1. Communication Systems
Introduction:
Communication Systems

2. Biography Min-Goo Kang ’82-’86 B.S. ’87-’89 M.S.
’89-’94 Ph.D. in the Dept. of Electronic engineering, Yonsei University
Professor in the Dept. of Inform. & Telecomm.,
Hanshin University, Osan, Korea, from 2000
’85-’87 Researcher in Samsung
’97-’98 Post Doc. in Osaka University, Japan
’06-’07 Visiting Scholar in Queen’s University, Canada
- Research interests Mobile Telecomm. & DTV. -

3. Contents Communication System Model
Analog-to-Digital (A/D) Conversion : Sampling, Quantization,Coding
Pulse-Code Modulation (PCM) : Line Coding(RZ, NRZ etc.)
CODEC : Souce CODEC(JPEG, MPEG, H.264, MP3,…)
Channel Coding(Viterbi, Turbo, …)  CH3,4,9
Filter : Lowpass Filter(LPF), Highpass Filter(HPF), Bandpass Filter(BPF)
Fourier Series, Fourier Transform :FFT(Frequency Analysis)
Modulation(MODEM) AM(ASK), FM(FM), PM(PSK)  CH11,14

4. Communication System Model
Message signal m(t)
analog or digital
baseband signal
Transmitter
modifies the baseband signal for efficient transmission
Channel
Channel is a medium through which the transmitter output is sent
example: wire, coaxial cable, optical fiber, radio link
Receiver
reprocesses the received signal by undoing the signal modifications made at the transmitter and the channel

5. Typical Digital Communication System

6. Analog-to-Digital (A/D) Conversion
Sampling
Sampling makes signal discrete in time
Sampling theorem says that bandlimited signal can be sampled without introducing distortion
The sample values are still not digital
Quantization
Quantizer makes signal discrete in amplitude
Quantizer introduces some distortion (“quantization noise”)
Good quantizers are able to use few bits and introduce small distortion
Inherently digital information (e.g. computer files) do not require sampling or quantization.

7. Sampling

8. Quantization

9. Pulse-Code Modulation (PCM)
Multi-amplitude pulse code

10. Channel Effects Distortion
attenuation, noise, fading
Simple channel model: additive white Gaussian noise (AWGN)
transmitted signal
received distorted signal (without noise)
received distorted signal (with noise)
regenerated signal (delayed)

11. Source Coding and Channel Coding
Compression of digital data to eliminate redundant information
Channel Coding
Provides protection against transmission errors by selectively inserting redundant data

12. SNR, Bandwidth, Rate of Communication
Fundamental parameters that control the rate and quality of information transmission are the channel bandwidth B and the signal power S.
Channel Bandwidth B
Range of frequencies that the channel can transmit with reasonable fidelity

13. SNR, Bandwidth, Rate of Communication
Signal Power S
Signal power is related to the quality of transmission
Increasing S reduces the effects of channel noise, and the information is received with less uncertainty
In any event, a certain minimum SNR is necessary for communication
Channel bandwidth B and signal power S are exchangeable;
- We can trade S for B, or vice versa.
- One may reduce B if one is willing to increase S.
Example: PCM with 16 quantization levels
- multi-amplitude scheme
- binary scheme

14. SNR, Bandwidth, Rate of Communication
Rate of Information Transmission C
Channel capacity : maximum number of bits that can be transmitted per second with a probability of error arbitrarily close to zero
Shannon’s limit
The channel capacity is related to channel bandwidth and signal power.
It is impossible to transmit at a rate higher than channel capacity without incurring errors.

15. Modulation
Converts digital data to a continuous waveform suitable for transmission over channel
Baseband (usually square waveform): “line coding”
Bandpass (usually sinusoidal waveform): “bandpass modulation”
Carrier Modulation (bandpass modulation)
Information is transmitted by varying one or more parameters of the carrier waveform: amplitude, frequency, phase
A carrier is a sinusoid of high frequency, and one of its parameters (amplitude, frequency, phase) is varied in proportion to the baseband signal (message signal).

16. Line Coding

17. Modulation

18. Modulation Examples of Digital Modulation
1) Amplitude Shift Keying (ASK) or
ON/OFF Keying (OOK):
2) Phase Shift Keying (PSK):
3) Frequency Shift Keying (FSK):

19. Bit Error Probability for Binary Systems

20. Good Communication System
Large data rate (measured in bits/sec), R
Small bandwidth (measured in Hertz), B
Small required signal power (measured in Watts or dBW), or equivalently small required Eb/N0
Low distortion (measured in S/N or probability of bit error)
Large system utilization: large number of users with small delay
Low system complexity, computational load, and system cost
With digital communications, high complexity does not always result in high cost
In practice, there are tradeoffs made in achieving these goals

21. Data Rate vs. Bandwidth
Data rate R ↑  data pulse width ↓  bandwidth B ↑
This tradeoff cannot be avoided - however, some systems use bandwidth more efficiently than others.
Define Bandwidth Efficiency as the ratio of data rate R to bandwidth B: hB = R / B
We want large bandwidth efficiency hB

22. BER vs. Signal Power
One way to get low probability of error would be to use large signal power to overcome the effect of noise.
Some types of modulation achieve low probability of error at lower power than others.
Define hE for Energy Efficiency :
We want small

23. Tradeoff in System Design
Tradeoff between bandwidth efficiency and energy efficiency
M-ary modulation
Binary modulation sends only one bit per use of the channel.
M-ary modulation sends multiple bits, but is more vulnerable to noise.
Error correction coding
Inserting redundant bits improves BER performance, but increases bandwidth

24. Why Digital Communication?
Any noise introduces distortion to an analog signal. Since a digital receiver needs only distinguish between two waveforms it is possible to exactly recover digital information.
Many signal processing techniques are available to improve system performance: source coding, channel coding, equalization, encryption.
Digital ICs are inexpensive to manufacture: a single chip can be mass produced at low cost, no matter how complex.
Digital communication allows integration of voice, video, and data on a single system.
Digital communication systems provide a better tradeoff of bandwidth efficiency and energy efficiency than analog systems.

25. Fourier Series
Example (pulse train)
 Line spectrum

26. Fourier Series

27. Fourier Series Example (pulse train): in terms of frequency
 Line spectrum

28. Fourier Series

29. Fourier Series
Sampling function and sinc function

30. Fourier Series

31. Fourier Series
Sampling function and sinc function

32. Fourier Series
Pulse Signals

33. Fourier Series

34. Fourier Series
impulse train

35. Linear System & Fourier Series
System Analysis
For periodic input: h

36. Fourier Transform :FFT (Fast Fourier Transform :Frequency Analysis)

37. Fourier Transform
As the fundamental period of the time waveform increases, the fundamental frequency of the Fourier series components making up the waveform decreases and the harmonics become more closely spaced.
In the limit, as the time between pulses approaches infinity, the harmonic spacing becomes infinitely small and the spectrum is in fact continuous and bounded by the sinc function as shown.

38. Fourier Transform

39. Fourier Transform

40. Fourier Transform
Fourier transform pair

41. Fourier Transform Example 1: rectangular pulse
A smaller  produces a wider main lobe (broad bandwidth)

42. Fourier Transform
Example 2: exponential function

43. Fourier Transform
Example 2: exponential function

44. Properties of Fourier Transform
Linearity
Time shifting; shifting in time changes only the phase

45. Properties of Fourier Transform
Time scaling
expanding in time (|a|<1) leads to slow variation
(deemphasizing high frequency components)
a fast replay (|a|>1) of man’s voice may be heard like girl’s voice

46. Properties of Fourier Transform
Time scaling

47. Properties of Fourier Transform
Frequency shifting and modulation
Example:

48. Properties of Fourier Transform
Frequency shifting and modulation

49. Properties of Fourier Transform
Duality
Example:

50. Properties of Fourier Transform
Duality

51. Properties of Fourier Transform
Differentiation
Example:
Integration

52. Properties of Fourier Transform
Example : triangular pulse

53. Properties of Fourier Transform
Parseval’s Theorem
Energy Spectral Density (ESD) :
the frequency distribution of total energy

54. Properties of Fourier Transform
Convolution (in time)

55. Properties of Fourier Transform
Multiplication (in time) : dual of convolution property

56. Application of Fourier Transform
Communication system : modulation and demodulation
Modulation

57. Application of Fourier Transform
Communication system : modulation and demodulation
Demodulation

58. Application of Fourier Transform
Multiplexing (frequency division multiplexing)

59. Useful Fourier Transform Pairs
Unit impulse
Constant
Unit step function
Exponential

60. Useful Fourier Transform Pairs
Complex exponential
Sinusoidal functions

61. Useful Fourier Transform Pairs
Rectangular pulse
Triangular pulse
Impulse train

62. Fourier Transform of Periodic Signals
Impulse train
i) Fourier series
...

63. Energy Spectral Density (ESD)
Energy transmission through LTI system

64. Power Spectral Density (PSD)
Power Spectral Density (PSD) function
Average power
Power Spectral Density Sx(f)

65. Power Spectral Density (PSD)
Power transmission through LTI system

66. Filter
Ideal Lowpass Filter (LPF)

67. Filter
Ideal Highpass Filter (HPF) and Bandpass Filter (BPF)