Transcript ELE 745 Digital Communications

ELE 745
Digital Communications
Xavier Fernando
Ryerson Communications Research Lab (RCL)
http://www.ee.ryerson.ca/~courses/ele745
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 everywhere

Wireless 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 more
One fiber can carry up to 6.4 Tb/s or 100 million
conversations simultaneously
Employment Statistics - 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
Telecom Market
is $2.7 Trillion in
2009
North America: $1.2T
The 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 years
Source: 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 - 2009
1.
2.
3.
4.
5.
China Mobile
Vodafone
Telefónica
T-Mobile/DT
AT&T Mobility
60.16 B
59.60 B
51.56 B
50.16 B
49.34 B
Part - I
Digital Communications
System Overview
System Overview

Information 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 Overview

Pulse Modulation:
◦ Generate waveform suitable for transmission

Bandpass (Passband) Modulation:
◦ Translate the baseband waveform to passband using a
carrier
The Channel





Different Channels:Telephone wire, TV (coaxial)
Cable, air (wireless), optical fiber
The 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?


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?


Complete clean-up and regeneration is
possible
Advanced 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 Example
Basics of Signals
Deterministic and Random Signals

Deterministic signals have known value at any time.
Explicit equations can be written
◦ Ex:

X(t)
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
Periodic signals are everlasting signals
Continuous and discrete time signals
Continuous (time) signal exists in all times
The Unit Impulse Function
X(t)
t
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 Filters
Practical Filter
Baseband and Pass band Spectrum