) are the key for ‘Triple play’ services (voice, data, TV)

Satellite: ◦ GPS, XM radio and lot moreOne fiber can carry up to 6.4 Tb/s or 100

million conversations simultaneously

Employment Statistics - Employment Statistics - 2008 (US)2008 (US)

◦Electrical engineers (power) - 157,800

◦Information and Communication Technology (ICT) engineers - 218,400 Computer hardware - 74,700 others - 143700

◦Biomedical engineers 16,000 (http://www.bls.gov/oco/ocos027.htm)

International International Telecom Telecom Market is Market is $2.7 Trillion $2.7 Trillion in 2009in 2009

North America: $1.2T

The Wireless BoomThe Wireless Boom 2.6 billion mobile phone users worldwide today

• vs. 1.3 billion fixed landline phones• vs. 1.5 billion TV sets in use

Expected to grow to 4.1 billion by 2014 37% increase in users over next 6 yearsSource: Telecom Trends International Inc. (February 2008)

Worldwide RFID revenues estimated to reach $1.2 billion in 2008 • 31% increase over 2007 revenues• Estimated to reach $3.5 billion by 2012

Source: Gartner Research Firm report cited in RFID World February 26, 2008

Wireless Leaders - Wireless Leaders - 20092009

1. China Mobile 60.16 B

2. Vodafone 59.60 B

3. Telefónica 51.56 B

4. T-Mobile/DT 50.16 B

5. AT&T Mobility 49.34 B

Part - IPart - IDigital Communications

System OverviewSystem Overview

System OverviewSystem OverviewInformation Source:

◦Analog (voice) or digital (e-mail, SMS, fax)Source Encoding:

◦Removing redundancy (to reduce bit rate)Encrypt: introduce security (optional)Channel Encoding:

◦Adding redundancy to overcome channel impairments such as noise & distortion

Multiplex: Share the channel with other sources

System OverviewSystem OverviewPulse Modulation:

◦Generate waveform suitable for transmission

Bandpass (Passband) Modulation: ◦ Translate the baseband waveform to

passband using a carrier

The ChannelThe ChannelDifferent Channels: Telephone wire, TV

(coaxial) Cable, air (wireless), optical fiberThe channel adds noise and distortion

◦Often adds white Gaussian noise and called AWGN channel

◦Distortion comes from multipath dispersion (in air), inductance, capacitance etc.

The channel could be stationary (wires) or time varying (wireless)

The channel is usually band-limited (lowpass or bandpass

Optical fiber channel offers huge bandwidth

Why Digital?Why Digital?

Analog receiver need to exactly reproduce the waveform, removing noise and distortion

Digital receiver only need to make a discrete decision (‘0’ or ‘1’?)

Why Digital? Why Digital? Complete clean-up and regeneration is

possibleAdvanced processing is possible, such

as:◦Channel coding (Ex: parity)◦Source coding (compression)◦Encryption & watermarking◦Multiplexing different users (TDMA, CDMA…)◦Multiplexing data from different sources

(voice, video, data, medical…)◦Lossless storing and retrieval◦Much more

An An Example Example

Basics of SignalsBasics of Signals

Deterministic and Random Deterministic and Random Signals Signals

Deterministic signals have known value at any time. Explicit equations can be written◦ Ex:

Random signals are unknown a priory◦ No equations can be written for the waveform◦ Statistical properties (mean, variance etc) are

used◦ Ex: Noise, Information

t

X(t)

The Unit Impulse Function

t

X(t)Periodic signals are everlasting signals

Continuous and discrete time signals

Continuous (time) signal exists in all times

Energy Signal – That has finite Energy for all time

Power Signal – That has finite power for all time

Energy Spectral Density

Since for real signals, X(f) is an even function of frequency,

Power Spectral Density (Periodic Signal)

Power

PSD

PSD of an aperiodic signal

Autocorrelation of a Periodic Signal

Properties 1-3 are the basic properties

Autocorrelation of an Energy Signal

Properties 1-3 are the basic properties

Ideal FiltersIdeal Filters

Practical FilterPractical Filter

Baseband and Pass band Baseband and Pass band SpectrumSpectrum