1. ELE 745 Digital Communications Xavier Fernando Ryerson Communications Research Lab (RCL) http://www.ee.ryerson.ca/~courses/ele745

2. 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

3. 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

4. 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)http://www.bls.gov/oco/ocos027.htm

5. International Telecom Market is $2.7 Trillion in 2009 North America: $1.2T

6. The Wireless Boom n 2.6 billion mobile phone users worldwide today vs. 1.3 billion fixed landline phones vs. 1.5 billion TV sets in use n Expected to grow to 4.1 billion by 2014 n 37% increase in users over next 6 years Source: Telecom Trends International Inc. (February 2008) n 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

7. Wireless Leaders - 2009 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

8. Part - I Digital Communications

9. System Overview

10. 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

11. System Overview Pulse Modulation: ◦ Generate waveform suitable for transmission Bandpass (Passband) Modulation: ◦ Translate the baseband waveform to passband using a carrier

12. 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

13. 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’?)

14. 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

15. An Example

16. Basics of Signals

17. 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 t X(t)

18. The Unit Impulse Function t X(t) Periodic signals are everlasting signals Continuous and discrete time signals Continuous (time) signal exists in all times

19. Energy Signal – That has finite Energy for all time Power Signal – That has finite power for all time

20. Energy Spectral Density Since for real signals, X(f) is an even function of frequency,

21. Power Spectral Density (Periodic Signal) Power PSD PSD of an aperiodic signal

22. Autocorrelation of a Periodic Signal Properties 1-3 are the basic properties

23. Autocorrelation of an Energy Signal Properties 1-3 are the basic properties

25. Ideal Filters

26. Practical Filter

27. Baseband and Pass band Spectrum