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CS412 Introduction to Computer Networking & Telecommunication. Theoretical Basis of Data Communication. Topics. Analog/Digital Signals Time and Frequency Domains Bandwidth and Channel Capacity Data Communication Measurements. Signals.

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cs412 introduction to computer networking telecommunication

CS412 Introduction to Computer Networking & Telecommunication

Theoretical Basis of

Data Communication

Chi-Cheng Lin, Winona State University

topics
Topics
  • Analog/Digital Signals
  • Time and Frequency Domains
  • Bandwidth and Channel Capacity
  • Data Communication Measurements
signals
Signals
  • Information must be transformed into electromagnetic signals to be transmitted
  • Signal forms
    • Analog or digital
    • Periodic or aperiodic
analog digital signals
Analog/Digital Signals
  • Analog signal
    • Continuous waveform
    • Can have a infinite number of values in a range
  • Digital signal
    • Discrete
    • Can have only a limited number of values
      • E.g., 0 or 1
periodic aperiodic signals
Periodic/Aperiodic Signals
  • Periodical signal
    • Contains continuously repeated pattern
    • Period (T): amount of time needed for the pattern to complete
  • Aperiodical signal
    • Contains no repetitive signals
analog signals
Analog Signals
  • Simple analog signal
    • Sine wave
    • 3 characteristics

1. Peak amplitude (A)

2. Frequency (f)

3. Phase ()

  • Composite analog signal
    • Composed of multiple sine waves
slide9
Figure 3.3Amplitude

s(t): instantaneous amplitude

t

characteristics of analog signal
Characteristics of Analog Signal
  • Peak amplitude: highest intensity
  • Frequency (f)
    • Number of cycles/rate of change per second
    • Measured in Hertz (Hz), KHz, MHz, GHz, …
    • Period (T): amount of time it takes to complete one cycle
    • f = 1/T
  • Phase: position of the waveform relative to time 0
characteristics of analog signal1
Characteristics of Analog Signal
  • Changes in the three characteristics provides the basis for telecommunication
    • Used by modems (later …)
time vs frequency domain
Time Vs. Frequency Domain
  • The sine waves shown previously are plotted in its time domain.
  • An analog signal is best represented in the frequency domain.
composite signals
Composite Signals
  • A composite signal can be decomposed into component sine waves - harmonics
  • The decomposition is performed by FourierAnalysis
slide21
Figure 4-13

Signal with DC Component

The McGraw-Hill Companies, Inc., 1998

WCB/McGraw-Hill

frequency spectrum and bandwidth
Frequency Spectrum and Bandwidth
  • Frequency spectrum
    • Collection of all component frequencies it contains
  • Bandwidth
    • Width of frequency spectrum
slide26
Example 3

If a periodic signal is decomposed into five sine waves with frequencies of 100, 300, 500, 700, and 900 Hz, what is the bandwidth? Draw the spectrum, assuming all components have a maximum amplitude of 10 V.

Solution

B = fh-fl = 900 - 100 = 800 Hz

The spectrum has only five spikes, at 100, 300, 500, 700, and 900 (see Figure 13.4 )

slide28
Example 4

A signal has a bandwidth of 20 Hz. The highest frequency is 60 Hz. What is the lowest frequency? Draw the spectrum if the signal contains all integral frequencies of the same amplitude.

Solution

B = fh- fl

20 = 60 - fl

fl = 60 - 20 = 40 Hz

slide30
Example 5

A signal has a spectrum with frequencies between 1000 and 2000 Hz (bandwidth of 1000 Hz). A medium can pass frequencies from 3000 to 4000 Hz (a bandwidth of 1000 Hz). Can this signal faithfully pass through this medium?

Solution

The answer is definitely no. Although the signal can have the same bandwidth (1000 Hz), the range does not overlap. The medium can only pass the frequencies between 3000 and 4000 Hz; the signal is totally lost.

digital signals
Digital Signals
  • 0s and 1s
  • Bit interval and bit rate
    • Bit interval: time required to send 1 bit
    • Bit rate: #bit intervals in one second
slide32
Example 6

A digital signal has a bit rate of 2000 bps. What is the duration of each bit (bit interval)

Solution

The bit interval is the inverse of the bit rate.

Bit interval = 1/ 2000 s = 0.000500 s = 0.000500 x 106ms = 500 ms

digital signal decomposition
Digital Signal - Decomposition
  • A digital signal can be decomposed into an infinite number of simple sine waves (harmonics), each with a different amplitude, frequency, and phase

A digital signal is a composite signal with an infinite bandwidth.

  • Significant spectrum
    • Components required to reconstruct the digital signal
slide34
Figure 4-20

Harmonics of a Digital Signal

The McGraw-Hill Companies, Inc., 1998

WCB/McGraw-Hill

bandwidth limited signals
Bandwidth-Limited Signals
  • (a) A binary signal and its root-mean-square Fourier amplitudes.
bandwidth limited signals 2
Bandwidth-Limited Signals (2)
  • (b) – (e) Successive approximations to the original signal.
slide37
Figure 4-21

Exact and Significant Spectrums

The McGraw-Hill Companies, Inc., 1998

WCB/McGraw-Hill

channel capacity
Channel Capacity
  • Channel capacity
    • Max. bit rate a transmission medium can transfer
  • Nyquist theorem
    • C = 2H log2V

where C: channel capacity (bit per second)

H: bandwidth (Hz)

V: signal levels (2 for binary)

    • C is proportional to H

 Significant bandwidth puts a limit on channel capacity

slide39
Figure 3.18Digital versus analog

To transmit 6bps, we need a bandwidth = 3 - 0 = 3Hz

channel capacity1
Channel Capacity
  • Nyquist theorem is for noiseless (error-free) channels.
  • Shannon Capacity
    • C = H log2(1 + S/N)

where C: (noisy) channel capacity (bps)

H: bandwidth (Hz)

S/N: signal-to-noise ratio

dB = 10 log10S/N

  • In practice, we have to apply both for determining the channel capacity.
slide41
Example 7

Consider a noiseless channel with a bandwidth of 3000 Hz transmitting a signal with two signal levels. The maximum bit rate can be calculated as

BitRate = 2  3000  log2 2 = 6000 bps

Example 8

Consider the same noiseless channel, transmitting a signal with four signal levels (for each level, we send two bits). The maximum bit rate can be calculated as:

Bit Rate = 2 x 3000 x log2 4 = 12,000 bps

slide42
Example 9

Consider an extremely noisy channel in which the value of the signal-to-noise ratio is almost zero. In other words, the noise is so strong that the signal is faint. For this channel the capacity is calculated as

C = B log2 (1 + S/N) = B log2 (1 + 0)= B log2 (1) = B  0 = 0

slide43
Example 10

We can calculate the theoretical highest bit rate of a regular telephone line. A telephone line normally has a bandwidth of 3000 Hz (300 Hz to 3300 Hz). The signal-to-noise ratio is usually 35dB, i.e., 3162. For this channel the capacity is calculated as

C = B log2 (1 + S/N) = 3000 log2 (1 + 3162) = 3000 log2 (3163)

C = 3000  11.62 = 34,860 bps

slide44
Example 11

We have a channel with a 1 MHz bandwidth. The S/N for this channel is 63; what is the appropriate bit rate and signal level?

Solution

First, we use the Shannon formula to find our upper limit.

C = B log2 (1 + S/N) = 106 log2 (1 + 63) = 106 log2 (64) = 6 Mbps

Then we use the Nyquist formula to find the

number of signal levels.

4 Mbps = 2  1 MHz  log2L L = 4

data communication measurements
Data Communication Measurements
  • Throughput
    • How fast data can pass through an entity
  • Propagation speed
    • Depends on medium and signal frequency
  • Propagation time (propagation delay)
    • Time required for one bit to travel from one point to another
  • Wavelength
    • Propagation speed = wavelength X frequency
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