ELE 745 Digital Communications

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ELE 745 Digital Communications

• Xavier Fernando
• Ryerson Communications Research Lab (RCL)
• http//www.ee.ryerson.ca/courses/ele745

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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

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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
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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)

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International Telecom Market is 2.7 Trillion in
2009
North America 1.2T
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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

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Wireless Leaders - 2009

• China Mobile 60.16 B
• Vodafone 59.60 B
• Telefónica 51.56 B
• T-Mobile/DT 50.16 B
• ATT Mobility 49.34 B

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Part - I

• Digital Communications

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System Overview
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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

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System Overview

• Pulse Modulation
• Generate waveform suitable for transmission
• Bandpass (Passband) Modulation
• Translate the baseband waveform to passband using
  a carrier

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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

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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?)

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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

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An Example
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Basics of Signals
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Deterministic and Random 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

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Periodic signals are everlasting signals
Continuous and discrete time signals
Continuous (time) signal exists in all times
The Unit Impulse Function
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Energy Signal That has finite Energy for all
time
Power Signal That has finite power for all time
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Energy Spectral Density
Since for real signals, X(f) is an even function
of frequency,
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Power Spectral Density (Periodic Signal)
Power
PSD
PSD of an aperiodic signal
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Autocorrelation of a Periodic Signal
Properties 1-3 are the basic properties
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Autocorrelation of an Energy Signal
Properties 1-3 are the basic properties
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Ideal Filters
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Practical Filter
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Baseband and Pass band Spectrum