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
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